A multi-objective evolutionary algorithm using min-max strategy and sphere coordinate transformation

Hai Lin Liu, Yuping Wang, Yiu Ming CHEUNG

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)

Abstract

Multi-objective evolutionary algorithms using the weighted sum of the objectives as the fitness functions feature simple execution and effectiveness in multi-objective optimization. However, they cannot fmd the Pareto solutions on the non-convex part of the Pareto frontier, and thus aze difficult to find evenly distributed solutions. Under the circumstances, this paper proposes anew evolutionary algorithm using multiple fitness functions. Although the weights generated via the sphere coordinate transformation and uniform design are used to define the fitness, the fitness is not defined by the weighted sum of the objectives. Instead, it is defined by the maximum value of the weighted normalized objectives using amin-max strategy. In this manner, the proposed algorithm can overcome the drawbacks of the algorithms using the weighted sum of the objectives, and explore the objective space to fmd approximate uniformly distributed solutions on the Pareto front gradually. The numerical simulations show the proposed algorithm outperforms the existing ones.

Original languageEnglish
Pages (from-to)361-384
Number of pages24
JournalIntelligent Automation and Soft Computing
Volume15
Issue number3
DOIs
Publication statusPublished - Jan 2009

Scopus Subject Areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

User-Defined Keywords

  • Evolutionary Algorithrn
  • Min-Max Strategy
  • Multi-Objective Optimization
  • Sphere Coordinate Transformation
  • Uniform Design

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