Entropy-Based Termination Criterion for Multiobjective Evolutionary Algorithms

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journal

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Detail(s)

Original languageEnglish
Article number7273886
Pages (from-to)485-498
Journal / PublicationIEEE Transactions on Evolutionary Computation
Volume20
Issue number4
StatePublished - 1 Aug 2016

Abstract

Multiobjective evolutionary algorithms evolve a population of solutions through successive generations toward the Pareto-optimal front (POF). One of the most critical questions faced by the researchers and practitioners in this domain relates to the number of generations that may be sufficient for an algorithm to offer a good approximation of the POF for a given problem. Ironically, to date, this question largely remains unanswered and the number of generations are arbitrarily fixed a priori, with potentially punitive implications. If the a priori fixed generations are insufficient, then the algorithm reports suboptimal solutions. In contrast, if the a priori fixed generations are far too many, it implies waste of computational resources. This paper proposes a novel entropy-based dissimilarity measure that helps identify on the fly the number of generations beyond which an algorithm stabilizes, implying that either a good approximation has been obtained or that it cannot be obtained due to the stagnation of the algorithm in the search space. Given that in either case no further improvement in the approximation can be obtained, despite additional computational expense, the proposed dissimilarity measure provides a termination criterion and facilitates a termination detection algorithm. The generality, on-the-fly implementation, low-computational complexity, and the demonstrated efficacy of the proposed termination detection algorithm, on a wide range of multiobjective and many-objective test problems, define the novel contribution of this paper.

Research Area(s)

  • Entropy, evolutionary multiobjective optimization, many-objective optimization, termination detection algorithm

Citation Format(s)

Entropy-Based Termination Criterion for Multiobjective Evolutionary Algorithms. / Saxena, Dhish Kumar; Sinha, Arnab; Duro, João A.; Zhang, Qingfu.

In: IEEE Transactions on Evolutionary Computation, Vol. 20, No. 4, 7273886, 01.08.2016, p. 485-498.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journal