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
Publisher
Pergamon Press
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.
Access to external full text or publisher's version may require subscription.
Full-text Version
Accepted Author Manuscript
Language
English
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