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parameters</title> <meta name="description" content="Search results for: optimization parameters"> <meta name="keywords" content="optimization parameters"> <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 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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="optimization parameters"> <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> 11154</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: optimization parameters</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11094</span> Design Optimisation of a Novel Cross Vane Expander-Compressor Unit for Refrigeration System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20D.%20Lim">Y. D. Lim</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20S.%20Yap"> K. S. Yap</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20T.%20Ooi"> K. T. Ooi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, environmental issue has been a hot topic in the world, especially the global warming effect caused by conventional non-environmentally friendly refrigerants has increased. Several studies of a more energy-efficient and environmentally friendly refrigeration system have been conducted in order to tackle the issue. In search of a better refrigeration system, CO2 refrigeration system has been proposed as a better option. However, the high throttling loss involved during the expansion process of the refrigeration cycle leads to a relatively low efficiency and thus the system is impractical. In order to improve the efficiency of the refrigeration system, it is suggested by replacing the conventional expansion valve in the refrigeration system with an expander. Based on this issue, a new type of expander-compressor combined unit, named Cross Vane Expander-Compressor (CVEC) was introduced to replace the compressor and the expansion valve of a conventional refrigeration system. A mathematical model was developed to calculate the performance of CVEC, and it was found that the machine is capable of saving the energy consumption of a refrigeration system by as much as 18%. Apart from energy saving, CVEC is also geometrically simpler and more compact. To further improve its efficiency, optimization study of the device is carried out. In this report, several design parameters of CVEC were chosen to be the variables of optimization study. This optimization study was done in a simulation program by using complex optimization method, which is a direct search, multi-variables and constrained optimization method. It was found that the main design parameters, which was shaft radius was reduced around 8% while the inner cylinder radius was remained unchanged at its lower limit after optimization. Furthermore, the port sizes were increased to their upper limit after optimization. The changes of these design parameters have resulted in reduction of around 12% in the total frictional loss and reduction of 4% in power consumption. Eventually, the optimization study has resulted in an improvement in the mechanical efficiency CVEC by 4% and improvement in COP by 6%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complex%20optimization%20method" title="complex optimization method">complex optimization method</a>, <a href="https://publications.waset.org/abstracts/search?q=COP" title=" COP"> COP</a>, <a href="https://publications.waset.org/abstracts/search?q=cross%20vane%20expander-compressor" title=" cross vane expander-compressor"> cross vane expander-compressor</a>, <a href="https://publications.waset.org/abstracts/search?q=CVEC" title=" CVEC"> CVEC</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20optimization" title=" design optimization"> design optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=direct%20search" title=" direct search"> direct search</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20saving" title=" energy saving"> energy saving</a>, <a href="https://publications.waset.org/abstracts/search?q=improvement" title=" improvement"> improvement</a>, <a href="https://publications.waset.org/abstracts/search?q=mechanical%20efficiency" title=" mechanical efficiency"> mechanical efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20variables" title=" multi variables"> multi variables</a> </p> <a href="https://publications.waset.org/abstracts/36956/design-optimisation-of-a-novel-cross-vane-expander-compressor-unit-for-refrigeration-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36956.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">11093</span> Speed Control of DC Motor Using Optimization Techniques Based PID Controller </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Santosh%20Kumar%20Suman">Santosh Kumar Suman</a>, <a href="https://publications.waset.org/abstracts/search?q=Vinod%20Kumar%20Giri"> Vinod Kumar Giri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goal of this paper is to outline a speed controller of a DC motor by choice of a PID parameters utilizing genetic algorithms (GAs), the DC motor is extensively utilized as a part of numerous applications such as steel plants, electric trains, cranes and a great deal more. DC motor could be represented by a nonlinear model when nonlinearities such as attractive dissemination are considered. To provide effective control, nonlinearities and uncertainties in the model must be taken into account in the control design. The DC motor is considered as third order system. Objective of this paper three type of tuning techniques for PID parameter. In this paper, an independently energized DC motor utilizing MATLAB displaying, has been outlined whose velocity might be examined utilizing the Proportional, Integral, Derivative (KP, KI , KD) addition of the PID controller. Since, established controllers PID are neglecting to control the drive when weight parameters be likewise changed. The principle point of this paper is to dissect the execution of optimization techniques viz. The Genetic Algorithm (GA) for improve PID controllers parameters for velocity control of DC motor and list their points of interest over the traditional tuning strategies. The outcomes got from GA calculations were contrasted and that got from traditional technique. It was found that the optimization techniques beat customary tuning practices of ordinary PID controllers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DC%20motor" title="DC motor">DC motor</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20controller" title=" PID controller"> PID controller</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20techniques" title=" optimization techniques"> optimization techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm%20%28GA%29" title=" genetic algorithm (GA)"> genetic algorithm (GA)</a>, <a href="https://publications.waset.org/abstracts/search?q=objective%20function" title=" objective function"> objective function</a>, <a href="https://publications.waset.org/abstracts/search?q=IAE" title=" IAE"> IAE</a> </p> <a href="https://publications.waset.org/abstracts/48103/speed-control-of-dc-motor-using-optimization-techniques-based-pid-controller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48103.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">419</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">11092</span> Algorithm Development of Individual Lumped Parameter Modelling for Blood Circulatory System: An Optimization Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bao%20Li">Bao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Aike%20Qiao"> Aike Qiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaoyang%20Li"> Gaoyang Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Youjun%20Liu"> Youjun Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Lumped parameter model (LPM) is a common numerical model for hemodynamic calculation. LPM uses circuit elements to simulate the human blood circulatory system. Physiological indicators and characteristics can be acquired through the model. However, due to the different physiological indicators of each individual, parameters in LPM should be personalized in order for convincing calculated results, which can reflect the individual physiological information. This study aimed to develop an automatic and effective optimization method to personalize the parameters in LPM of the blood circulatory system, which is of great significance to the numerical simulation of individual hemodynamics. Methods: A closed-loop LPM of the human blood circulatory system that is applicable for most persons were established based on the anatomical structures and physiological parameters. The patient-specific physiological data of 5 volunteers were non-invasively collected as personalized objectives of individual LPM. In this study, the blood pressure and flow rate of heart, brain, and limbs were the main concerns. The collected systolic blood pressure, diastolic blood pressure, cardiac output, and heart rate were set as objective data, and the waveforms of carotid artery flow and ankle pressure were set as objective waveforms. Aiming at the collected data and waveforms, sensitivity analysis of each parameter in LPM was conducted to determine the sensitive parameters that have an obvious influence on the objectives. Simulated annealing was adopted to iteratively optimize the sensitive parameters, and the objective function during optimization was the root mean square error between the collected waveforms and data and simulated waveforms and data. Each parameter in LPM was optimized 500 times. Results: In this study, the sensitive parameters in LPM were optimized according to the collected data of 5 individuals. Results show a slight error between collected and simulated data. The average relative root mean square error of all optimization objectives of 5 samples were 2.21%, 3.59%, 4.75%, 4.24%, and 3.56%, respectively. Conclusions: Slight error demonstrated good effects of optimization. The individual modeling algorithm developed in this study can effectively achieve the individualization of LPM for the blood circulatory system. LPM with individual parameters can output the individual physiological indicators after optimization, which are applicable for the numerical simulation of patient-specific hemodynamics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blood%20circulatory%20system" title="blood circulatory system">blood circulatory system</a>, <a href="https://publications.waset.org/abstracts/search?q=individual%20physiological%20indicators" title=" individual physiological indicators"> individual physiological indicators</a>, <a href="https://publications.waset.org/abstracts/search?q=lumped%20parameter%20model" title=" lumped parameter model"> lumped parameter model</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20algorithm" title=" optimization algorithm"> optimization algorithm</a> </p> <a href="https://publications.waset.org/abstracts/110466/algorithm-development-of-individual-lumped-parameter-modelling-for-blood-circulatory-system-an-optimization-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110466.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">137</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">11091</span> Modeling Methodologies for Optimization and Decision Support on Coastal Transport Information System (Co.Tr.I.S.)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vassilios%20Moussas">Vassilios Moussas</a>, <a href="https://publications.waset.org/abstracts/search?q=Dimos%20N.%20Pantazis"> Dimos N. Pantazis</a>, <a href="https://publications.waset.org/abstracts/search?q=Panagioths%20Stratakis"> Panagioths Stratakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this paper is to present the optimization methodology developed in the frame of a Coastal Transport Information System. The system will be used for the effective design of coastal transportation lines and incorporates subsystems that implement models, tools and techniques that may support the design of improved networks. The role of the optimization and decision subsystem is to provide the user with better and optimal scenarios that will best fulfill any constrains, goals or requirements posed. The complexity of the problem and the large number of parameters and objectives involved led to the adoption of an evolutionary method (Genetic Algorithms). The problem model and the subsystem structure are presented in detail, and, its support for simulation is also discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coastal%20transport" title="coastal transport">coastal transport</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/29258/modeling-methodologies-for-optimization-and-decision-support-on-coastal-transport-information-system-cotris" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29258.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">499</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">11090</span> Multi-Criteria Test Case Selection Using Ant Colony Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niranjana%20Devi%20N.">Niranjana Devi N.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Test case selection is to select the subset of only the fit test cases and remove the unfit, ambiguous, redundant, unnecessary test cases which in turn improve the quality and reduce the cost of software testing. Test cases optimization is the problem of finding the best subset of test cases from a pool of the test cases to be audited. It will meet all the objectives of testing concurrently. But most of the research have evaluated the fitness of test cases only on single parameter fault detecting capability and optimize the test cases using a single objective. In the proposed approach, nine parameters are considered for test case selection and the best subset of parameters for test case selection is obtained using Interval Type-2 Fuzzy Rough Set. Test case selection is done in two stages. The first stage is the fuzzy entropy-based filtration technique, used for estimating and reducing the ambiguity in test case fitness evaluation and selection. The second stage is the ant colony optimization-based wrapper technique with a forward search strategy, employed to select test cases from the reduced test suite of the first stage. The results are evaluated using the Coverage parameters, Precision, Recall, F-Measure, APSC, APDC, and SSR. The experimental evaluation demonstrates that by this approach considerable computational effort can be avoided. <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=fuzzy%20entropy" title=" fuzzy entropy"> fuzzy entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20type-2%20fuzzy%20rough%20set" title=" interval type-2 fuzzy rough set"> interval type-2 fuzzy rough set</a>, <a href="https://publications.waset.org/abstracts/search?q=test%20case%20selection" title=" test case selection"> test case selection</a> </p> <a href="https://publications.waset.org/abstracts/30870/multi-criteria-test-case-selection-using-ant-colony-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30870.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">668</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">11089</span> Optimal Tuning of a Fuzzy Immune PID Parameters to Control a Delayed System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Gherbi">S. Gherbi</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Bouchareb"> F. Bouchareb</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the novel intelligent bio-inspired control strategies, it presents a novel approach based on an optimal fuzzy immune PID parameters tuning, it is a combination of a PID controller, inspired by the human immune mechanism with fuzzy logic. Such controller offers more possibilities to deal with the delayed systems control difficulties due to the delay term. Indeed, we use an optimization approach to tune the four parameters of the controller in addition to the fuzzy function; the obtained controller is implemented in a modified Smith predictor structure, which is well known that it is the most efficient to the control of delayed systems. The application of the presented approach to control a three tank delay system shows good performances and proves the efficiency of the method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=delayed%20systems" title="delayed systems">delayed systems</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20immune%20PID" title=" fuzzy immune PID"> fuzzy immune PID</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Smith%20predictor" title=" Smith predictor"> Smith predictor</a> </p> <a href="https://publications.waset.org/abstracts/10235/optimal-tuning-of-a-fuzzy-immune-pid-parameters-to-control-a-delayed-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10235.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">433</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">11088</span> Optimized Algorithm for Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuzhang%20Zhao">Fuzhang Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Particle swarm optimization (PSO) is becoming one of the most important swarm intelligent paradigms for solving global optimization problems. Although some progress has been made to improve PSO algorithms over the last two decades, additional work is still needed to balance parameters to achieve better numerical properties of accuracy, efficiency, and stability. In the optimal PSO algorithm, the optimal weightings of (√ 5 − 1)/2 and (3 − √5)/2 are used for the cognitive factor and the social factor, respectively. By the same token, the same optimal weightings have been applied for intensification searches and diversification searches, respectively. Perturbation and constriction effects are optimally balanced. Simulations of the de Jong, the Rosenbrock, and the Griewank functions show that the optimal PSO algorithm indeed achieves better numerical properties and outperforms the canonical PSO algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diversification%20search" title="diversification search">diversification search</a>, <a href="https://publications.waset.org/abstracts/search?q=intensification%20search" title=" intensification search"> intensification search</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20weighting" title=" optimal weighting"> optimal weighting</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/36390/optimized-algorithm-for-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36390.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">581</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">11087</span> Optimization Studies on Biosorption of Ni(II) and Cd(II) from Wastewater Using Pseudomonas putida in a Packed Bed Bioreactor </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.Narasimhulu">K.Narasimhulu</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Pydi%20Setty">Y. Pydi Setty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this present study is the optimization of process parameters in biosorption of Ni(II) and Cd(II) ions by Pseudomonas putida using Response Surface Methodology in a Packed bed bioreactor. The experimental data were also tested with theoretical models to find the best fit model. The present paper elucidates RSM as an efficient approach for predictive model building and optimization of Ni(II) and Cd(II) ions using Pseudomonas putida. In packed bed biosorption studies, comparison of the breakthrough curves of Ni(II) and Cd(II) for Agar immobilized and PAA immobilized Pseudomonas putida at optimum conditions of flow rate of 300 mL/h, initial metal ion concentration of 100 mg/L and bed height of 20 cm with weight of biosorbent of 12 g, it was found that the Agar immobilized Pseudomonas putida showed maximum percent biosorption and bed saturation occurred at 20 minutes. Optimization results of Ni(II) and Cd(II) by Pseudomonas putida from the Design Expert software were obtained as bed height of 19.93 cm, initial metal ion concentration of 103.85 mg/L, and flow rate of 310.57 mL/h. The percent biosorption of Ni(II) and Cd(II) is 87.2% and 88.2% respectively. The predicted optimized parameters are in agreement with the experimental results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=packed%20bed%20bioreactor" title="packed bed bioreactor">packed bed bioreactor</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface%20mthodology" title=" response surface mthodology"> response surface mthodology</a>, <a href="https://publications.waset.org/abstracts/search?q=pseudomonas%20putida" title=" pseudomonas putida"> pseudomonas putida</a>, <a href="https://publications.waset.org/abstracts/search?q=biosorption" title=" biosorption"> biosorption</a>, <a href="https://publications.waset.org/abstracts/search?q=waste%20water" title=" waste water"> waste water</a> </p> <a href="https://publications.waset.org/abstracts/16551/optimization-studies-on-biosorption-of-niii-and-cdii-from-wastewater-using-pseudomonas-putida-in-a-packed-bed-bioreactor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16551.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">452</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">11086</span> Reducing the Computational Overhead of Metaheuristics Parameterization with Exploratory Landscape Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Iannick%20Gagnon">Iannick Gagnon</a>, <a href="https://publications.waset.org/abstracts/search?q=Alain%20April"> Alain April</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The performance of a metaheuristic on a given problem class depends on the class itself and the choice of parameters. Parameter tuning is the most time-consuming phase of the optimization process after the main calculations and it often nullifies the speed advantage of metaheuristics over traditional optimization algorithms. Several off-the-shelf parameter tuning algorithms are available, but when the objective function is expensive to evaluate, these can be prohibitively expensive to use. This paper presents a surrogate-like method for finding adequate parameters using fitness landscape analysis on simple benchmark functions and real-world objective functions. The result is a simple compound similarity metric based on the empirical correlation coefficient and a measure of convexity. It is then used to find the best benchmark functions to serve as surrogates. The near-optimal parameter set is then found using fractional factorial design. The real-world problem of NACA airfoil lift coefficient maximization is used as a preliminary proof of concept. The overall aim of this research is to reduce the computational overhead of metaheuristics parameterization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=metaheuristics" title="metaheuristics">metaheuristics</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=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=exploratory%20landscape%20analysis" title=" exploratory landscape analysis"> exploratory landscape analysis</a> </p> <a href="https://publications.waset.org/abstracts/120306/reducing-the-computational-overhead-of-metaheuristics-parameterization-with-exploratory-landscape-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120306.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">11085</span> Comparative Analysis of Two Modeling Approaches for Optimizing Plate Heat Exchangers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F%C3%A1bio%20A.%20S.%20Mota">Fábio A. S. Mota</a>, <a href="https://publications.waset.org/abstracts/search?q=Mauro%20A.%20S.%20S.%20Ravagnani"> Mauro A. S. S. Ravagnani</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20P.%20Carvalho"> E. P. Carvalho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the present paper the design of plate heat exchangers is formulated as an optimization problem considering two mathematical modeling. The number of plates is the objective function to be minimized, considering implicitly some parameters configuration. Screening is the optimization method used to solve the problem. Thermal and hydraulic constraints are verified, not viable solutions are discarded and the method searches for the convergence to the optimum, case it exists. A case study is presented to test the applicability of the developed algorithm. Results show coherency with the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=plate%20heat%20exchanger" title="plate heat exchanger">plate heat exchanger</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/2716/comparative-analysis-of-two-modeling-approaches-for-optimizing-plate-heat-exchangers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2716.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">518</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">11084</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">11083</span> Parametric Influence and Optimization of Wire-EDM on Oil Hardened Non-Shrinking Steel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nixon%20Kuruvila">Nixon Kuruvila</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20V.%20Ravindra"> H. V. Ravindra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wire-cut Electro Discharge Machining (WEDM) is a special form of conventional EDM process in which electrode is a continuously moving conductive wire. The present study aims at determining parametric influence and optimum process parameters of Wire-EDM using Taguchi’s Technique and Genetic algorithm. The variation of the performance parameters with machining parameters was mathematically modeled by Regression analysis method. The objective functions are Dimensional Accuracy (DA) and Material Removal Rate (MRR). Experiments were designed as per Taguchi’s L16 Orthogonal Array (OA) where in Pulse-on duration, Pulse-off duration, Current, Bed-speed and Flushing rate have been considered as the important input parameters. The matrix experiments were conducted for the material Oil Hardened Non Shrinking Steel (OHNS) having the thickness of 40 mm. The results of the study reveals that among the machining parameters it is preferable to go in for lower pulse-off duration for achieving over all good performance. Regarding MRR, OHNS is to be eroded with medium pulse-off duration and higher flush rate. Finally, the validation exercise performed with the optimum levels of the process parameters. The results confirm the efficiency of the approach employed for optimization of process parameters in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dimensional%20accuracy%20%28DA%29" title="dimensional accuracy (DA)">dimensional accuracy (DA)</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20analysis%20%28RA%29" title=" regression analysis (RA)"> regression analysis (RA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi%20method%20%28TM%29" title=" Taguchi method (TM)"> Taguchi method (TM)</a>, <a href="https://publications.waset.org/abstracts/search?q=volumetric%20material%20removal%20rate%20%28VMRR%29" title=" volumetric material removal rate (VMRR)"> volumetric material removal rate (VMRR)</a> </p> <a href="https://publications.waset.org/abstracts/24005/parametric-influence-and-optimization-of-wire-edm-on-oil-hardened-non-shrinking-steel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24005.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">11082</span> A Lightweight Pretrained Encrypted Traffic Classification Method with Squeeze-and-Excitation Block and Sharpness-Aware Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhiyan%20Meng">Zhiyan Meng</a>, <a href="https://publications.waset.org/abstracts/search?q=Dan%20Liu"> Dan Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jintao%20Meng"> Jintao Meng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dependable encrypted traffic classification is crucial for improving cybersecurity and handling the growing amount of data. Large language models have shown that learning from large datasets can be effective, making pre-trained methods for encrypted traffic classification popular. However, attention-based pre-trained methods face two main issues: their large neural parameters are not suitable for low-computation environments like mobile devices and real-time applications, and they often overfit by getting stuck in local minima. To address these issues, we developed a lightweight transformer model, which reduces the computational parameters through lightweight vocabulary construction and Squeeze-and-Excitation Block. We use sharpness-aware optimization to avoid local minima during pre-training and capture temporal features with relative positional embeddings. Our approach keeps the model's classification accuracy high for downstream tasks. We conducted experiments on four datasets -USTC-TFC2016, VPN 2016, Tor 2016, and CICIOT 2022. Even with fewer than 18 million parameters, our method achieves classification results similar to methods with ten times as many parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sharpness-aware%20optimization" title="sharpness-aware optimization">sharpness-aware optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=encrypted%20traffic%20classification" title=" encrypted traffic classification"> encrypted traffic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=squeeze-and-excitation%20block" title=" squeeze-and-excitation block"> squeeze-and-excitation block</a>, <a href="https://publications.waset.org/abstracts/search?q=pretrained%20model" title=" pretrained model"> pretrained model</a> </p> <a href="https://publications.waset.org/abstracts/191101/a-lightweight-pretrained-encrypted-traffic-classification-method-with-squeeze-and-excitation-block-and-sharpness-aware-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191101.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">30</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">11081</span> An Optimization of Machine Parameters for Modified Horizontal Boring Tool Using Taguchi Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thirasak%20Panyaphirawat">Thirasak Panyaphirawat</a>, <a href="https://publications.waset.org/abstracts/search?q=Pairoj%20Sapsmarnwong"> Pairoj Sapsmarnwong</a>, <a href="https://publications.waset.org/abstracts/search?q=Teeratas%20Pornyungyuen"> Teeratas Pornyungyuen </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the findings of an experimental investigation of important machining parameters for the horizontal boring tool modified to mouth with a horizontal lathe machine to bore an overlength workpiece. In order to verify a usability of a modified tool, design of experiment based on Taguchi method is performed. The parameters investigated are spindle speed, feed rate, depth of cut and length of workpiece. Taguchi L9 orthogonal array is selected for four factors three level parameters in order to minimize surface roughness (Ra and Rz) of S45C steel tubes. Signal to noise ratio analysis and analysis of variance (ANOVA) is performed to study an effect of said parameters and to optimize the machine setting for best surface finish. The controlled factors with most effect are depth of cut, spindle speed, length of workpiece, and feed rate in order. The confirmation test is performed to test the optimal setting obtained from Taguchi method and the result is satisfactory. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=design%20of%20experiment" title="design of experiment">design of experiment</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi%20design" title=" Taguchi design"> Taguchi design</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=analysis%20of%20variance" title=" analysis of variance"> analysis of variance</a>, <a href="https://publications.waset.org/abstracts/search?q=machining%20parameters" title=" machining parameters"> machining parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=horizontal%20boring%20tool" title=" horizontal boring tool"> horizontal boring tool</a> </p> <a href="https://publications.waset.org/abstracts/12992/an-optimization-of-machine-parameters-for-modified-horizontal-boring-tool-using-taguchi-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12992.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">439</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">11080</span> Optimization of Machining Parameters by Using Cryogenic Media</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shafqat%20Wahab">Shafqat Wahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Waseem%20Tahir"> Waseem Tahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Manzoor%20Ahmad"> Manzoor Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarfraz%20Khan"> Sarfraz Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Azam"> M. Azam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optimization and analysis of tool flank wear width and surface finish of alloy steel rods are studied in the presence of cryogenic media (LN2) by using Tungsten Carbide Insert (CNMG 120404- WF 4215). Robust design concept of Taguchi L9(34) method and ANOVA is applied to determine the contribution of key cutting parameters and their optimum conditions. Through analysis, it revealed that cryogenic impact is more significant in reduction of the tool flank wear width while surface finish is mostly dependent on feed rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=turning" title="turning">turning</a>, <a href="https://publications.waset.org/abstracts/search?q=cryogenic%20fluid" title=" cryogenic fluid"> cryogenic fluid</a>, <a href="https://publications.waset.org/abstracts/search?q=liquid%20nitrogen" title=" liquid nitrogen"> liquid nitrogen</a>, <a href="https://publications.waset.org/abstracts/search?q=flank%20wear" title=" flank wear"> flank wear</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20roughness" title=" surface roughness"> surface roughness</a>, <a href="https://publications.waset.org/abstracts/search?q=taguchi" title=" taguchi"> taguchi</a> </p> <a href="https://publications.waset.org/abstracts/25627/optimization-of-machining-parameters-by-using-cryogenic-media" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25627.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">666</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">11079</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">11078</span> Modeling Studies on the Elevated Temperatures Formability of Tube Ends Using RSM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20J.%20Davidson">M. J. Davidson</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Selvaraj"> N. Selvaraj</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Venugopal"> L. Venugopal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The elevated temperature forming studies on the expansion of thin walled tubes have been studied in the present work. The influence of process parameters namely the die angle, the die ratio and the operating temperatures on the expansion of tube ends at elevated temperatures is carried out. The range of operating parameters have been identified by perfoming extensive simulation studies. The hot forming parameters have been evaluated for AA2014 alloy for performing the simulation studies. Experimental matrix has been developed from the feasible range got from the simulation results. The design of experiments is used for the optimization of process parameters. Response Surface Method’s (RSM) and Box-Behenken design (BBD) is used for developing the mathematical model for expansion. Analysis of variance (ANOVA) is used to analyze the influence of process parameters on the expansion of tube ends. The effect of various process combinations of expansion are analyzed through graphical representations. The developed model is found to be appropriate as the coefficient of determination value is very high and is equal to 0.9726. The predicted values are found to coincide well with the experimental results, within acceptable error limits. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expansion" title="expansion">expansion</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Response%20Surface%20Method%20%28RSM%29" title=" Response Surface Method (RSM)"> Response Surface Method (RSM)</a>, <a href="https://publications.waset.org/abstracts/search?q=ANOVA" title=" ANOVA"> ANOVA</a>, <a href="https://publications.waset.org/abstracts/search?q=bbd" title=" bbd"> bbd</a>, <a href="https://publications.waset.org/abstracts/search?q=residuals" title=" residuals"> residuals</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a>, <a href="https://publications.waset.org/abstracts/search?q=tube" title=" tube"> tube</a> </p> <a href="https://publications.waset.org/abstracts/21681/modeling-studies-on-the-elevated-temperatures-formability-of-tube-ends-using-rsm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21681.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">11077</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">11076</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">11075</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">11074</span> Fructooligosaccharide Prebiotics: Optimization of Different Cultivation Parameters on Their Microbial Production</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elsayed%20Ahmed%20Elsayed">Elsayed Ahmed Elsayed</a>, <a href="https://publications.waset.org/abstracts/search?q=Azza%20Noor%20El-Deen"> Azza Noor El-Deen</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20A.%20Farid"> Mohamed A. Farid</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20A.%20Wadaan"> Mohamed A. Wadaan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, a great attention has been paid to the use of dietary carbohydrates as prebiotic functional foods. Among the new commercially available products, fructooligosaccharides (FOS), which are microbial produced from sucrose, have attracted special interest due to their valuable properties and, thus, have a great economic potential for the sugar industrial branch. They are non-cariogenic sweeteners of low caloric value, as they are not hydrolyzed by the gastro-intestinal enzymes, promoting selectively the growth of the bifidobacteria in the colon, helping to eliminate the harmful microbial species to human and animal health and preventing colon cancer. FOS has been also found to reduce cholesterol, phospholipids and triglyceride levels in blood. FOS has been mainly produced by microbial fructosyltransferase (FTase) enzymes. The present work outlines bioprocess optimization for different cultivation parameters affecting the production of FTase by Penicillium aurantiogriseum AUMC 5605. The optimization involves both traditional as well as fractional factorial design approaches. Additionally, the production process will be compared under batch and fed-batch conditions. Finally, the optimized process conditions will be applied to 5-L stirred tank bioreactor cultivations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prebiotics" title="prebiotics">prebiotics</a>, <a href="https://publications.waset.org/abstracts/search?q=fructooligosaccharides" title=" fructooligosaccharides"> fructooligosaccharides</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=cultivation" title=" cultivation"> cultivation</a> </p> <a href="https://publications.waset.org/abstracts/3181/fructooligosaccharide-prebiotics-optimization-of-different-cultivation-parameters-on-their-microbial-production" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3181.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">387</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">11073</span> Optimization of Fermentation Parameters for Bioethanol Production from Waste Glycerol by Microwave Induced Mutant Escherichia coli EC-MW (ATCC 11105)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Refal%20Hussain">Refal Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Saifuddin%20M.%20Nomanbhay"> Saifuddin M. Nomanbhay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Glycerol is a valuable raw material for the production of industrially useful metabolites. Among many promising applications for the use of glycerol is its bioconversion to high value-added compounds, such as bioethanol through microbial fermentation. Bioethanol is an important industrial chemical with emerging potential as a biofuel to replace vanishing fossil fuels. The yield of liquid fuel in this process was greatly influenced by various parameters viz, temperature, pH, glycerol concentration, organic concentration, and agitation speed were considered. The present study was undertaken to investigate optimum parameters for bioethanol production from raw glycerol by immobilized mutant Escherichia coli (E.coli) (ATCC11505) strain on chitosan cross linked glutaraldehyde optimized by Taguchi statistical method in shake flasks. The initial parameters were set each at four levels and the orthogonal array layout of L16 (45) conducted. The important controlling parameters for optimized the operational fermentation was temperature 38 °C, medium pH 6.5, initial glycerol concentration (250 g/l), and organic source concentration (5 g/l). Fermentation with optimized parameters was carried out in a custom fabricated shake flask. The predicted value of bioethanol production under optimized conditions was (118.13 g/l). Immobilized cells are mainly used for economic benefits of continuous production or repeated use in continuous as well as in batch mode. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bioethanol" title="bioethanol">bioethanol</a>, <a href="https://publications.waset.org/abstracts/search?q=Escherichia%20coli" title=" Escherichia coli"> Escherichia coli</a>, <a href="https://publications.waset.org/abstracts/search?q=immobilization" title=" immobilization"> immobilization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/26809/optimization-of-fermentation-parameters-for-bioethanol-production-from-waste-glycerol-by-microwave-induced-mutant-escherichia-coli-ec-mw-atcc-11105" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26809.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">653</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">11072</span> Optimization of 3D Printing Parameters Using Machine Learning to Enhance Mechanical Properties in Fused Deposition Modeling (FDM) Technology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Darwin%20Junnior%20Sabino%20Diego">Darwin Junnior Sabino Diego</a>, <a href="https://publications.waset.org/abstracts/search?q=Brando%20Burgos%20Guerrero"> Brando Burgos Guerrero</a>, <a href="https://publications.waset.org/abstracts/search?q=Diego%20Arroyo%20Villanueva"> Diego Arroyo Villanueva</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Additive manufacturing, commonly known as 3D printing, has revolutionized modern manufacturing by enabling the agile creation of complex objects. However, challenges persist in the consistency and quality of printed parts, particularly in their mechanical properties. This study focuses on addressing these challenges through the optimization of printing parameters in FDM technology, using Machine Learning techniques. Our aim is to improve the mechanical properties of printed objects by optimizing parameters such as speed, temperature, and orientation. We implement a methodology that combines experimental data collection with Machine Learning algorithms to identify relationships between printing parameters and mechanical properties. The results demonstrate the potential of this methodology to enhance the quality and consistency of 3D printed products, with significant applications across various industrial fields. This research not only advances understanding of additive manufacturing but also opens new avenues for practical implementation in industrial settings. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20printing" title="3D printing">3D printing</a>, <a href="https://publications.waset.org/abstracts/search?q=additive%20manufacturing" title=" additive manufacturing"> additive manufacturing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=mechanical%20properties" title=" mechanical properties"> mechanical properties</a> </p> <a href="https://publications.waset.org/abstracts/185830/optimization-of-3d-printing-parameters-using-machine-learning-to-enhance-mechanical-properties-in-fused-deposition-modeling-fdm-technology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185830.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">51</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">11071</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">11070</span> Uncertainty and Optimization Analysis Using PETREL RE</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ankur%20Sachan">Ankur Sachan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ability to make quick yet intelligent and value-added decisions to develop new fields has always been of great significance. In situations where the capital expenses and subsurface risk are high, carefully analyzing the inherent uncertainties in the reservoir and how they impact the predicted hydrocarbon accumulation and production becomes a daunting task. The problem is compounded in offshore environments, especially in the presence of heavy oils and disconnected sands where the margin for error is small. Uncertainty refers to the degree to which the data set may be in error or stray from the predicted values. To understand and quantify the uncertainties in reservoir model is important when estimating the reserves. Uncertainty parameters can be geophysical, geological, petrophysical etc. Identification of these parameters is necessary to carry out the uncertainty analysis. With so many uncertainties working at different scales, it becomes essential to have a consistent and efficient way of incorporating them into our analysis. Ranking the uncertainties based on their impact on reserves helps to prioritize/ guide future data gathering and uncertainty reduction efforts. Assigning probabilistic ranges to key uncertainties also enables the computation of probabilistic reserves. With this in mind, this paper, with the help the uncertainty and optimization process in petrel RE shows how the most influential uncertainties can be determined efficiently and how much impact so they have on the reservoir model thus helping in determining a cost effective and accurate model of the reservoir. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title="uncertainty">uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=reservoir%20model" title=" reservoir model"> reservoir model</a>, <a href="https://publications.waset.org/abstracts/search?q=parameters" title=" parameters"> parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20analysis" title=" optimization analysis"> optimization analysis</a> </p> <a href="https://publications.waset.org/abstracts/21057/uncertainty-and-optimization-analysis-using-petrel-re" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21057.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">651</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">11069</span> Multi Objective Optimization for Two-Sided Assembly Line Balancing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srushti%20Bhatt">Srushti Bhatt</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20B.%20Kiran"> M. B. Kiran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two-sided assembly line balancing problem is yet to be addressed simply to compete for the global market for manufacturers. The task assigned in an ordered sequence to get optimum performance of the system is known as assembly line balancing problem mainly classified as single and two sided. It is very challenging in manufacturing industries to balance two-sided assembly line, wherein the set of sequential workstations the task operations are performed in two sides of the line. The conflicting major objective in two-sided assembly line balancing problem is either to maximize /minimize the performance parameters. The present study emphases on combining different evolutionary algorithm; ant colony, Tabu search and petri net method; and compares their results of an algorithm for solving two-sided assembly line balancing problem. The concept of multi objective optimization of performance parameters is now a day adopted to make a decision involving more than one objective function to be simultaneously optimized. The optimum result can be expected among the selected methods using multi-objective optimization. The performance parameters considered in the present study are a number of workstation, slickness and smoothness index. The simulation of the assembly line balancing problem provides optimal results of classical and practical problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ant%20colony" title="Ant colony">Ant colony</a>, <a href="https://publications.waset.org/abstracts/search?q=petri%20net" title=" petri net"> petri net</a>, <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=two%20sided%20ALBP" title=" two sided ALBP"> two sided ALBP</a> </p> <a href="https://publications.waset.org/abstracts/63766/multi-objective-optimization-for-two-sided-assembly-line-balancing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63766.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">278</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">11068</span> Design Parameters Optimization of a Gas Turbine with Exhaust Gas Recirculation: An Energy and Exergy Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joe%20Hachem">Joe Hachem</a>, <a href="https://publications.waset.org/abstracts/search?q=Marianne%20Cuif-Sjostrand"> Marianne Cuif-Sjostrand</a>, <a href="https://publications.waset.org/abstracts/search?q=Thierry%20Schuhler"> Thierry Schuhler</a>, <a href="https://publications.waset.org/abstracts/search?q=Dominique%20Orhon"> Dominique Orhon</a>, <a href="https://publications.waset.org/abstracts/search?q=Assaad%20Zoughaib"> Assaad Zoughaib</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The exhaust gas recirculation, EGR, implementation on gas turbines is increasingly gaining the attention of many researchers. This emerging technology presents many advantages, such as lowering the NOx emissions and facilitating post-combustion carbon capture as the carbon dioxide concentration in the cycle increases. As interesting as this technology may seem, the gas turbine, or its thermodynamic equivalent, the Brayton cycle, shows an intrinsic efficiency decrease with increasing EGR rate. In this paper, a thermodynamic model is presented to show the cycle efficiency decrease with EGR, alternative values of design parameters of both the pressure ratio (PR) and the turbine inlet temperature (TIT) are then proposed to optimize the cycle efficiency with different EGR rates. Results show that depending on the given EGR rate, both the design PR & TIT should be increased to compensate for the deficit in efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gas%20turbines" title="gas turbines">gas turbines</a>, <a href="https://publications.waset.org/abstracts/search?q=exhaust%20gas%20recirculation" title=" exhaust gas recirculation"> exhaust gas recirculation</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20parameters%20optimization" title=" design parameters optimization"> design parameters optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=thermodynamic%20approach" title=" thermodynamic approach"> thermodynamic approach</a> </p> <a href="https://publications.waset.org/abstracts/135673/design-parameters-optimization-of-a-gas-turbine-with-exhaust-gas-recirculation-an-energy-and-exergy-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135673.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">145</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">11067</span> Multi-Response Optimization of EDM for Ti-6Al-4V Using Taguchi-Grey Relational Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ritesh%20Joshi">Ritesh Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Kishan%20Fuse"> Kishan Fuse</a>, <a href="https://publications.waset.org/abstracts/search?q=Gopal%20Zinzala"> Gopal Zinzala</a>, <a href="https://publications.waset.org/abstracts/search?q=Nishit%20Nirmal"> Nishit Nirmal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ti-6Al-4V is a titanium alloy having high strength, low weight and corrosion resistant which is a required characteristic for a material to be used in aerospace industry. Titanium, being a hard alloy is difficult to the machine via conventional methods, so it is a call to use non-conventional processes. In present work, the effects on Ti-6Al-4V by drilling a hole of Ø 6 mm using copper (99%) electrode in Electric Discharge Machining (EDM) process is analyzed. Effect of various input parameters like peak current, pulse-on time and pulse-off time on output parameters viz material removal rate (MRR) and electrode wear rate (EWR) is studied. Multi-objective optimization technique Grey relational analysis is used for process optimization. Experiments are designed using an L9 orthogonal array. ANOVA is used for finding most contributing parameter followed by confirmation tests for validating the results. Improvement of 7.45% in gray relational grade is observed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANOVA" title="ANOVA">ANOVA</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20discharge%20machining" title=" electric discharge machining"> electric discharge machining</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20relational%20analysis" title=" grey relational analysis"> grey relational analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Ti-6Al-4V" title=" Ti-6Al-4V"> Ti-6Al-4V</a> </p> <a href="https://publications.waset.org/abstracts/69436/multi-response-optimization-of-edm-for-ti-6al-4v-using-taguchi-grey-relational-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69436.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">363</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">11066</span> Multi-Objective Optimization of Electric Discharge Machining for Inconel 718</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pushpendra%20S.%20Bharti">Pushpendra S. Bharti</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Maheshwari"> S. Maheshwari </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electric discharge machining (EDM) is one of the most widely used non-conventional manufacturing process to shape difficult-to-cut materials. The process yield, in terms of material removal rate, surface roughness and tool wear rate, of EDM may considerably be improved by selecting the optimal combination(s) of process parameters. This paper employs Multi-response signal-to-noise (MRSN) ratio technique to find the optimal combination(s) of the process parameters during EDM of Inconel 718. Three cases <em>v.i.z.</em> high cutting efficiency, high surface finish, and normal machining have been taken and the optimal combinations of input parameters have been obtained for each case. Analysis of variance (ANOVA) has been employed to find the dominant parameter(s) in all three cases. The experimental verification of the obtained results has also been made. MRSN ratio technique found to be a simple and effective multi-objective optimization technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electric%20discharge%20machining" title="electric discharge machining">electric discharge machining</a>, <a href="https://publications.waset.org/abstracts/search?q=material%20removal%20rate" title=" material removal rate"> material removal rate</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20roughness" title=" surface roughness"> surface roughness</a>, <a href="https://publications.waset.org/abstracts/search?q=too%20wear%20rate" title=" too wear rate"> too wear rate</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-response%20signal-to-noise%20ratio" title=" multi-response signal-to-noise ratio"> multi-response signal-to-noise ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20response%20signal-to-noise%20ratio" title=" multi response signal-to-noise ratio"> multi response signal-to-noise ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/48317/multi-objective-optimization-of-electric-discharge-machining-for-inconel-718" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48317.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">354</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">11065</span> The Optimization Process of Aortic Heart Valve Stent Geometry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arkadiusz%20Mezyk">Arkadiusz Mezyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Wojciech%20Klein"> Wojciech Klein</a>, <a href="https://publications.waset.org/abstracts/search?q=Mariusz%20Pawlak"> Mariusz Pawlak</a>, <a href="https://publications.waset.org/abstracts/search?q=Jacek%20Gnilka"> Jacek Gnilka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aortic heart valve stents should fulfill many criterions. These criteria have a strong impact on the geometrical shape of the stent. Usually, the final construction of stent is a result of many year experience and knowledge. Depending on patents claims, different stent shapes are produced by different companies. This causes difficulties for biomechanics engineers narrowing the domain of feasible solutions. The paper present optimization method for stent geometry defining by a specific analytical equation based on various mathematical functions. This formula was implemented as APDL script language in ANSYS finite element environment. For the purpose of simulation tests, a few parameters were separated from developed equation. The application of the genetic algorithms allows finding the best solution due to selected objective function. Obtained solution takes into account parameters such as radial force, compression ratio and coefficient of expansion on the transverse axial. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aortic%20stent" title="aortic stent">aortic stent</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20process" title=" optimization process"> optimization process</a>, <a href="https://publications.waset.org/abstracts/search?q=geometry" title=" geometry"> geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title=" finite element method"> finite element method</a> </p> <a href="https://publications.waset.org/abstracts/47096/the-optimization-process-of-aortic-heart-valve-stent-geometry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47096.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info 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