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optimal solution</title> <meta name="description" content="Search results for: pareto optimal solution"> <meta name="keywords" content="pareto optimal solution"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="pareto optimal solution"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 8326</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: pareto optimal solution</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8326</span> Multi-Objective Random Drift Particle Swarm Optimization Algorithm Based on RDPSO and Crowding Distance Sorting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yiqiong%20Yuan">Yiqiong Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun%20Sun"> Jun Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Dongmei%20Zhou"> Dongmei Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianan%20Sun"> Jianan Sun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we presented a Multi-Objective Random Drift Particle Swarm Optimization algorithm (MORDPSO-CD) based on RDPSO and crowding distance sorting to improve the convergence and distribution with less computation cost. MORDPSO-CD makes the most of RDPSO to approach the true Pareto optimal solutions fast. We adopt the crowding distance sorting technique to update and maintain the archived optimal solutions. Introducing the crowding distance technique into MORDPSO can make the leader particles find the true Pareto solution ultimately. The simulation results reveal that the proposed algorithm has better convergence and distribution <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title="multi-objective optimization">multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20drift%20particle%20swarm%20optimization" title=" random drift particle swarm optimization"> random drift particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=crowding%20distance%20sorting" title=" crowding distance sorting"> crowding distance sorting</a>, <a href="https://publications.waset.org/abstracts/search?q=pareto%20optimal%20solution" title=" pareto optimal solution"> pareto optimal solution</a> </p> <a href="https://publications.waset.org/abstracts/44631/multi-objective-random-drift-particle-swarm-optimization-algorithm-based-on-rdpso-and-crowding-distance-sorting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44631.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">255</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8325</span> Multi-Objective Optimal Design of a Cascade Control System for a Class of Underactuated Mechanical Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuekun%20Chen">Yuekun Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Yousef%20Sardahi"> Yousef Sardahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Salam%20Hajjar"> Salam Hajjar</a>, <a href="https://publications.waset.org/abstracts/search?q=Christopher%20Greer"> Christopher Greer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a multi-objective optimal design of a cascade control system for an underactuated mechanical system. Cascade control structures usually include two control algorithms (inner and outer). To design such a control system properly, the following conflicting objectives should be considered at the same time: 1) the inner closed-loop control must be faster than the outer one, 2) the inner loop should fast reject any disturbance and prevent it from propagating to the outer loop, 3) the controlled system should be insensitive to measurement noise, and 4) the controlled system should be driven by optimal energy. Such a control problem can be formulated as a multi-objective optimization problem such that the optimal trade-offs among these design goals are found. To authors best knowledge, such a problem has not been studied in multi-objective settings so far. In this work, an underactuated mechanical system consisting of a rotary servo motor and a ball and beam is used for the computer simulations, the setup parameters of the inner and outer control systems are tuned by NSGA-II (Non-dominated Sorting Genetic Algorithm), and the dominancy concept is used to find the optimal design points. The solution of this problem is not a single optimal cascade control, but rather a set of optimal cascade controllers (called Pareto set) which represent the optimal trade-offs among the selected design criteria. The function evaluation of the Pareto set is called the Pareto front. The solution set is introduced to the decision-maker who can choose any point to implement. The simulation results in terms of Pareto front and time responses to external signals show the competing nature among the design objectives. The presented study may become the basis for multi-objective optimal design of multi-loop control systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cascade%20control" title="cascade control">cascade control</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-Loop%20control%20systems" title=" multi-Loop control systems"> multi-Loop control systems</a>, <a href="https://publications.waset.org/abstracts/search?q=multiobjective%20optimization" title=" multiobjective optimization"> multiobjective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a> </p> <a href="https://publications.waset.org/abstracts/113986/multi-objective-optimal-design-of-a-cascade-control-system-for-a-class-of-underactuated-mechanical-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113986.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">153</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8324</span> Optimal Retrofit Design of Reinforced Concrete Frame with Infill Wall Using Fiber Reinforced Plastic Materials</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sang%20Wook%20Park">Sang Wook Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Se%20Woon%20Choi"> Se Woon Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yousok%20Kim"> Yousok Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Byung%20Kwan%20Oh"> Byung Kwan Oh</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyo%20Seon%20Park"> Hyo Seon Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Various retrofit techniques for reinforced concrete frame with infill wall have been steadily developed. Among those techniques, strengthening methodology based on diagonal FRP strips (FRP bracings) has numerous advantages such as feasibility of implementing without interrupting the building under operation, reduction of cost and time, and easy application. Considering the safety of structure and retrofit cost, the most appropriate retrofit solution is needed. Thus, the objective of this study is to suggest pareto-optimal solution for existing building using FRP bracings. To find pareto-optimal solution analysis, NSGA-II is applied. Moreover, the seismic performance of retrofit building is evaluated. The example building is 5-storey, 3-bay RC frames with infill wall. Nonlinear static pushover analyses are performed with FEMA 356. The criterion of performance evaluation is inter-story drift ratio at the performance level IO, LS, CP. Optimal retrofit solutions is obtained for 32 individuals and 200 generations. Through the proposed optimal solutions, we confirm the improvement of seismic performance of the example building. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=retrofit" title="retrofit">retrofit</a>, <a href="https://publications.waset.org/abstracts/search?q=FRP%20bracings" title=" FRP bracings"> FRP bracings</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforced%20concrete%20frame%20with%20infill%20wall" title=" reinforced concrete frame with infill wall"> reinforced concrete frame with infill wall</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20performance%20evaluation" title=" seismic performance evaluation"> seismic performance evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=NSGA-II" title=" NSGA-II"> NSGA-II</a> </p> <a href="https://publications.waset.org/abstracts/42927/optimal-retrofit-design-of-reinforced-concrete-frame-with-infill-wall-using-fiber-reinforced-plastic-materials" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42927.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">437</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8323</span> Pareto Optimal Material Allocation Mechanism</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peter%20Egri">Peter Egri</a>, <a href="https://publications.waset.org/abstracts/search?q=Tamas%20Kis"> Tamas Kis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Scheduling problems have been studied by the algorithmic mechanism design research from the beginning. This paper is focusing on a practically important, but theoretically rather neglected field: the project scheduling problem where the jobs connected by precedence constraints compete for various nonrenewable resources, such as materials. Although the centralized problem can be solved in polynomial-time by applying the algorithm of Carlier and Rinnooy Kan from the Eighties, obtaining materials in a decentralized environment is usually far from optimal. It can be observed in practical production scheduling situations that project managers tend to cache the required materials as soon as possible in order to avoid later delays due to material shortages. This greedy practice usually leads both to excess stocks for some projects and materials, and simultaneously, to shortages for others. The aim of this study is to develop a model for the material allocation problem of a production plant, where a central decision maker—the inventory—should assign the resources arriving at different points in time to the jobs. Since the actual due dates are not known by the inventory, the mechanism design approach is applied with the projects as the self-interested agents. The goal of the mechanism is to elicit the required information and allocate the available materials such that it minimizes the maximal tardiness among the projects. It is assumed that except the due dates, the inventory is familiar with every other parameters of the problem. A further requirement is that due to practical considerations monetary transfer is not allowed. Therefore a mechanism without money is sought which excludes some widely applied solutions such as the Vickrey–Clarke–Groves scheme. In this work, a type of Serial Dictatorship Mechanism (SDM) is presented for the studied problem, including a polynomial-time algorithm for computing the material allocation. The resulted mechanism is both truthful and Pareto optimal. Thus the randomization over the possible priority orderings of the projects results in a universally truthful and Pareto optimal randomized mechanism. However, it is shown that in contrast to problems like the many-to-many matching market, not every Pareto optimal solution can be generated with an SDM. In addition, no performance guarantee can be given compared to the optimal solution, therefore this approximation characteristic is investigated with experimental study. All in all, the current work studies a practically relevant scheduling problem and presents a novel truthful material allocation mechanism which eliminates the potential benefit of the greedy behavior that negatively influences the outcome. The resulted allocation is also shown to be Pareto optimal, which is the most widely used criteria describing a necessary condition for a reasonable solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=material%20allocation" title="material allocation">material allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=mechanism%20without%20money" title=" mechanism without money"> mechanism without money</a>, <a href="https://publications.waset.org/abstracts/search?q=polynomial-time%20mechanism" title=" polynomial-time mechanism"> polynomial-time mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20scheduling" title=" project scheduling"> project scheduling</a> </p> <a href="https://publications.waset.org/abstracts/68829/pareto-optimal-material-allocation-mechanism" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68829.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8322</span> Performance Evaluation of Karanja Oil Based Biodiesel Engine Using Modified Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Bhushan">G. Bhushan</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Dhingra"> S. Dhingra</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20K.%20Dubey"> K. K. Dubey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the evaluation of performance (BSFC and BTE), combustion (P<sub>max</sub>) and emission (CO, NO<sub>x</sub>, HC and smoke opacity) parameters of karanja biodiesel in a single cylinder, four stroke, direct injection diesel engine by considering significant engine input parameters (blending ratio, compression ratio and load torque). Multi-objective optimization of performance, combustion and emission parameters is also carried out in a karanja biodiesel engine using hybrid RSM-NSGA-II technique. The pareto optimum solutions are predicted by running the hybrid RSM-NSGA-II technique. Each pareto optimal solution is having its own importance. Confirmation tests are also conducted at randomly selected few pareto solutions to check the authenticity of the results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=rsm" title=" rsm"> rsm</a>, <a href="https://publications.waset.org/abstracts/search?q=biodiesel" title=" biodiesel"> biodiesel</a>, <a href="https://publications.waset.org/abstracts/search?q=karanja" title=" karanja"> karanja</a> </p> <a href="https://publications.waset.org/abstracts/51865/performance-evaluation-of-karanja-oil-based-biodiesel-engine-using-modified-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51865.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">305</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8321</span> On Multiobjective Optimization to Improve the Scalability of Fog Application Deployments Using Fogtorch</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suleiman%20Aliyu">Suleiman Aliyu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Integrating IoT applications with Fog systems presents challenges in optimization due to diverse environments and conflicting objectives. This study explores achieving Pareto optimal deployments for Fog-based IoT systems to address growing QoS demands. We introduce Pareto optimality to balance competing performance metrics. Using the FogTorch optimization framework, we propose a hybrid approach (Backtracking search with branch and bound) for scalable IoT deployments. Our research highlights the advantages of Pareto optimality over single-objective methods and emphasizes the role of FogTorch in this context. Initial results show improvements in IoT deployment cost in Fog systems, promoting resource-efficient strategies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pareto%20optimality" title="pareto optimality">pareto optimality</a>, <a href="https://publications.waset.org/abstracts/search?q=fog%20application%20deployment" title=" fog application deployment"> fog application deployment</a>, <a href="https://publications.waset.org/abstracts/search?q=resource%20allocation" title=" resource allocation"> resource allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=internet%20of%20things" title=" internet of things"> internet of things</a> </p> <a href="https://publications.waset.org/abstracts/175224/on-multiobjective-optimization-to-improve-the-scalability-of-fog-application-deployments-using-fogtorch" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175224.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">88</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8320</span> Pareto System of Optimal Placement and Sizing of Distributed Generation in Radial Distribution Networks Using Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sani%20M.%20Lawal">Sani M. Lawal</a>, <a href="https://publications.waset.org/abstracts/search?q=Idris%20Musa"> Idris Musa</a>, <a href="https://publications.waset.org/abstracts/search?q=Aliyu%20D.%20Usman"> Aliyu D. Usman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Pareto approach of optimal solutions in a search space that evolved in multi-objective optimization problems is adopted in this paper, which stands for a set of solutions in the search space. This paper aims at presenting an optimal placement of Distributed Generation (DG) in radial distribution networks with an optimal size for minimization of power loss and voltage deviation as well as maximizing voltage profile of the networks. And these problems are formulated using particle swarm optimization (PSO) as a constraint nonlinear optimization problem with both locations and sizes of DG being continuous. The objective functions adopted are the total active power loss function and voltage deviation function. The multiple nature of the problem, made it necessary to form a multi-objective function in search of the solution that consists of both the DG location and size. The proposed PSO algorithm is used to determine optimal placement and size of DG in a distribution network. The output indicates that PSO algorithm technique shows an edge over other types of search methods due to its effectiveness and computational efficiency. The proposed method is tested on the standard IEEE 34-bus and validated with 33-bus test systems distribution networks. Results indicate that the sizing and location of DG are system dependent and should be optimally selected before installing the distributed generators in the system and also an improvement in the voltage profile and power loss reduction have been achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20generation" title="distributed generation">distributed generation</a>, <a href="https://publications.waset.org/abstracts/search?q=pareto" title=" pareto"> pareto</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20loss" title=" power loss"> power loss</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20deviation" title=" voltage deviation"> voltage deviation</a> </p> <a href="https://publications.waset.org/abstracts/54635/pareto-system-of-optimal-placement-and-sizing-of-distributed-generation-in-radial-distribution-networks-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54635.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">364</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8319</span> Optimal Sliding Mode Controller for Knee Flexion during Walking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20Sitler">Gabriel Sitler</a>, <a href="https://publications.waset.org/abstracts/search?q=Yousef%20Sardahi"> Yousef Sardahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Asad%20Salem"> Asad Salem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an optimal and robust sliding mode controller (SMC) to regulate the position of the knee joint angle for patients suffering from knee injuries. The controller imitates the role of active orthoses that produce the joint torques required to overcome gravity and loading forces and regain natural human movements. To this end, a mathematical model of the shank, the lower part of the leg, is derived first and then used for the control system design and computer simulations. The design of the controller is carried out in optimal and multi-objective settings. Four objectives are considered: minimization of the control effort and tracking error; and maximization of the control signal smoothness and closed-loop system’s speed of response. Optimal solutions in terms of the Pareto set and its image, the Pareto front, are obtained. The results show that there are trade-offs among the design objectives and many optimal solutions from which the decision-maker can choose to implement. Also, computer simulations conducted at different points from the Pareto set and assuming knee squat movement demonstrate competing relationships among the design goals. In addition, the proposed control algorithm shows robustness in tracking a standard gait signal when accounting for uncertainty in the shank’s parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sliding%20mode%20control" title=" sliding mode control"> sliding mode control</a>, <a href="https://publications.waset.org/abstracts/search?q=wearable%20knee%20exoskeletons" title=" wearable knee exoskeletons"> wearable knee exoskeletons</a> </p> <a href="https://publications.waset.org/abstracts/164514/optimal-sliding-mode-controller-for-knee-flexion-during-walking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164514.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8318</span> A Flexible Pareto Distribution Using α-Power Transformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shumaila%20Ehtisham">Shumaila Ehtisham</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Statistical Distribution Theory, considering an additional parameter to classical distributions is a usual practice. In this study, a new distribution referred to as α-Power Pareto distribution is introduced by including an extra parameter. Several properties of the proposed distribution including explicit expressions for the moment generating function, mode, quantiles, entropies and order statistics are obtained. Unknown parameters have been estimated by using maximum likelihood estimation technique. Two real datasets have been considered to examine the usefulness of the proposed distribution. It has been observed that α-Power Pareto distribution outperforms while compared to different variants of Pareto distribution on the basis of model selection criteria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=%CE%B1-power%20transformation" title="α-power transformation">α-power transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=moment%20generating%20function" title=" moment generating function"> moment generating function</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20distribution" title=" Pareto distribution"> Pareto distribution</a> </p> <a href="https://publications.waset.org/abstracts/89859/a-flexible-pareto-distribution-using-a-power-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89859.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8317</span> The Application of Pareto Local Search to the Single-Objective Quadratic Assignment Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Alsheddy">Abdullah Alsheddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the employment of Pareto optimality as a strategy to help (single-objective) local search escaping local optima. Instead of local search, Pareto local search is applied to solve the quadratic assignment problem which is multi-objectivized by adding a helper objective. The additional objective is defined as a function of the primary one with augmented penalties that are dynamically updated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pareto%20optimization" title="Pareto optimization">Pareto optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objectivization" title=" multi-objectivization"> multi-objectivization</a>, <a href="https://publications.waset.org/abstracts/search?q=quadratic%20assignment%20problem" title=" quadratic assignment problem"> quadratic assignment problem</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20search" title=" local search"> local search</a> </p> <a href="https://publications.waset.org/abstracts/9877/the-application-of-pareto-local-search-to-the-single-objective-quadratic-assignment-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9877.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">466</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8316</span> Optimal Design of Composite Patch for a Cracked Pipe by Utilizing Genetic Algorithm and Finite Element Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Fakoor">Mahdi Fakoor</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Mohammad%20Navid%20Ghoreishi"> Seyed Mohammad Navid Ghoreishi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Composite patching is a common way for reinforcing the cracked pipes and cylinders. The effects of composite patch reinforcement on fracture parameters of a cracked pipe depend on a variety of parameters such as number of layers, angle, thickness, and material of each layer. Therefore, stacking sequence optimization of composite patch becomes crucial for the applications of cracked pipes. In this study, in order to obtain the optimal stacking sequence for a composite patch that has minimum weight and maximum resistance in propagation of cracks, a coupled Multi-Objective Genetic Algorithm (MOGA) and Finite Element Method (FEM) process is proposed. This optimization process has done for longitudinal and transverse semi-elliptical cracks and optimal stacking sequences and Pareto’s front for each kind of cracks are presented. The proposed algorithm is validated against collected results from the existing literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi%20objective%20optimization" title="multi objective optimization">multi objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=pareto%20front" title=" pareto front"> pareto front</a>, <a href="https://publications.waset.org/abstracts/search?q=composite%20patch" title=" composite patch"> composite patch</a>, <a href="https://publications.waset.org/abstracts/search?q=cracked%20pipe" title=" cracked pipe"> cracked pipe</a> </p> <a href="https://publications.waset.org/abstracts/67559/optimal-design-of-composite-patch-for-a-cracked-pipe-by-utilizing-genetic-algorithm-and-finite-element-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67559.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">312</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8315</span> Multi-Objective Optimization in Carbon Abatement Technology Cycles (CAT) and Related Areas: Survey, Developments and Prospects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hameed%20Rukayat%20Opeyemi">Hameed Rukayat Opeyemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Pericles%20Pilidis"> Pericles Pilidis</a>, <a href="https://publications.waset.org/abstracts/search?q=Pagone%20Emanuele"> Pagone Emanuele</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An infinitesimal increase in performance can have immense reduction in operating and capital expenses in a power generation system. Therefore, constant studies are being carried out to improve both conventional and novel power cycles. Globally, power producers are constantly researching on ways to minimize emission and to collectively downsize the total cost rate of power plants. A substantial spurt of developmental technologies of low carbon cycles have been suggested and studied, however they all have their limitations and financial implication. In the area of carbon abatement in power plants, three major objectives conflict: The cost rate of the plant, Power output and Environmental impact. Since, an increase in one of this parameter directly affects the other. This poses a multi-objective problem. It is paramount to be able to discern the point where improving one objective affects the other. Hence, the need for a Pareto-based optimization algorithm. Pareto-based optimization algorithm helps to find those points where improving one objective influences another objective negatively and stops there. The application of Pareto-based optimization algorithm helps the user/operator/designer make an informed decision. This paper sheds more light on areas that multi-objective optimization has been applied in carbon abatement technologies in the last five years, developments and prospects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gas%20turbine" title="gas turbine">gas turbine</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20carbon%20technology" title=" low carbon technology"> low carbon technology</a>, <a href="https://publications.waset.org/abstracts/search?q=pareto%20optimal" title=" pareto optimal"> pareto optimal</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/27738/multi-objective-optimization-in-carbon-abatement-technology-cycles-cat-and-related-areas-survey-developments-and-prospects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27738.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">791</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8314</span> The Generalized Pareto Distribution as a Model for Sequential Order Statistics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdy%20%E2%80%8EEsmailian">Mahdy ‎Esmailian</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20%E2%80%8EDoostparast"> Mahdi ‎Doostparast</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20%E2%80%8EParsian"> Ahmad ‎Parsian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> ‎In this article‎, ‎sequential order statistics (SOS) censoring type II samples coming from the generalized Pareto distribution are considered‎. ‎Maximum likelihood (ML) estimators of the unknown parameters are derived on the basis of the available multiple SOS data‎. ‎Necessary conditions for existence and uniqueness of the derived ML estimates are given‎. Due to complexity in the proposed likelihood function‎, ‎a useful re-parametrization is suggested‎. ‎For illustrative purposes‎, ‎a Monte Carlo simulation study is conducted and an illustrative example is analysed‎. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20estimation%E2%80%8E" title="bayesian estimation‎">bayesian estimation‎</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20pareto%20distribution%E2%80%8E" title=" generalized pareto distribution‎"> generalized pareto distribution‎</a>, <a href="https://publications.waset.org/abstracts/search?q=%E2%80%8Emaximum%20likelihood%20%20estimation%E2%80%8E" title=" ‎maximum likelihood estimation‎"> ‎maximum likelihood estimation‎</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20order%20statistics" title=" sequential order statistics"> sequential order statistics</a> </p> <a href="https://publications.waset.org/abstracts/26988/the-generalized-pareto-distribution-as-a-model-for-sequential-order-statistics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26988.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">509</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8313</span> A Novel Guided Search Based Multi-Objective Evolutionary Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Baviskar">A. Baviskar</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Sandeep"> C. Sandeep</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Shankar"> K. Shankar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Solving Multi-objective Optimization Problems requires faster convergence and better spread. Though existing Evolutionary Algorithms (EA's) are able to achieve this, the computation effort can further be reduced by hybridizing them with innovative strategies. This study is focuses on converging to the pareto front faster while adapting the advantages of Strength Pareto Evolutionary Algorithm-II (SPEA-II) for a better spread. Two different approaches based on optimizing the objective functions independently are implemented. In the first method, the decision variables corresponding to the optima of individual objective functions are strategically used to guide the search towards the pareto front. In the second method, boundary points of the pareto front are calculated and their decision variables are seeded to the initial population. Both the methods are applied to different constrained and unconstrained multi-objective test functions. It is observed that proposed guided search based algorithm gives better convergence and diversity than several well-known existing algorithms (such as NSGA-II and SPEA-II) in considerably less number of iterations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boundary%20points" title="boundary points">boundary points</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms%20%28EA%27s%29" title=" evolutionary algorithms (EA's)"> evolutionary algorithms (EA's)</a>, <a href="https://publications.waset.org/abstracts/search?q=guided%20search" title=" guided search"> guided search</a>, <a href="https://publications.waset.org/abstracts/search?q=strength%20pareto%20evolutionary%20algorithm-II%20%28SPEA-II%29" title=" strength pareto evolutionary algorithm-II (SPEA-II)"> strength pareto evolutionary algorithm-II (SPEA-II)</a> </p> <a href="https://publications.waset.org/abstracts/40983/a-novel-guided-search-based-multi-objective-evolutionary-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40983.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">277</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8312</span> Two-Stage Approach for Solving the Multi-Objective Optimization Problem on Combinatorial Configurations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liudmyla%20Koliechkina">Liudmyla Koliechkina</a>, <a href="https://publications.waset.org/abstracts/search?q=Olena%20Dvirna"> Olena Dvirna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The statement of the multi-objective optimization problem on combinatorial configurations is formulated, and the approach to its solution is proposed. The problem is of interest as a combinatorial optimization one with many criteria, which is a model of many applied tasks. The approach to solving the multi-objective optimization problem on combinatorial configurations consists of two stages; the first is the reduction of the multi-objective problem to the single criterion based on existing multi-objective optimization methods, the second stage solves the directly replaced single criterion combinatorial optimization problem by the horizontal combinatorial method. This approach provides the optimal solution to the multi-objective optimization problem on combinatorial configurations, taking into account additional restrictions for a finite number of steps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20set" title="discrete set">discrete set</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20combinatorial%20optimization" title=" linear combinatorial optimization"> linear combinatorial optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions" title=" Pareto solutions"> Pareto solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20permutation%20set" title=" partial permutation set"> partial permutation set</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20graph" title=" structural graph"> structural graph</a> </p> <a href="https://publications.waset.org/abstracts/133824/two-stage-approach-for-solving-the-multi-objective-optimization-problem-on-combinatorial-configurations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133824.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8311</span> An Extended Inverse Pareto Distribution, with Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdel%20Hadi%20Ebraheim">Abdel Hadi Ebraheim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a new extension of the Inverse Pareto distribution in the framework of Marshal-Olkin (1997) family of distributions. This model is capable of modeling various shapes of aging and failure data. The statistical properties of the new model are discussed. Several methods are used to estimate the parameters involved. Explicit expressions are derived for different types of moments of value in reliability analysis are obtained. Besides, the order statistics of samples from the new proposed model have been studied. Finally, the usefulness of the new model for modeling reliability data is illustrated using two real data sets with simulation study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pareto%20distribution" title="pareto distribution">pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=marshal-Olkin" title=" marshal-Olkin"> marshal-Olkin</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability" title=" reliability"> reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=hazard%20functions" title=" hazard functions"> hazard functions</a>, <a href="https://publications.waset.org/abstracts/search?q=moments" title=" moments"> moments</a>, <a href="https://publications.waset.org/abstracts/search?q=estimation" title=" estimation"> estimation</a> </p> <a href="https://publications.waset.org/abstracts/169013/an-extended-inverse-pareto-distribution-with-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169013.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8310</span> A Study on Computational Fluid Dynamics (CFD)-Based Design Optimization Techniques Using Multi-Objective Evolutionary Algorithms (MOEA)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20E.%20Hodaib">Ahmed E. Hodaib</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20A.%20Hashem"> Mohamed A. Hashem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In engineering applications, a design has to be as fully perfect as possible in some defined case. The designer has to overcome many challenges in order to reach the optimal solution to a specific problem. This process is called optimization. Generally, there is always a function called “objective function” that is required to be maximized or minimized by choosing input parameters called “degrees of freedom” within an allowed domain called “search space” and computing the values of the objective function for these input values. It becomes more complex when we have more than one objective for our design. As an example for Multi-Objective Optimization Problem (MOP): A structural design that aims to minimize weight and maximize strength. In such case, the Pareto Optimal Frontier (POF) is used, which is a curve plotting two objective functions for the best cases. At this point, a designer should make a decision to choose the point on the curve. Engineers use algorithms or iterative methods for optimization. In this paper, we will discuss the Evolutionary Algorithms (EA) which are widely used with Multi-objective Optimization Problems due to their robustness, simplicity, suitability to be coupled and to be parallelized. Evolutionary algorithms are developed to guarantee the convergence to an optimal solution. An EA uses mechanisms inspired by Darwinian evolution principles. Technically, they belong to the family of trial and error problem solvers and can be considered global optimization methods with a stochastic optimization character. The optimization is initialized by picking random solutions from the search space and then the solution progresses towards the optimal point by using operators such as Selection, Combination, Cross-over and/or Mutation. These operators are applied to the old solutions “parents” so that new sets of design variables called “children” appear. The process is repeated until the optimal solution to the problem is reached. Reliable and robust computational fluid dynamics solvers are nowadays commonly utilized in the design and analyses of various engineering systems, such as aircraft, turbo-machinery, and auto-motives. Coupling of Computational Fluid Dynamics “CFD” and Multi-Objective Evolutionary Algorithms “MOEA” has become substantial in aerospace engineering applications, such as in aerodynamic shape optimization and advanced turbo-machinery design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mathematical%20optimization" title="mathematical optimization">mathematical optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20evolutionary%20algorithms%20%22MOEA%22" title=" multi-objective evolutionary algorithms "MOEA""> multi-objective evolutionary algorithms "MOEA"</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20fluid%20dynamics%20%22CFD%22" title=" computational fluid dynamics "CFD""> computational fluid dynamics "CFD"</a>, <a href="https://publications.waset.org/abstracts/search?q=aerodynamic%20shape%20optimization" title=" aerodynamic shape optimization"> aerodynamic shape optimization</a> </p> <a href="https://publications.waset.org/abstracts/54515/a-study-on-computational-fluid-dynamics-cfd-based-design-optimization-techniques-using-multi-objective-evolutionary-algorithms-moea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54515.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8309</span> Modeling of Maximum Rainfall Using Poisson-Generalized Pareto Distribution in Kigali, Rwanda</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Iyamuremye">Emmanuel Iyamuremye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extreme rainfall events have caused significant damage to agriculture, ecology, and infrastructure, disruption of human activities, injury, and loss of life. They also have significant social, economic, and environmental consequences because they considerably damage urban as well as rural areas. Early detection of extreme maximum rainfall helps to implement strategies and measures, before they occur, hence mitigating the consequences. Extreme value theory has been used widely in modeling extreme rainfall and in various disciplines, such as financial markets, the insurance industry, failure cases. Climatic extremes have been analyzed by using either generalized extreme value (GEV) or generalized Pareto (GP) distributions, which provides evidence of the importance of modeling extreme rainfall from different regions of the world. In this paper, we focused on Peak Over Thresholds approach, where the Poisson-generalized Pareto distribution is considered as the proper distribution for the study of the exceedances. This research also considers the use of the generalized Pareto (GP) distribution with a Poisson model for arrivals to describe peaks over a threshold. The research used statistical techniques to fit models that used to predict extreme rainfall in Kigali. The results indicate that the proposed Poisson-GP distribution provides a better fit to maximum monthly rainfall data. Further, the Poisson-GP models are able to estimate various return levels. The research also found a slow increase in return levels for maximum monthly rainfall for higher return periods, and further, the intervals are increasingly wider as the return period is increasing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exceedances" title="exceedances">exceedances</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title=" extreme value theory"> extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20Pareto%20distribution" title=" generalized Pareto distribution"> generalized Pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20generalized%20Pareto%20distribution" title=" Poisson generalized Pareto distribution"> Poisson generalized Pareto distribution</a> </p> <a href="https://publications.waset.org/abstracts/127379/modeling-of-maximum-rainfall-using-poisson-generalized-pareto-distribution-in-kigali-rwanda" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127379.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">135</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8308</span> Multiobjective Optimization of a Pharmaceutical Formulation Using Regression Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20Satya%20Eswari">J. Satya Eswari</a>, <a href="https://publications.waset.org/abstracts/search?q=Ch.%20Venkateswarlu"> Ch. Venkateswarlu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The formulation of a commercial pharmaceutical product involves several composition factors and response characteristics. When the formulation requires to satisfy multiple response characteristics which are conflicting, an optimal solution requires the need for an efficient multiobjective optimization technique. In this work, a regression is combined with a non-dominated sorting differential evolution (NSDE) involving Naïve & Slow and ε constraint techniques to derive different multiobjective optimization strategies, which are then evaluated by means of a trapidil pharmaceutical formulation. The analysis of the results show the effectiveness of the strategy that combines the regression model and NSDE with the integration of both Naïve & Slow and ε constraint techniques for Pareto optimization of trapidil formulation. With this strategy, the optimal formulation at pH=6.8 is obtained with the decision variables of micro crystalline cellulose, hydroxypropyl methylcellulose and compression pressure. The corresponding response characteristics of rate constant and release order are also noted down. The comparison of these results with the experimental data and with those of other multiple regression model based multiobjective evolutionary optimization strategies signify the better performance for optimal trapidil formulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pharmaceutical%20formulation" title="pharmaceutical formulation">pharmaceutical formulation</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20regression%20model" title=" multiple regression model"> multiple regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface%20method" title=" response surface method"> response surface method</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20network" title=" radial basis function network"> radial basis function network</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution" title=" differential evolution"> differential evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=multiobjective%20optimization" title=" multiobjective optimization"> multiobjective optimization</a> </p> <a href="https://publications.waset.org/abstracts/62859/multiobjective-optimization-of-a-pharmaceutical-formulation-using-regression-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62859.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">409</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8307</span> A Bi-Objective Stochastic Mathematical Model for Agricultural Supply Chain Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Mahdi%20Paydar">Mohammad Mahdi Paydar</a>, <a href="https://publications.waset.org/abstracts/search?q=Armin%20Cheraghalipour"> Armin Cheraghalipour</a>, <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Hajiaghaei-Keshteli"> Mostafa Hajiaghaei-Keshteli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, in advanced countries, agriculture as one of the most significant sectors of the economy, plays an important role in its political and economic independence. Due to farmers' lack of information about products' demand and lack of proper planning for harvest time, annually the considerable amount of products is corrupted. Besides, in this paper, we attempt to improve these unfavorable conditions via designing an effective supply chain network that tries to minimize total costs of agricultural products along with minimizing shortage in demand points. To validate the proposed model, a stochastic optimization approach by using a branch and bound solver of the LINGO software is utilized. Furthermore, to accumulate the data of parameters, a case study in Mazandaran province placed in the north of Iran has been applied. Finally, using ɛ-constraint approach, a Pareto front is obtained and one of its Pareto solutions as best solution is selected. Then, related results of this solution are explained. Finally, conclusions and suggestions for the future research are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=perishable%20products" title="perishable products">perishable products</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20optimization" title=" stochastic optimization"> stochastic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=agricultural%20supply%20chain" title=" agricultural supply chain"> agricultural supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=%C9%9B-constraint" title=" ɛ-constraint"> ɛ-constraint</a> </p> <a href="https://publications.waset.org/abstracts/82374/a-bi-objective-stochastic-mathematical-model-for-agricultural-supply-chain-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82374.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">365</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8306</span> An Improved Genetic Algorithm for Traveling Salesman Problem with Precedence Constraint</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20F.%20F.%20Ab%20Rashid">M. F. F. Ab Rashid</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20N.%20Mohd%20Rose"> A. N. Mohd Rose</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20M.%20Z.%20Nik%20Mohamed"> N. M. Z. Nik Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20S.%20Wan%20Harun"> W. S. Wan Harun</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Che%20Ghani"> S. A. Che Ghani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traveling salesman problem with precedence constraint (TSPPC) is one of the most complex problems in combinatorial optimization. The existing algorithms to solve TSPPC cost large computational time to find the optimal solution. The purpose of this paper is to present an efficient genetic algorithm that guarantees optimal solution with less number of generations and iterations time. Unlike the existing algorithm that generates priority factor as chromosome, the proposed algorithm directly generates sequence of solution as chromosome. As a result, the proposed algorithm is capable of generating optimal solution with smaller number of generations and iteration time compare to existing algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesman%20problem" title="traveling salesman problem">traveling salesman problem</a>, <a href="https://publications.waset.org/abstracts/search?q=sequencing" title=" sequencing"> sequencing</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=precedence%20constraint" title=" precedence constraint"> precedence constraint</a> </p> <a href="https://publications.waset.org/abstracts/15486/an-improved-genetic-algorithm-for-traveling-salesman-problem-with-precedence-constraint" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15486.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">560</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8305</span> A Hybrid Pareto-Based Swarm Optimization Algorithm for the Multi-Objective Flexible Job Shop Scheduling Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new hybrid particle swarm optimization algorithm is proposed for the multi-objective flexible job shop scheduling problem that is very important and hard combinatorial problem. The Pareto approach is used for solving the multi-objective problem. Several new local search heuristics are integrated into an algorithm based on the critical block concept to enhance the performance of the algorithm. The algorithm is compared with the recently published multi-objective algorithms based on benchmarks selected from the literature. Several metrics are used for quantifying performance and comparison of the achieved solutions. The algorithms are also compared based on the Weighting summation of objectives approach. The proposed algorithm can find the Pareto solutions more efficiently than the compared algorithms in less computational time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=swarm-based%20optimization" title="swarm-based optimization">swarm-based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20search" title=" local search"> local search</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20optimality" title=" Pareto optimality"> Pareto optimality</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20job%20shop%20scheduling" title=" flexible job shop scheduling"> flexible job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/72144/a-hybrid-pareto-based-swarm-optimization-algorithm-for-the-multi-objective-flexible-job-shop-scheduling-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72144.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8304</span> Optimal Management of Internal Capital of Company</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Sadallah">S. Sadallah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, dynamic programming is used to determine the optimal management of financial resources in company. Solution of the problem by consider into simpler substructures is constructed. The optimal management of internal capital of company are simulated. The tools applied in this development are based on graph theory. The software of given problems is built by using greedy algorithm. The obtained model and program maintenance enable us to define the optimal version of management of proper financial flows by using visual diagram on each level of investment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=management" title="management">management</a>, <a href="https://publications.waset.org/abstracts/search?q=software" title=" software"> software</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal" title=" optimal"> optimal</a>, <a href="https://publications.waset.org/abstracts/search?q=greedy%20algorithm" title=" greedy algorithm"> greedy algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=graph-diagram" title=" graph-diagram"> graph-diagram</a> </p> <a href="https://publications.waset.org/abstracts/47406/optimal-management-of-internal-capital-of-company" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47406.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">285</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8303</span> Optimum Stratiﬁcation of a Skewed Population</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20K.%20Rao">D. K. Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20G.%20M.%20Khan"> M. G. M. Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20G.%20Reddy"> K. G. Reddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The focus of this paper is to develop a technique of solving a combined problem of determining Optimum Strata Boundaries (OSB) and Optimum Sample Size (OSS) of each stratum, when the population understudy is skewed and the study variable has a Pareto frequency distribution. The problem of determining the OSB is formulated as a Mathematical Programming Problem (MPP) which is then solved by dynamic programming technique. A numerical example is presented to illustrate the computational details of the proposed method. The proposed technique is useful to obtain OSB and OSS for a Pareto type skewed population, which minimizes the variance of the estimate of population mean. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=strati%EF%AC%81ed%20sampling" title="stratiﬁed sampling">stratiﬁed sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=optimum%20strata%20boundaries" title=" optimum strata boundaries"> optimum strata boundaries</a>, <a href="https://publications.waset.org/abstracts/search?q=optimum%20sample%20size" title=" optimum sample size"> optimum sample size</a>, <a href="https://publications.waset.org/abstracts/search?q=pareto%20distribution" title=" pareto distribution"> pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20programming%20problem" title=" mathematical programming problem"> mathematical programming problem</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20programming%20technique" title=" dynamic programming technique"> dynamic programming technique</a> </p> <a href="https://publications.waset.org/abstracts/5894/optimum-stratification-of-a-skewed-population" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5894.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">453</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8302</span> Optimal Placement of the Unified Power Controller to Improve the Power System Restoration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Reza%20Esmaili">Mohammad Reza Esmaili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the most important parts of the restoration process of a power network is the synchronizing of its subsystems. In this situation, the biggest concern of the system operators will be the reduction of the standing phase angle (SPA) between the endpoints of the two islands. In this regard, the system operators perform various actions and maneuvers so that the synchronization operation of the subsystems is successfully carried out and the system finally reaches acceptable stability. The most common of these actions include load control, generation control and, in some cases, changing the network topology. Although these maneuvers are simple and common, due to the weak network and extreme load changes, the restoration will be associated with low speed. One of the best ways to control the SPA is to use FACTS devices. By applying a soft control signal, these tools can reduce the SPA between two subsystems with more speed and accuracy, and the synchronization process can be done in less time. Meanwhile, the unified power controller (UPFC), a series-parallel compensator device with the change of transmission line power and proper adjustment of the phase angle, will be the proposed option in order to realize the subject of this research. Therefore, with the optimal placement of UPFC in a power system, in addition to improving the normal conditions of the system, it is expected to be effective in reducing the SPA during power system restoration. Therefore, the presented paper provides an optimal structure to coordinate the three problems of improving the division of subsystems, reducing the SPA and optimal power flow with the aim of determining the optimal location of UPFC and optimal subsystems. The proposed objective functions in this paper include maximizing the quality of the subsystems, reducing the SPA at the endpoints of the subsystems, and reducing the losses of the power system. Since there will be a possibility of creating contradictions in the simultaneous optimization of the proposed objective functions, the structure of the proposed optimization problem is introduced as a non-linear multi-objective problem, and the Pareto optimization method is used to solve it. The innovative technique proposed to implement the optimization process of the mentioned problem is an optimization algorithm called the water cycle (WCA). To evaluate the proposed method, the IEEE 39 bus power system will be used. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=UPFC" title="UPFC">UPFC</a>, <a href="https://publications.waset.org/abstracts/search?q=SPA" title=" SPA"> SPA</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20cycle%20algorithm" title=" water cycle algorithm"> water cycle algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20problem" title=" multi-objective problem"> multi-objective problem</a>, <a href="https://publications.waset.org/abstracts/search?q=pareto" title=" pareto"> pareto</a> </p> <a href="https://publications.waset.org/abstracts/172408/optimal-placement-of-the-unified-power-controller-to-improve-the-power-system-restoration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172408.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">66</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8301</span> Analysis of the Statistical Characterization of Significant Wave Data Exceedances for Designing Offshore Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rui%20Teixeira">Rui Teixeira</a>, <a href="https://publications.waset.org/abstracts/search?q=Alan%20O%E2%80%99Connor"> Alan O’Connor</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Nogal"> Maria Nogal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The statistical theory of extreme events is progressively a topic of growing interest in all the fields of science and engineering. The changes currently experienced by the world, economic and environmental, emphasized the importance of dealing with extreme occurrences with improved accuracy. When it comes to the design of offshore structures, particularly offshore wind turbines, the importance of efficiently characterizing extreme events is of major relevance. Extreme events are commonly characterized by extreme values theory. As an alternative, the accurate modeling of the tails of statistical distributions and the characterization of the low occurrence events can be achieved with the application of the Peak-Over-Threshold (POT) methodology. The POT methodology allows for a more refined fit of the statistical distribution by truncating the data with a minimum value of a predefined threshold u. For mathematically approximating the tail of the empirical statistical distribution the Generalised Pareto is widely used. Although, in the case of the exceedances of significant wave data (H_s) the 2 parameters Weibull and the Exponential distribution, which is a specific case of the Generalised Pareto distribution, are frequently used as an alternative. The Generalized Pareto, despite the existence of practical cases where it is applied, is not completely recognized as the adequate solution to model exceedances over a certain threshold u. References that set the Generalised Pareto distribution as a secondary solution in the case of significant wave data can be identified in the literature. In this framework, the current study intends to tackle the discussion of the application of statistical models to characterize exceedances of wave data. Comparison of the application of the Generalised Pareto, the 2 parameters Weibull and the Exponential distribution are presented for different values of the threshold u. Real wave data obtained in four buoys along the Irish coast was used in the comparative analysis. Results show that the application of the statistical distributions to characterize significant wave data needs to be addressed carefully and in each particular case one of the statistical models mentioned fits better the data than the others. Depending on the value of the threshold u different results are obtained. Other variables of the fit, as the number of points and the estimation of the model parameters, are analyzed and the respective conclusions were drawn. Some guidelines on the application of the POT method are presented. Modeling the tail of the distributions shows to be, for the present case, a highly non-linear task and, due to its growing importance, should be addressed carefully for an efficient estimation of very low occurrence events. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20events" title="extreme events">extreme events</a>, <a href="https://publications.waset.org/abstracts/search?q=offshore%20structures" title=" offshore structures"> offshore structures</a>, <a href="https://publications.waset.org/abstracts/search?q=peak-over-threshold" title=" peak-over-threshold"> peak-over-threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=significant%20wave%20data" title=" significant wave data"> significant wave data</a> </p> <a href="https://publications.waset.org/abstracts/56287/analysis-of-the-statistical-characterization-of-significant-wave-data-exceedances-for-designing-offshore-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56287.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">272</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8300</span> Bi-Criteria Vehicle Routing Problem for Possibility Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bezhan%20Ghvaberidze">Bezhan Ghvaberidze</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A multiple criteria optimization approach for the solution of the Fuzzy Vehicle Routing Problem (FVRP) is proposed. For the possibility environment the levels of movements between customers are calculated by the constructed simulation interactive algorithm. The first criterion of the bi-criteria optimization problem - minimization of the expectation of total fuzzy travel time on closed routes is constructed for the FVRP. A new, second criterion – maximization of feasibility of movement on the closed routes is constructed by the Choquet finite averaging operator. The FVRP is reduced to the bi-criteria partitioning problem for the so called “promising” routes which were selected from the all admissible closed routes. The convenient selection of the “promising” routes allows us to solve the reduced problem in the real-time computing. For the numerical solution of the bi-criteria partitioning problem the -constraint approach is used. An exact algorithm is implemented based on D. Knuth’s Dancing Links technique and the algorithm DLX. The Main objective was to present the new approach for FVRP, when there are some difficulties while moving on the roads. This approach is called FVRP for extreme conditions (FVRP-EC) on the roads. Also, the aim of this paper was to construct the solving model of the constructed FVRP. Results are illustrated on the numerical example where all Pareto-optimal solutions are found. Also, an approach for more complex model FVRP with time windows was developed. A numerical example is presented in which optimal routes are constructed for extreme conditions on the roads. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combinatorial%20optimization" title="combinatorial optimization">combinatorial optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Fuzzy%20Vehicle%20routing%20problem" title=" Fuzzy Vehicle routing problem"> Fuzzy Vehicle routing problem</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20objective%20programming" title=" multiple objective programming"> multiple objective programming</a>, <a href="https://publications.waset.org/abstracts/search?q=possibility%20theory" title=" possibility theory"> possibility theory</a> </p> <a href="https://publications.waset.org/abstracts/59577/bi-criteria-vehicle-routing-problem-for-possibility-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59577.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">485</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8299</span> Improved Multi–Objective Firefly Algorithms to Find Optimal Golomb Ruler Sequences for Optimal Golomb Ruler Channel Allocation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shonak%20Bansal">Shonak Bansal</a>, <a href="https://publications.waset.org/abstracts/search?q=Prince%20Jain"> Prince Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Arun%20Kumar%20Singh"> Arun Kumar Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Neena%20Gupta"> Neena Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently nature–inspired algorithms have widespread use throughout the tough and time consuming multi–objective scientific and engineering design optimization problems. In this paper, we present extended forms of firefly algorithm to find optimal Golomb ruler (OGR) sequences. The OGRs have their one of the major application as unequally spaced channel–allocation algorithm in optical wavelength division multiplexing (WDM) systems in order to minimize the adverse four–wave mixing (FWM) crosstalk effect. The simulation results conclude that the proposed optimization algorithm has superior performance compared to the existing conventional computing and nature–inspired optimization algorithms to find OGRs in terms of ruler length, total optical channel bandwidth and computation time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=channel%20allocation" title="channel allocation">channel allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=conventional%20computing" title=" conventional computing"> conventional computing</a>, <a href="https://publications.waset.org/abstracts/search?q=four%E2%80%93wave%20mixing" title=" four–wave mixing"> four–wave mixing</a>, <a href="https://publications.waset.org/abstracts/search?q=nature%E2%80%93inspired%20algorithm" title=" nature–inspired algorithm"> nature–inspired algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20Golomb%20ruler" title=" optimal Golomb ruler"> optimal Golomb ruler</a>, <a href="https://publications.waset.org/abstracts/search?q=l%C3%A9vy%20flight%20distribution" title=" lévy flight distribution"> lévy flight distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=improved%20multi%E2%80%93objective%20firefly%20algorithms" title=" improved multi–objective firefly algorithms"> improved multi–objective firefly algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20optimal" title=" Pareto optimal"> Pareto optimal</a> </p> <a href="https://publications.waset.org/abstracts/46108/improved-multi-objective-firefly-algorithms-to-find-optimal-golomb-ruler-sequences-for-optimal-golomb-ruler-channel-allocation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46108.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8298</span> Combination Approach Using Experiments and Optimal Experimental Design to Optimize Chemical Concentration in Alkali-Surfactant-Polymer Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Tai%20Pham">H. Tai Pham</a>, <a href="https://publications.waset.org/abstracts/search?q=Bae%20Wisup"> Bae Wisup</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungmin%20Jung"> Sungmin Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivan%20Efriza"> Ivan Efriza</a>, <a href="https://publications.waset.org/abstracts/search?q=Ratna%20Widyaningsih"> Ratna Widyaningsih</a>, <a href="https://publications.waset.org/abstracts/search?q=Byung%20Un%20Min"> Byung Un Min</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The middle-phase-microemulsion in Alkaline-Surfactant-Polymer (ASP) solution and oil play important roles in the success of an ASP flooding process. The high quality microemulsion phase has ultralow interfacial tensions and it can increase oil recovery. The research used optimal experimental design and response-surface-methodology to predict the optimum concentration of chemicals in ASP solution for maximum microemulsion quality. Secondly, this optimal ASP formulation was implemented in core flooding test to investigate the effective injection volume. As the results, the optimum concentration of surfactants in the ASP solution is 0.57 wt.% and the highest effective injection volume is 19.33% pore volume. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimize" title="optimize">optimize</a>, <a href="https://publications.waset.org/abstracts/search?q=ASP" title=" ASP"> ASP</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface%20methodology" title=" response surface methodology"> response surface methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=solubilization%20ratio" title=" solubilization ratio"> solubilization ratio</a> </p> <a href="https://publications.waset.org/abstracts/55285/combination-approach-using-experiments-and-optimal-experimental-design-to-optimize-chemical-concentration-in-alkali-surfactant-polymer-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55285.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">348</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8297</span> A Hybrid Tabu Search Algorithm for the Multi-Objective Job Shop Scheduling Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a hybrid Tabu Search (TS) algorithm is suggested for the multi-objective job shop scheduling problems (MO-JSSPs). The algorithm integrates several shifting bottleneck based neighborhood structures with the Giffler & Thompson algorithm, which improve efficiency of the search. Diversification and intensification are provided with local and global left shift algorithms application and also new semi-active, active, and non-delay schedules creation. The suggested algorithm is tested in the MO-JSSPs benchmarks from the literature based on the Pareto optimality concept. Different performances criteria are used for the multi-objective algorithm evaluation. The proposed algorithm is able to find the Pareto solutions of the test problems in shorter time than other algorithm of the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tabu%20search" title="tabu search">tabu search</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics" title=" heuristics"> heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=job%20shop%20scheduling" title=" job shop scheduling"> job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20optimality" title=" Pareto optimality"> Pareto optimality</a> </p> <a href="https://publications.waset.org/abstracts/71920/a-hybrid-tabu-search-algorithm-for-the-multi-objective-job-shop-scheduling-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71920.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">443</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=pareto%20optimal%20solution&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=pareto%20optimal%20solution&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=pareto%20optimal%20solution&page=4">4</a></li> <li class="page-item"><a class="page-link" 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