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optimization</title> <meta name="description" content="Search results for: local optimization"> <meta name="keywords" content="local optimization"> <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 class="d-block d-lg-none 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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="local optimization"> <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> 8449</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: local optimization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8449</span> Improved Particle Swarm Optimization with Cellular Automata and Fuzzy Cellular Automata</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Javadzadeh">Ramin Javadzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The particle swarm optimization are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Particle swarm optimization is introduced for the first time to overcome its problems. The fuzzy cellular automata is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the Particle swarm optimization algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cellular%20automata" title="cellular automata">cellular automata</a>, <a href="https://publications.waset.org/abstracts/search?q=cellular%20learning%20automata" title=" cellular learning automata"> cellular learning automata</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=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/24739/improved-particle-swarm-optimization-with-cellular-automata-and-fuzzy-cellular-automata" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24739.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">606</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">8448</span> Improved Whale Algorithm Based on Information Entropy and Its Application in Truss Structure Optimization Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serges%20Mendomo%20%20Meye">Serges Mendomo Meye</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Guowei"> Li Guowei</a>, <a href="https://publications.waset.org/abstracts/search?q=Shen%20Zhenzhong"> Shen Zhenzhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Gan%20Lei"> Gan Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Xu%20Liqun"> Xu Liqun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given the limitations of the original whale optimization algorithm (WAO) in local optimum and low convergence accuracy in truss structure optimization problems, based on the fundamental whale algorithm, an improved whale optimization algorithm (SWAO) based on information entropy is proposed. The information entropy itself is an uncertain measure. It is used to control the range of whale searches in path selection. It can overcome the shortcomings of the basic whale optimization algorithm (WAO) and can improve the global convergence speed of the algorithm. Taking truss structure as the optimization research object, the mathematical model of truss structure optimization is established; the cross-sectional area of truss is taken as the design variable; the objective function is the weight of truss structure; and an improved whale optimization algorithm (SWAO) is used for optimization design, which provides a new idea and means for its application in large and complex engineering structure optimization design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20entropy" title="information entropy">information entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20optimization" title=" structural optimization"> structural optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=truss%20structure" title=" truss structure"> truss structure</a>, <a href="https://publications.waset.org/abstracts/search?q=whale%20algorithm" title=" whale algorithm"> whale algorithm</a> </p> <a href="https://publications.waset.org/abstracts/139986/improved-whale-algorithm-based-on-information-entropy-and-its-application-in-truss-structure-optimization-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139986.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">249</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">8447</span> Parallel 2-Opt Local Search on GPU</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wen-Bao%20Qiao">Wen-Bao Qiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Charles%20Cr%C3%A9put"> Jean-Charles Créput</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To accelerate the solution for large scale traveling salesman problems (TSP), a parallel 2-opt local search algorithm with simple implementation based on Graphics Processing Unit (GPU) is presented and tested in this paper. The parallel scheme is based on technique of data decomposition by dynamically assigning multiple K processors on the integral tour to treat K edges’ 2-opt local optimization simultaneously on independent sub-tours, where K can be user-defined or have a function relationship with input size N. We implement this algorithm with doubly linked list on GPU. The implementation only requires O(N) memory. We compare this parallel 2-opt local optimization against sequential exhaustive 2-opt search along integral tour on TSP instances from TSPLIB with more than 10000 cities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parallel%202-opt" title="parallel 2-opt">parallel 2-opt</a>, <a href="https://publications.waset.org/abstracts/search?q=double%20links" title=" double links"> double links</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20scale%20TSP" title=" large scale TSP"> large scale TSP</a>, <a href="https://publications.waset.org/abstracts/search?q=GPU" title=" GPU"> GPU</a> </p> <a href="https://publications.waset.org/abstracts/58582/parallel-2-opt-local-search-on-gpu" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58582.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">624</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">8446</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">8445</span> A Multi-Population DE with Adaptive Mutation and Local Search for Global Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhoucheng%20Bao">Zhoucheng Bao</a>, <a href="https://publications.waset.org/abstracts/search?q=Haiyan%20Zhu"> Haiyan Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tingting%20Pang"> Tingting Pang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zuling%20Wang"> Zuling Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a multi-population DE with adaptive mutation and local search for global optimization, named AMMADE. In order to better coordinate the cooperation between the populations and the rational use of resources. In AMMADE, the population is divided based on the Euclidean distance sorting method at each generation to appropriately coordinate the cooperation between subpopulations and the usage of resources, such that the best-performed subpopulation will get more computing resources in the next generation. Further, an adaptive local search strategy is employed on the best-performed subpopulation to achieve a balanced search. The proposed algorithm has been tested by solving optimization problems taken from CEC2014 benchmark problems. Experimental results show that our algorithm can achieve a competitive or better than related methods. The results also confirm the significance of devised strategies in the proposed algorithm. <p class="card-text"><strong>Keywords:</strong> <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=multi-mutation%20strategies" title=" multi-mutation strategies"> multi-mutation strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=memetic%20algorithm" title=" memetic algorithm"> memetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20local%20search" title=" adaptive local search"> adaptive local search</a> </p> <a href="https://publications.waset.org/abstracts/145112/a-multi-population-de-with-adaptive-mutation-and-local-search-for-global-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145112.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">157</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">8444</span> Elephant Herding Optimization for Service Selection in QoS-Aware Web Service Composition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samia%20Sadouki%20Chibani">Samia Sadouki Chibani</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkamel%20Tari"> Abdelkamel Tari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Web service composition combines available services to provide new functionality. Given the number of available services with similar functionalities and different non functional aspects (QoS), the problem of finding a QoS-optimal web service composition is considered as an optimization problem belonging to NP-hard class. Thus, an optimal solution cannot be found by exact algorithms within a reasonable time. In this paper, a meta-heuristic bio-inspired is presented to address the QoS aware web service composition; it is based on Elephant Herding Optimization (EHO) algorithm, which is inspired by the herding behavior of elephant group. EHO is characterized by a process of dividing and combining the population to sub populations (clan); this process allows the exchange of information between local searches to move toward a global optimum. However, with Applying others evolutionary algorithms the problem of early stagnancy in a local optimum cannot be avoided. Compared with PSO, the results of experimental evaluation show that our proposition significantly outperforms the existing algorithm with better performance of the fitness value and a fast convergence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bio-inspired%20algorithms" title="bio-inspired algorithms">bio-inspired algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=elephant%20herding%20optimization" title=" elephant herding optimization"> elephant herding optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=QoS%20optimization" title=" QoS optimization"> QoS optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20service%20composition" title=" web service composition"> web service composition</a> </p> <a href="https://publications.waset.org/abstracts/64274/elephant-herding-optimization-for-service-selection-in-qos-aware-web-service-composition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64274.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">327</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">8443</span> Ant System with Acoustic Communication</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saad%20Bougrine">Saad Bougrine</a>, <a href="https://publications.waset.org/abstracts/search?q=Salma%20Ouchraa"> Salma Ouchraa</a>, <a href="https://publications.waset.org/abstracts/search?q=Belaid%20Ahiod"> Belaid Ahiod</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhakim%20Ameur%20El%20Imrani"> Abdelhakim Ameur El Imrani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ant colony optimization is an ant algorithm framework that took inspiration from foraging behaviour of ant colonies. Indeed, ACO algorithms use a chemical communication, represented by pheromone trails, to build good solutions. However, ants involve different communication channels to interact. Thus, this paper introduces the acoustic communication between ants while they are foraging. This process allows fine and local exploration of search space and permits optimal solution to be improved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acoustic%20communication" title="acoustic communication">acoustic communication</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title=" ant colony optimization"> ant colony 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=traveling%20salesman%20problem" title=" traveling salesman problem"> traveling salesman problem</a> </p> <a href="https://publications.waset.org/abstracts/7857/ant-system-with-acoustic-communication" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7857.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">586</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">8442</span> Evaluation of a Surrogate Based Method for Global Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=David%20Lindstr%C3%B6m">David Lindström</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We evaluate the performance of a numerical method for global optimization of expensive functions. The method is using a response surface to guide the search for the global optimum. This metamodel could be based on radial basis functions, kriging, or a combination of different models. We discuss how to set the cycling parameters of the optimization method to get a balance between local and global search. We also discuss the eventual problem with Runge oscillations in the response surface. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expensive%20function" title="expensive function">expensive function</a>, <a href="https://publications.waset.org/abstracts/search?q=infill%20sampling%20criterion" title=" infill sampling criterion"> infill sampling criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=kriging" title=" kriging"> kriging</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20optimization" title=" global optimization"> global optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface" title=" response surface"> response surface</a>, <a href="https://publications.waset.org/abstracts/search?q=Runge%20phenomenon" title=" Runge phenomenon"> Runge phenomenon</a> </p> <a href="https://publications.waset.org/abstracts/24538/evaluation-of-a-surrogate-based-method-for-global-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24538.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">578</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">8441</span> An Enhanced Particle Swarm Optimization Algorithm for Multiobjective Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Houda%20Abadlia">Houda Abadlia</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Smairi"> Nadia Smairi</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Ghedira"> Khaled Ghedira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multiobjective Particle Swarm Optimization (MOPSO) has shown an effective performance for solving test functions and real-world optimization problems. However, this method has a premature convergence problem, which may lead to lack of diversity. In order to improve its performance, this paper presents a hybrid approach which embedded the MOPSO into the island model and integrated a local search technique, Variable Neighborhood Search, to enhance the diversity into the swarm. Experiments on two series of test functions have shown the effectiveness of the proposed approach. A comparison with other evolutionary algorithms shows that the proposed approach presented a good performance in solving multiobjective optimization problems. <p class="card-text"><strong>Keywords:</strong> <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=migration" title=" migration"> migration</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20neighborhood%20search" title=" variable neighborhood search"> variable neighborhood search</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/99544/an-enhanced-particle-swarm-optimization-algorithm-for-multiobjective-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99544.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">8440</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">8439</span> Second Order Optimality Conditions in Nonsmooth Analysis on Riemannian Manifolds</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyedehsomayeh%20Hosseini">Seyedehsomayeh Hosseini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Much attention has been paid over centuries to understanding and solving the problem of minimization of functions. Compared to linear programming and nonlinear unconstrained optimization problems, nonlinear constrained optimization problems are much more difficult. Since the procedure of finding an optimizer is a search based on the local information of the constraints and the objective function, it is very important to develop techniques using geometric properties of the constraints and the objective function. In fact, differential geometry provides a powerful tool to characterize and analyze these geometric properties. Thus, there is clearly a link between the techniques of optimization on manifolds and standard constrained optimization approaches. Furthermore, there are manifolds that are not defined as constrained sets in R^n an important example is the Grassmann manifolds. Hence, to solve optimization problems on these spaces, intrinsic methods are used. In a nondifferentiable problem, the gradient information of the objective function generally cannot be used to determine the direction in which the function is decreasing. Therefore, techniques of nonsmooth analysis are needed to deal with such a problem. As a manifold, in general, does not have a linear structure, the usual techniques, which are often used in nonsmooth analysis on linear spaces, cannot be applied and new techniques need to be developed. This paper presents necessary and sufficient conditions for a strict local minimum of extended real-valued, nonsmooth functions defined on Riemannian manifolds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Riemannian%20manifolds" title="Riemannian manifolds">Riemannian manifolds</a>, <a href="https://publications.waset.org/abstracts/search?q=nonsmooth%20optimization" title=" nonsmooth optimization"> nonsmooth optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=lower%20semicontinuous%20functions" title=" lower semicontinuous functions"> lower semicontinuous functions</a>, <a href="https://publications.waset.org/abstracts/search?q=subdifferential" title=" subdifferential"> subdifferential</a> </p> <a href="https://publications.waset.org/abstracts/35809/second-order-optimality-conditions-in-nonsmooth-analysis-on-riemannian-manifolds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35809.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">361</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">8438</span> A Hybrid Particle Swarm Optimization-Nelder- Mead Algorithm (PSO-NM) for Nelson-Siegel- Svensson Calibration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Ayouche">Sofia Ayouche</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachid%20Ellaia"> Rachid Ellaia</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajae%20Aboulaich"> Rajae Aboulaich</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today, insurers may use the yield curve as an indicator evaluation of the profit or the performance of their portfolios; therefore, they modeled it by one class of model that has the ability to fit and forecast the future term structure of interest rates. This class of model is the Nelson-Siegel-Svensson model. Unfortunately, many authors have reported a lot of difficulties when they want to calibrate the model because the optimization problem is not convex and has multiple local optima. In this context, we implement a hybrid Particle Swarm optimization and Nelder Mead algorithm in order to minimize by least squares method, the difference between the zero-coupon curve and the NSS curve. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=zero-coupon%20curve" title=" zero-coupon curve"> zero-coupon curve</a>, <a href="https://publications.waset.org/abstracts/search?q=Nelson-Siegel-Svensson" title=" Nelson-Siegel-Svensson"> Nelson-Siegel-Svensson</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=Nelder-Mead%20algorithm" title=" Nelder-Mead algorithm"> Nelder-Mead algorithm</a> </p> <a href="https://publications.waset.org/abstracts/48619/a-hybrid-particle-swarm-optimization-nelder-mead-algorithm-pso-nm-for-nelson-siegel-svensson-calibration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48619.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">430</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">8437</span> A Genetic Algorithm for the Load Balance of Parallel Computational Fluid Dynamics Computation with Multi-Block Structured Mesh</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chunye%20Gong">Chunye Gong</a>, <a href="https://publications.waset.org/abstracts/search?q=Ming%20Tie"> Ming Tie</a>, <a href="https://publications.waset.org/abstracts/search?q=Jie%20Liu"> Jie Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Weimin%20Bao"> Weimin Bao</a>, <a href="https://publications.waset.org/abstracts/search?q=Xinbiao%20Gan"> Xinbiao Gan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shengguo%20Li"> Shengguo Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Yang"> Bo Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xuguang%20Chen"> Xuguang Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Tiaojie%20Xiao"> Tiaojie Xiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang%20Sun"> Yang Sun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Large-scale CFD simulation relies on high-performance parallel computing, and the load balance is the key role which affects the parallel efficiency. This paper focuses on the load-balancing problem of parallel CFD simulation with structured mesh. A mathematical model for this load-balancing problem is presented. The genetic algorithm, fitness computing, two-level code are designed. Optimal selector, robust operator, and local optimization operator are designed. The properties of the presented genetic algorithm are discussed in-depth. The effects of optimal selector, robust operator, and local optimization operator are proved by experiments. The experimental results of different test sets, DLR-F4, and aircraft design applications show the presented load-balancing algorithm is robust, quickly converged, and is useful in real engineering problems. <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=load-balancing%20algorithm" title=" load-balancing algorithm"> load-balancing algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20variation" title=" optimal variation"> optimal variation</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20optimization" title=" local optimization"> local optimization</a> </p> <a href="https://publications.waset.org/abstracts/131281/a-genetic-algorithm-for-the-load-balance-of-parallel-computational-fluid-dynamics-computation-with-multi-block-structured-mesh" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131281.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">183</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">8436</span> Interactive Winding Geometry Design of Power Transformers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paffrath%20Meinhard">Paffrath Meinhard</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhou%20Yayun"> Zhou Yayun</a>, <a href="https://publications.waset.org/abstracts/search?q=Guo%20Yiqing"> Guo Yiqing</a>, <a href="https://publications.waset.org/abstracts/search?q=Ertl%20Harald"> Ertl Harald</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Winding geometry design is an important part of power transformer electrical design. Conventionally, the winding geometry is designed manually, which is a time-consuming job because it involves many iteration steps in order to meet all cost, manufacturing and electrical requirements. Here a method is presented which automatically generates the winding geometry for given user parameters and allows the user to interactively set and change parameters. To achieve this goal, the winding problem is transferred to a mixed integer nonlinear optimization problem. The relevant geometrical design parameters are defined as optimization variables. The cost and other requirements are modeled as constraints. For the solution, a stochastic ant colony optimization algorithm is applied. It is well-known, that an optimizer can get stuck in a local minimum. For the winding problem, we present efficient strategies to come out of local minima, furthermore a reduced variable search range helps to accelerate the solution process. Numerical examples show that the optimization result is delivered within seconds such that the user can interactively change the variable search area and constraints to improve the design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title="ant colony optimization">ant colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20integer%20nonlinear%20programming" title=" mixed integer nonlinear programming"> mixed integer nonlinear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20transformer" title=" power transformer"> power transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=winding%20design" title=" winding design"> winding design</a> </p> <a href="https://publications.waset.org/abstracts/74700/interactive-winding-geometry-design-of-power-transformers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74700.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">380</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">8435</span> Demand Forecasting Using Artificial Neural Networks Optimized by Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daham%20Owaid%20Matrood">Daham Owaid Matrood</a>, <a href="https://publications.waset.org/abstracts/search?q=Naqaa%20Hussein%20Raheem"> Naqaa Hussein Raheem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20forecasting" title=" demand forecasting"> demand forecasting</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=weight%20optimization" title=" weight optimization"> weight optimization</a> </p> <a href="https://publications.waset.org/abstracts/45069/demand-forecasting-using-artificial-neural-networks-optimized-by-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45069.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">451</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">8434</span> Improved Multi-Objective Particle Swarm Optimization Applied to Design Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kapse%20Swapnil">Kapse Swapnil</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Shankar"> K. Shankar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aiming at optimizing the weight and deflection of cantilever beam subjected to maximum stress and maximum deflection, Multi-objective Particle Swarm Optimization (MOPSO) with Utopia Point based local search is implemented. Utopia point is used to govern the search towards the Pareto Optimal set. The elite candidates obtained during the iterations are stored in an archive according to non-dominated sorting and also the archive is truncated based on least crowding distance. Local search is also performed on elite candidates and the most diverse particle is selected as the global best. This method is implemented on standard test functions and it is observed that the improved algorithm gives better convergence and diversity as compared to NSGA-II in fewer iterations. Implementation on practical structural problem shows that in 5 to 6 iterations, the improved algorithm converges with better diversity as evident by the improvement of cantilever beam on an average of 0.78% and 9.28% in the weight and deflection respectively compared to NSGA-II. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Utopia%20point" title="Utopia point">Utopia point</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20particle%20swarm%20optimization" title=" multi-objective particle swarm optimization"> multi-objective particle swarm 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=cantilever%20beam" title=" cantilever beam"> cantilever beam</a> </p> <a href="https://publications.waset.org/abstracts/65174/improved-multi-objective-particle-swarm-optimization-applied-to-design-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65174.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">519</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">8433</span> Curve Fitting by Cubic Bezier Curves Using Migrating Birds Optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mitat%20Uysal">Mitat Uysal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new met heuristic optimization algorithm called as Migrating Birds Optimization is used for curve fitting by rational cubic Bezier Curves. This requires solving a complicated multivariate optimization problem. In this study, the solution of this optimization problem is achieved by Migrating Birds Optimization algorithm that is a powerful met heuristic nature-inspired algorithm well appropriate for optimization. The results of this study show that the proposed method performs very well and being able to fit the data points to cubic Bezier Curves with a high degree of accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Bezier%20curves" title=" Bezier curves"> Bezier curves</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20optimization" title=" heuristic optimization"> heuristic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=migrating%20birds%20optimization" title=" migrating birds optimization"> migrating birds optimization</a> </p> <a href="https://publications.waset.org/abstracts/78026/curve-fitting-by-cubic-bezier-curves-using-migrating-birds-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78026.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">336</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">8432</span> Optimal Design of Multimachine Power System Stabilizers Using Improved Multi-Objective Particle Swarm Optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Badr%20M.%20Alshammari">Badr M. Alshammari</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Guesmi"> T. Guesmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the concept of a non-dominated sorting multi-objective particle swarm optimization with local search (NSPSO-LS) is presented for the optimal design of multimachine power system stabilizers (PSSs). The controller design is formulated as an optimization problem in order to shift the system electromechanical modes in a pre-specified region in the s-plan. A composite set of objective functions comprising the damping factor and the damping ratio of the undamped and lightly damped electromechanical modes is considered. The performance of the proposed optimization algorithm is verified for the 3-machine 9-bus system. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation show the potential and superiority of the NSPSO-LS algorithm in tuning PSSs over a wide range of loading conditions and large disturbance compared to the classic PSO technique and genetic algorithms. <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=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20system%20stabilizer" title=" power system stabilizer"> power system stabilizer</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20frequency%20oscillations" title=" low frequency oscillations"> low frequency oscillations</a> </p> <a href="https://publications.waset.org/abstracts/60093/optimal-design-of-multimachine-power-system-stabilizers-using-improved-multi-objective-particle-swarm-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60093.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">431</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">8431</span> Neighborhood Graph-Optimized Preserving Discriminant Analysis for Image Feature Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaoheng%20Tan">Xiaoheng Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xianfang%20Li"> Xianfang Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Tan%20Guo"> Tan Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuchuan%20Liu"> Yuchuan Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhijun%20Yang"> Zhijun Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongye%20Li"> Hongye Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai%20Fu"> Kai Fu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yufang%20Wu"> Yufang Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Heling%20Gong"> Heling Gong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The image data collected in reality often have high dimensions, and it contains noise and redundant information. Therefore, it is necessary to extract the compact feature expression of the original perceived image. In this process, effective use of prior knowledge such as data structure distribution and sample label is the key to enhance image feature discrimination and robustness. Based on the above considerations, this paper proposes a local preserving discriminant feature learning model based on graph optimization. The model has the following characteristics: (1) Locality preserving constraint can effectively excavate and preserve the local structural relationship between data. (2) The flexibility of graph learning can be improved by constructing a new local geometric structure graph using label information and the nearest neighbor threshold. (3) The L₂,₁ norm is used to redefine LDA, and the diagonal matrix is introduced as the scale factor of LDA, and the samples are selected, which improves the robustness of feature learning. The validity and robustness of the proposed algorithm are verified by experiments in two public image datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title="feature extraction">feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20optimization%20local%20preserving%20projection" title=" graph optimization local preserving projection"> graph optimization local preserving projection</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=L%E2%82%82" title=" L₂"> L₂</a>, <a href="https://publications.waset.org/abstracts/search?q=%E2%82%81%20norm" title="₁ norm">₁ norm</a> </p> <a href="https://publications.waset.org/abstracts/129863/neighborhood-graph-optimized-preserving-discriminant-analysis-for-image-feature-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129863.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">149</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">8430</span> Hybrid Adaptive Modeling to Enhance Robustness of Real-Time Optimization </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hussain%20Syed%20Asad">Hussain Syed Asad</a>, <a href="https://publications.waset.org/abstracts/search?q=Richard%20Kwok%20Kit%20Yuen"> Richard Kwok Kit Yuen</a>, <a href="https://publications.waset.org/abstracts/search?q=Gongsheng%20Huang"> Gongsheng Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Real-time optimization has been considered an effective approach for improving energy efficient operation of heating, ventilation, and air-conditioning (HVAC) systems. In model-based real-time optimization, model mismatches cannot be avoided. When model mismatches are significant, the performance of the real-time optimization will be impaired and hence the expected energy saving will be reduced. In this paper, the model mismatches for chiller plant on real-time optimization are considered. In the real-time optimization of the chiller plant, simplified semi-physical or grey box model of chiller is always used, which should be identified using available operation data. To overcome the model mismatches associated with the chiller model, hybrid Genetic Algorithms (HGAs) method is used for online real-time training of the chiller model. HGAs combines Genetic Algorithms (GAs) method (for global search) and traditional optimization method (i.e. faster and more efficient for local search) to avoid conventional hit and trial process of GAs. The identification of model parameters is synthesized as an optimization problem; and the objective function is the Least Square Error between the output from the model and the actual output from the chiller plant. A case study is used to illustrate the implementation of the proposed method. It has been shown that the proposed approach is able to provide reliability in decision making, enhance the robustness of the real-time optimization strategy and improve on energy performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20performance" title="energy performance">energy performance</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20adaptive%20modeling" title=" hybrid adaptive modeling"> hybrid adaptive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20genetic%20algorithms" title=" hybrid genetic algorithms"> hybrid genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20optimization" title=" real-time optimization"> real-time optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=heating" title=" heating"> heating</a>, <a href="https://publications.waset.org/abstracts/search?q=ventilation" title=" ventilation"> ventilation</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20air-conditioning" title=" and air-conditioning"> and air-conditioning</a> </p> <a href="https://publications.waset.org/abstracts/59904/hybrid-adaptive-modeling-to-enhance-robustness-of-real-time-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59904.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">417</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">8429</span> A Mean–Variance–Skewness Portfolio Optimization Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Metaxiotis">Kostas Metaxiotis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portfolio optimization is one of the most important topics in finance. This paper proposes a mean–variance–skewness (MVS) portfolio optimization model. Traditionally, the portfolio optimization problem is solved by using the mean–variance (MV) framework. In this study, we formulate the proposed model as a three-objective optimization problem, where the portfolio's expected return and skewness are maximized whereas the portfolio risk is minimized. For solving the proposed three-objective portfolio optimization model we apply an adapted version of the non-dominated sorting genetic algorithm (NSGAII). Finally, we use a real dataset from FTSE-100 for validating the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title="evolutionary algorithms">evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=skewness" title=" skewness"> skewness</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20selection" title=" stock selection"> stock selection</a> </p> <a href="https://publications.waset.org/abstracts/102472/a-mean-variance-skewness-portfolio-optimization-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102472.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">198</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">8428</span> Local Directional Encoded Derivative Binary Pattern Based Coral Image Classification Using Weighted Distance Gray Wolf Optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Annalakshmi%20G.">Annalakshmi G.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sakthivel%20Murugan%20S."> Sakthivel Murugan S.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title="feature extraction">feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20directional%20pattern" title=" local directional pattern"> local directional pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=ELM%20classifier" title=" ELM classifier"> ELM classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=GWO%20optimization" title=" GWO optimization"> GWO optimization</a> </p> <a href="https://publications.waset.org/abstracts/142439/local-directional-encoded-derivative-binary-pattern-based-coral-image-classification-using-weighted-distance-gray-wolf-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142439.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">163</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">8427</span> Descent Algorithms for Optimization Algorithms Using q-Derivative</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Geetanjali%20Panda">Geetanjali Panda</a>, <a href="https://publications.waset.org/abstracts/search?q=Suvrakanti%20Chakraborty"> Suvrakanti Chakraborty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, Newton-like descent methods are proposed for unconstrained optimization problems, which use q-derivatives of the gradient of an objective function. First, a local scheme is developed with alternative sufficient optimality condition, and then the method is extended to a global scheme. Moreover, a variant of practical Newton scheme is also developed introducing a real sequence. Global convergence of these schemes is proved under some mild conditions. Numerical experiments and graphical illustrations are provided. Finally, the performance profiles on a test set show that the proposed schemes are competitive to the existing first-order schemes for optimization problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Descent%20algorithm" title="Descent algorithm">Descent algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20search%20method" title=" line search method"> line search method</a>, <a href="https://publications.waset.org/abstracts/search?q=q%20calculus" title=" q calculus"> q calculus</a>, <a href="https://publications.waset.org/abstracts/search?q=Quasi%20Newton%20method" title=" Quasi Newton method"> Quasi Newton method</a> </p> <a href="https://publications.waset.org/abstracts/62700/descent-algorithms-for-optimization-algorithms-using-q-derivative" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62700.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">398</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">8426</span> Non-Stationary Stochastic Optimization of an Oscillating Water Column</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mar%C3%ADa%20L.%20Jal%C3%B3n">María L. Jalón</a>, <a href="https://publications.waset.org/abstracts/search?q=Feargal%20Brennan"> Feargal Brennan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A non-stationary stochastic optimization methodology is applied to an OWC (oscillating water column) to find the design that maximizes the wave energy extraction. Different temporal cycles are considered to represent the long-term variability of the wave climate at the site in the optimization problem. The results of the non-stationary stochastic optimization problem are compared against those obtained by a stationary stochastic optimization problem. The comparative analysis reveals that the proposed non-stationary optimization provides designs with a better fit to reality. However, the stationarity assumption can be adequate when looking at averaged system response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-stationary%20stochastic%20optimization" title="non-stationary stochastic optimization">non-stationary stochastic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=oscillating%20water" title=" oscillating water"> oscillating water</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20variability" title=" temporal variability"> temporal variability</a>, <a href="https://publications.waset.org/abstracts/search?q=wave%20energy" title=" wave energy"> wave energy</a> </p> <a href="https://publications.waset.org/abstracts/75300/non-stationary-stochastic-optimization-of-an-oscillating-water-column" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75300.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">373</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">8425</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">8424</span> Co-Evolutionary Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Unconstrained Optimization Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20M.%20Rizk-Allah">R. M. Rizk-Allah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents co-evolutionary fruit fly optimization algorithm based on firefly algorithm (CFOA-FA) for solving unconstrained optimization problems. The proposed algorithm integrates the merits of fruit fly optimization algorithm (FOA), firefly algorithm (FA) and elite strategy to refine the performance of classical FOA. Moreover, co-evolutionary mechanism is performed by applying FA procedures to ensure the diversity of the swarm. Finally, the proposed algorithm CFOA- FA is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm for finding the global optimal solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=firefly%20algorithm" title="firefly algorithm">firefly algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=fruit%20fly%20optimization%20algorithm" title=" fruit fly optimization algorithm"> fruit fly optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization%20problems" title=" unconstrained optimization problems"> unconstrained optimization problems</a> </p> <a href="https://publications.waset.org/abstracts/15923/co-evolutionary-fruit-fly-optimization-algorithm-and-firefly-algorithm-for-solving-unconstrained-optimization-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15923.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">536</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">8423</span> Efficiency of Robust Heuristic Gradient Based Enumerative and Tunneling Algorithms for Constrained Integer Programming Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vijaya%20K.%20Srivastava">Vijaya K. Srivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=Davide%20Spinello"> Davide Spinello</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents performance of two robust gradient-based heuristic optimization procedures based on 3<sup>n</sup> enumeration and tunneling approach to seek global optimum of constrained integer problems. Both these procedures consist of two distinct phases for locating the global optimum of integer problems with a linear or non-linear objective function subject to linear or non-linear constraints. In both procedures, in the first phase, a local minimum of the function is found using the gradient approach coupled with hemstitching moves when a constraint is violated in order to return the search to the feasible region. In the second phase, in one optimization procedure, the second sub-procedure examines 3<sup>n</sup> integer combinations on the boundary and within hypercube volume encompassing the result neighboring the result from the first phase and in the second optimization procedure a tunneling function is constructed at the local minimum of the first phase so as to find another point on the other side of the barrier where the function value is approximately the same. In the next cycle, the search for the global optimum commences in both optimization procedures again using this new-found point as the starting vector. The search continues and repeated for various step sizes along the function gradient as well as that along the vector normal to the violated constraints until no improvement in optimum value is found. The results from both these proposed optimization methods are presented and compared with one provided by popular MS Excel solver that is provided within MS Office suite and other published results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=constrained%20integer%20problems" title="constrained integer problems">constrained integer problems</a>, <a href="https://publications.waset.org/abstracts/search?q=enumerative%20search%20algorithm" title=" enumerative search algorithm"> enumerative search algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Heuristic%20algorithm" title=" Heuristic algorithm"> Heuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Tunneling%20algorithm" title=" Tunneling algorithm"> Tunneling algorithm</a> </p> <a href="https://publications.waset.org/abstracts/58272/efficiency-of-robust-heuristic-gradient-based-enumerative-and-tunneling-algorithms-for-constrained-integer-programming-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58272.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">325</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">8422</span> Model of Optimal Centroids Approach for Multivariate Data Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pham%20Van%20Nha">Pham Van Nha</a>, <a href="https://publications.waset.org/abstracts/search?q=Le%20Cam%20Binh"> Le Cam Binh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analysis%20of%20optimization" title="analysis of optimization">analysis of optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence%20based%20optimization" title=" artificial intelligence based optimization"> artificial intelligence based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20for%20learning%20and%20data%20analysis" title=" optimization for learning and data analysis"> optimization for learning and data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20optimization" title=" global optimization"> global optimization</a> </p> <a href="https://publications.waset.org/abstracts/126058/model-of-optimal-centroids-approach-for-multivariate-data-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126058.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">208</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">8421</span> A Novel Approach of NPSO on Flexible Logistic (S-Shaped) Model for Software Reliability Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pooja%20Rani">Pooja Rani</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20S.%20Mahapatra"> G. S. Mahapatra</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20K.%20Pandey"> S. K. Pandey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=software%20reliability" title="software reliability">software reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20logistic%20growth%20curve%20model" title=" flexible logistic growth curve model"> flexible logistic growth curve model</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20cumulative%20failure%20prediction" title=" software cumulative failure prediction"> software cumulative failure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/36601/a-novel-approach-of-npso-on-flexible-logistic-s-shaped-model-for-software-reliability-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36601.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">344</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">8420</span> Cuckoo Search (CS) Optimization Algorithm for Solving Constrained Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sait%20Ali%20Uymaz">Sait Ali Uymaz</a>, <a href="https://publications.waset.org/abstracts/search?q=G%C3%BClay%20Tezel"> Gülay Tezel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the comparison results on the performance of the Cuckoo Search (CS) algorithm for constrained optimization problems. For constraint handling, CS algorithm uses penalty method. CS algorithm is tested on thirteen well-known test problems and the results obtained are compared to Particle Swarm Optimization (PSO) algorithm. Mean, best, median and worst values were employed for the analyses of performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cuckoo%20search" title="cuckoo search">cuckoo search</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=constrained%20optimization%20problems" title=" constrained optimization problems"> constrained optimization problems</a>, <a href="https://publications.waset.org/abstracts/search?q=penalty%20method" title=" penalty method"> penalty method</a> </p> <a href="https://publications.waset.org/abstracts/13991/cuckoo-search-cs-optimization-algorithm-for-solving-constrained-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13991.pdf" target="_blank" class="btn btn-primary 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