Hybrid optimization technique coupling an evolutionary algorithm and chaotic local search

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Published: Jul 11, 2014

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abd allah A. mousa
taif university,faculty of science, mathematics department

Abstract

Evolutionary algorithms (EAs) are randomized heuristics for search and optimization that are based on principles derived from natural evolution. Mutation, recombination, and selection are iterated with the goal of driving a population of candidate solutions toward better and better regions of the search space. Since the underlying idea is easy to grasp and almost no information about the problem to be optimized is necessary in order to apply it, EAs are widely used in many practical disciplines, mainly in computer science and engineering. However, they are time consuming algorithms that are unpractical from the industrial viewpoint, and they are very poor in terms of Global convergence performance. On the other hand, local search algorithms can converge quickly to these local minima and get stuck in a local optimum solution. In this paper, chaotic local search is proposed as a neighborhood search engine to improve the solution quality, where it intends to explore the less-crowded area in the current archive to possibly obtain more nondominated solutions. Finally, various kinds of multiobjective (MO) benchmark problems have been reported to stress the importance of hybridization algorithms in generating Pareto optimal sets for multiobjective optimization problems.

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mousa, abd allah A. (2014). Hybrid optimization technique coupling an evolutionary algorithm and chaotic local search. Journal of Global Research in Mathematical Archives(JGRMA), 2(1), 01–10. Retrieved from https://jgrma.com/index.php/jgrma/article/view/149
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Vol. 2 No. 1 (2014): January-2014
Section
Research Paper
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