Simultaneous concept-based evolutionary multi-objective optimization

Gideon Avigad, Amiram Moshaiov*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In contrast to traditional multi-objective problems the concept-based version of such problems involves sets of particular solutions, which represent predefined conceptual solutions. This paper addresses the concept-based multi-objective problem by proposing two novel multi objective evolutionary algorithms. It also compares two major search approaches.The suggested algorithms deal with resource sharing among concepts, and within each concept, while simultaneously evolving concepts towards a Pareto front by way of their representing sets. The introduced algorithms, which use a simultaneous search approach, are compared with a sequential one. For this purpose concept-based performance indicators are suggested and used. The comparison study includes both the computational time and the quality of the concept-based front representation. Finally, the effect on the computational time of both the concept fitness evaluation time and concept optimality, for both the sequential and simultaneous approaches, is highlighted.

Original languageEnglish
Pages (from-to)193-207
Number of pages15
JournalApplied Soft Computing Journal
Volume11
Issue number1
DOIs
StatePublished - Jan 2011

Keywords

  • Co-evolution
  • Competing sub-populations
  • Conceptual design
  • Crowding
  • Elitism
  • Engineering design
  • Fitness sharing
  • Multi-objective optimization
  • Niching
  • Parallel GA
  • Species

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