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Comparison between Multiobjective Population-Based Algorithms in Mechanical Problem

Radhi, H.E. and Barrans, Simon (2011) Comparison between Multiobjective Population-Based Algorithms in Mechanical Problem. Applied Mechanics and Materials, 110-16. pp. 2383-2389. ISSN 1662-7482

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The objective of this paper was to perform a comparative study among multiobjective optimization methods on practical problem by using modeFRONTIER optimization software, to determine the efficiency of each method. In order to measure the effectiveness and competence of each method, the lifting arm problem was chosen from the literature [1]. Two numerical performance metrics and one visual criterion were chosen for qualitative and quantitative comparisons:(1) the variance of solution distribution in the Pareto optimal regions, (2) the ratio between the number of resulting Pareto front members to total numbers fitness function calculations which is denoted by hit rate [2], and lastly (3) graphical representation of the Pareto fronts for discussion. These metrics were chosen to represent the quality, as well as speed of the algorithms by ensuring well extends solutions. The definition of the variance as the sum of the square difference between the distance of each Pareto solutions and the average distance between Pareto solutions, over the total number of Pareto solutions. Comparisons among the results obtained using different algorithms have been performed to verify their performance. The experiments carried out indicate that FMOGA-II obtains remarkable results regarding all metrics used.

Item Type: Article
Uncontrolled Keywords: Multi-objective optimization, Genetic algorithms, MOSA, MOPSO
Subjects: T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Centre for Precision Technologies > Engineering Control and Machine Performance Research Group
School of Computing and Engineering > Pedagogical Research Group
School of Computing and Engineering > Turbocharger Research Institute
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References: 1- E. Gurbal: Un weighted Multi-criteria Mesh and Structural Optimization Method with Finite Element Analysis, Ph. D. Huddersfiels (2003). 2- V. Sedenka, Z. Raida: Critical Comparison of Multi-objective Optimization Methods: Genetic Algorithms versus Swarm Intelligence. Radioengineering Vol 19 (2010), p. 369-377. 3- K. Deeb: Multi-objective optimization using Evolutionary Algorithms. John Wiley and Sons (2001). 4- K. Deeb, A. Partap, S. Agarwal and T. Meyarivan: A Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, Vol 6 (2002), p. 182-197 5- modeFRONTIER v4.0 User manual. ESTECO srl, Trieste, Italy (2008). 6- P. Silvia: MOGA-II An improved Multi-Objective Genetic Algorithm, ESTECO Technical Report (2003). 7- S. Daisuke: ARMOGA An efficient Multi-Objective Genetic Algorithm, Technical Report (2005) 8- K. Deeb, S. Agarwal, A. Partap and T. Meyarivan: A Fast Elitist Non Dominated Sorting Genetic Algorithm for Multi-Objective Optimization NSGA-II, Indian Institute of Technology (2000) 9- M. Sanaz: Multi-Objective Evolutionary Algorithm: Data structures, Convergence and Diversity, Sharker Verlag, Ph D Thesis (2004). 10- A. Suppapinarm, K. A. Seffen and G. T. Parks: A Simulated Annealing Algorithm for a Multiobjective Optimization , Engineering Optimization, Vol. 33, p. 59-85 11- M. Gunzburer, J. Burkdart: Uniformity measures for point samples in hypercubes (2004)
Depositing User: Simon Barrans
Date Deposited: 25 Feb 2014 12:30
Last Modified: 28 Aug 2021 11:35


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