A hybrid evolutionary algorithm based on alopex and estimation of distribution algorithm and its application for optimization

Authors:
Shaojun Li;Fei Li;Zhenzhen Mei
Affiliations:
Institute of Automation, East China University of Science and Technology, Shanghai, P.R China;Institute of Automation, East China University of Science and Technology, Shanghai, P.R China;Institute of Automation, East China University of Science and Technology, Shanghai, P.R China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2010

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Abstract

Alopex is a correlation-based algorithm, which shares characteristics of both gradient descent approach and simulated annealing It has been successfully applied to continuous and combinatorial optimization problems for years Estimation of Distribution Algorithms (EDAs) is a class of novel evolutionary algorithms (EAs) proposed in recent years Compared with the traditional EAs, it possesses unique evolutionary characteristics In this paper, a hybrid evolutionary algorithm (EDA-Alopex) is proposed, which integrates the merits of both Alopex and EDA, and obtains more evolutionary information than these two approaches The new algorithm is tested with several benchmark functions; numerical case study results demonstrate that EDA-Alopex outperforms both EDA and AEA, especially for the complex multi-modal functions Finally, the proposed algorithm is investigated on high-dimensional and multi-peaks benchmark functions, and it also achieves satisfactory results.