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- A Hybrid Evolutionary Approach with Search Strategy Adaptation for Mutiobjective Optimization doi link

Auteur(s): Kafafy A.(Corresp.), Bonnevay S., Bounekkar A.

Conference: Genetic and Evolutionary Computation Conference (Amsterdam, NL, 2013-07-06)
Actes de conférence: Genetic and Evolutionary Computation Conference, vol. p.631-638 (2013)

DOI: 10.1145/2463372.2463458

Hybrid evolutionary algorithms have been successfully applied to solve numerous multiobjective optimization problems (MOP). In this paper, a new hybrid evolutionary approach based on search strategy adaptation (HESSA) is presented. In HESSA, the search process is carried out through adopting a pool of di erent search strategies, each of which has a speci ed success ratio. A new o spring is generated using a randomly selected strategy. Then, according to the success of the generated o spring to update the population or the archive, the success ratio of the selected strategy is adapted. This provides the ability for HESSA to adopt the appropriate search strategy according to the problem on hand. Furthermore, the cooperation among di erent strategies leads to improve the exploration and the exploitation of the search space. The proposed pool is combined to a suitable evolutionary framework for supporting the integration and cooperation. Moreover, the ecient solutions explored over the search are collected in an external repository to be used as global guides. The proposed HESSA is veri ed against some of the state of the art MOEAs using a set of test problems commonly used in the literature. The experimental results indicate that HESSA is highly competitive and can be considered as a viable alternative