ROUGH SESTS-INSPIRED EVOLUTIONARY ALGORITHM FOR ENGINEERING MULTIOBJECTIVE OPTIMIZATION PROBLEMS

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Published: May 1, 2014

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mohamed abdelsameea hussein
Department of Basic Engineering Sciences, Faculty of Engineering, Menoufia University, Shibin El-Kom, Egypt

Abstract


In this paper we present a new optimization algorithm, the proposed algorithm operates in two phases: in the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of  ε -dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for engineering optimization problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable.

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hussein, mohamed abdelsameea. (2014). ROUGH SESTS-INSPIRED EVOLUTIONARY ALGORITHM FOR ENGINEERING MULTIOBJECTIVE OPTIMIZATION PROBLEMS. Journal of Global Research in Mathematical Archives(JGRMA), 1(12), 21–30. Retrieved from https://jgrma.com/index.php/jgrma/article/view/140
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Vol. 1 No. 12 (2013): December-2013
Section
Research Paper
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