Distributed multi-objective GA for generating comprehensive Pareto front in deceptive optimization problems

Shin Ando, Einoshin Suzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

This paper discusses a structure of multi-objective optimization problems, which cause deception for conventional Multi-Objective Genetic Algorithms (MOGAs). Further, we propose a Distributed Multi-Objective Genetic Algorithm (DMOGA), which employs a multiple subpopulation implementation and a replacement scheme based on the information theoretic entropy, to improve the performance of MOGA in such deceptive problems. Several studies have reported that the conventional MOGAs' have difficulties in generating marginal segments of the Pareto front in a combinatorial optimization problems, though structural causes of their behaviors have not yet been thoroughly studied. Our analysis of the conventional MOGAs' behaviors in two test deceptive problems suggests that the use of the local density in the selection causes an implicit bias which results in a premature convergence. DMOGA is a distributed implementation of MOGA, which emphasizes the diversity of the subpopulations by the entropy of the objective functions. This approach alleviates the premature convergence and enables MOGA to effectively generate Pareto fronts for complex objective functions. In a set of simulated experiments, the proposed method generated more comprehensive Pareto fronts than the conventional MOGAs, i.e., NSGA-II and SPEA2 in the deceptive test functions, and also achieved comparable performance in the standard multi-objective benchmarks.

Original languageEnglish
Title of host publication2006 IEEE Congress on Evolutionary Computation, CEC 2006
Pages1569-1576
Number of pages8
Publication statusPublished - Dec 1 2006
Externally publishedYes
Event2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada
Duration: Jul 16 2006Jul 21 2006

Publication series

Name2006 IEEE Congress on Evolutionary Computation, CEC 2006

Other

Other2006 IEEE Congress on Evolutionary Computation, CEC 2006
CountryCanada
CityVancouver, BC
Period7/16/067/21/06

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

Cite this

Ando, S., & Suzuki, E. (2006). Distributed multi-objective GA for generating comprehensive Pareto front in deceptive optimization problems. In 2006 IEEE Congress on Evolutionary Computation, CEC 2006 (pp. 1569-1576). [1688495] (2006 IEEE Congress on Evolutionary Computation, CEC 2006).