Dynamic archive evolution strategy for multiobjective optimization

Authors:
Yang Shu Min;Shao Dong Guo;Luo Yang Jie
Affiliations:
State Key Laboratory of Water Resource & Hydropower Engineering Science, Wuhan University, Wuhan, Hubei, P. R. China;State Key Laboratory of Water Resource & Hydropower Engineering Science, Wuhan University, Wuhan, Hubei, P. R. China;College of Urban & Environmental Sciences, Northeast Normal University, Changchun, Jilin, P.R. China
Venue:
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Year:
2005

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Abstract

This paper proposes a new multiobjective evolutionary approach—the dynamic archive evolution strategy (DAES) to investigate the adaptive balance between proximity and diversity. In DAES, a novel dynamic external archive is proposed to store elitist individuals as well as relatively better individuals through archive increase scheme and archive decrease scheme. Additionally, a combinatorial operator that inherits merits from Gaussian mutation of proximity exploration and Cauchy mutation of diversity preservation is elaborately devised. Meanwhile, a complete nondominance selection ensures maximal pressure of proximity exploitation while a corresponding fitness assignment ensures the similar pressure of diversity preservation. By graphical presentation and performance metrics on three prominent benchmark functions, DAES is found to outperform three state-of-the-art multiobjective evolutionary algorithms to some extent in terms of finding a near-optimal, well-extended and uniformly diversified Pareto optimal front.