Abstract: Hybrid evolutionary metaheuristics tend to enhance search capabilities, by improving intensication and diversication, through incorporating dierent 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 eorts is 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 verified 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.

Abstract: Hybrid Metaheuristics aim to incorporate and combine different metaheuristics with each other to enhance the search capabilities. It can improve both of intensification and diversification toward the preferred solutions and concentrates the search efforts to investigate the promising regions in the search space. In this paper, a comparative study was developed to study the effect of the hybridization of different metaheuristics within MOEA/D framework. We study four proposals of hybridization, the first proposal is to combine adaptive discrete differential evolution operator with MOEA/D. The second one is to combine the path-Relinking operator with MOEA/D. the third and the fourth proposals combine both of them in MOEA/D. The comparative study uses a set of MOKSP instances commonly used in the literature to investigate the hybridization effects as well as a set of quality assessment indicators. The experimental results indicate that the proposals are highly competitive for most test instances and can be considered as viable alternatives.

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Ref HAL: hal-00624096_v1
Abstract: Handling Multiobjective Optimization Problems (MOOP) using Hybrid Metaheuristics represents a promising and interest area of research. In this paper, a Hybrid Evolutionary Metaheuristics (HEMH) is presented. It combines different metaheuristics integrated with each other to enhance the search capabilities. It improves both of intensification and diversification toward the preferred solutions and concentrates the search efforts to investigate the promising regions in the search space. In the proposed HEMH, the search process is divided into two phases. In the first one, the DM-GRASP is applied to obtain an initial set of high quality solutions dispersed along the Pareto front. Then, the search efforts are intensified on the promising regions around these solutions through the second phase. The greedy randomized path-relinking with local search or reproduction operators are applied to improve the quality and to guide the search to explore the non discovered regions in the search space. The two phases are combined with a suitable evolutionary framework supporting the integration and cooperation. Moreover, the efficient solutions explored over the search are collected in an external archive. The HEMH is verified and tested against some of the state of the art MOEAs using a set of MOKSP instances commonly used in the literature. The experimental results indicate that the HEMH is highly competitive and can be considered as a viable alternative.

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