Document Type

Journal article

Source Publication

Expert Systems With Applications

Publication Date

12-1-2010

Volume

37

Issue

12

First Page

8462

Last Page

8470

Keywords

Evolutionary computation, multiobjective evolutionary algorithms, diversified archiving, convergence, ϵ-Pareto set

Abstract

It is crucial to obtain automatically and efficiently a well-distributed set of Pareto optimal solutions in multiobjective evolutionary algorithms (MOEAs). Many studies have proposed different evolutionary algorithms that can progress toward the Pareto front with a widely spread distribution of solutions. However, most theoretically, convergent MOEAs necessitate certain prior knowledge about the Pareto front in order to efficiently maintain widespread solutions. In this paper, we propose, based on the new E-dominance concept, an Adaptive Rectangle Archiving (ARA) strategy that overcomes this important problem. The MOEA with this archiving technique provably converges to well-distributed Pareto optimal solutions without prior knowledge about the Pareto front. ARA complements the existing archiving techniques and is useful to both researchers and practitioners.

DOI

10.1016/j.eswa.2010.05.032

Print ISSN

09574174

E-ISSN

18736793

Publisher Statement

Copyright © 2010 Elsevier Ltd.

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Full-text Version

Accepted Author Manuscript

Recommended Citation

Jin, H., & Wong, M. L. (2010). Adaptive, convergent, and diversified archiving strategy for multiobjective evolutionary algorithms. Expert Systems with Applications, 37(12), 8462-8470. doi: 10.1016/j.eswa.2010.05.032