Abstract
Hybrid evolutionary metaheuristics tend to enhance search capabilities, by improving intensification and diversification, through incorporating different cooperative metaheuristics. In this paper, an improved version of the Hybrid Evolutionary Metaheuristics (HEMH) [7] is presented. Unlike HEMH, HEMH2 uses simple inverse greedy algorithm to construct its initial population. Then, the search efforts are directed to improve these solutions by exploring the search space using binary differential evolution. After a certain number of evaluations, path relinking is applied on high quality solutions to investigate the non-visited regions in the search space. During evaluations, the dynamic-sized neighborhood structure is adopted to shrink/extend the mating/updating range. Furthermore, the Pareto adaptive epsilon concept is used to control the archiving process with preserving the extreme solutions. HEMH2 is verified against its predecessor HEMH and the MOEA/D [13], using a set of MOKSP instances from the literature. The experimental results indicate that the HEMH2 is highly competitive and can achieve better results.
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Kafafy, A., Bounekkar, A., Bonnevay, S. (2012). HEMH2: An Improved Hybrid Evolutionary Metaheuristics for 0/1 Multiobjective Knapsack Problems. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_11
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DOI: https://doi.org/10.1007/978-3-642-34859-4_11
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