Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm

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 number7160727
Pages (from-to)475-480
Journal / PublicationIEEE Transactions on Evolutionary Computation
Volume20
Issue number3
StatePublished - 1 Jun 2016

Abstract

A decomposition approach decomposes a multiobjective optimization problem into a number of scalar objective optimization subproblems. It plays a key role in decomposition-based multiobjective evolutionary algorithms. However, many widely used decomposition approaches, originally proposed for mathematical programming algorithms, may not be very suitable for evolutionary algorithms. To help decomposition-based multiobjective evolutionary algorithms balance the population diversity and convergence in an appropriate manner, this letter proposes to impose some constraints on the subproblems. Experiments have been conducted to demonstrate that our proposed constrained decomposition approach works well on most test instances. We further propose a strategy for adaptively adjusting constraints by using information collected from the search. Experimental results show that it can significantly improve the algorithm performance.

Research Area(s)

  • Constraint, decomposition approach, evolutionary multiobjective optimization

Citation Format(s)

Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm. / Wang, Luping; Zhang, Qingfu; Zhou, Aimin; Gong, Maoguo; Jiao, Licheng.

In: IEEE Transactions on Evolutionary Computation, Vol. 20, No. 3, 7160727, 01.06.2016, p. 475-480.

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