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Adaptive, convergent, and diversified archiving strategy for multiobjective evolutionary algorithms
Expert Systems With Applications
  • Huidong JIN, CSIRO Mathematics, Informatics and Statistics, Australia
  • Man Leung WONG, Lingnan University, Hong Kong
Document Type
Journal article
Publication Date
Pergamon Press
  • Evolutionary computation,
  • multiobjective evolutionary algorithms,
  • diversified archiving,
  • convergence,
  • ϵ-Pareto set

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.

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Copyright © 2010 Elsevier Ltd.

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Citation Information
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