A dynamic polynomial mutation for evolutionary multi-objective optimization algorithms

Research output: Contribution to journalArticle

Abstract

Polynomial mutation is widely used in evolutionary optimization algorithms as a variation operator. In previous work on the use of evolutionary algorithms for solving multi-objective problems, two versions of polynomial mutations were introduced. The first is non-highly disruptive that is not prone to local optima and the second is highly disruptive polynomial mutation. This paper looks at the two variants and proposes a dynamic version of polynomial mutation. The experimental results show that the proposed adaptive algorithm is doing well for three evolutionary multiobjective algorithms on well known multiobjective optimization problems in terms of convergence speed, generational distance and hypervolume performance metrics.

Original languageEnglish
Pages (from-to)209-219
Number of pages11
JournalInternational Journal on Artificial Intelligence Tools
Volume20
Issue number1
DOIs
Publication statusPublished - 1 Feb 2011

Keywords

  • evolutionary algorithms
  • Multi-objective optimization
  • mutation

ASJC Scopus subject areas

  • Artificial Intelligence

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