Adaptive Cross-Generation Differential Evolution Operators for Multiobjective Optimization

Research output: Research - peer-review21_Publication in refereed journal

16 Scopus Citations
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Original languageEnglish
Article number7138611
Pages (from-to)232-244
Journal / PublicationIEEE Transactions on Evolutionary Computation
Issue number2
StatePublished - 1 Apr 2016
Externally publishedYes


Convergence performance and parametric sensitivity are two issues that tend to be neglected when extending differential evolution (DE) to multiobjective optimization (MO). To fill this research gap, we develop two novel mutation operators and a new parameter adaptation mechanism. A multiobjective DE variant is obtained through integration of the proposed strategies. The main innovation of this paper is the simultaneous use of individuals across generations from an objective-based perspective. Good convergence-diversity tradeoff and satisfactory exploration-exploitation balance are achieved via the hybrid cross-generation mutation operation. Furthermore, the cross-generation adaptation mechanism enables the individuals to self-adapt their associated parameters not only optimization-stage-wise but also objective-space-wise. Empirical results indicate the statistical superiority of the proposed algorithm over several state-of - The-art evolutionary algorithms in handling MO problems.

Research Area(s)

  • Differential evolution, multi-objective optimization, mutation operation