Multi-objective Analysis through Elite-induced Evolutionary Algorithm

• Shimizu Yoshiaki

  Department of Mechanical Engineering, Graduate School of Toyohashi University of Technology

• Takayama Masaki

  Department of Mechanical Engineering, Graduate School of Toyohashi University of Technology

• Ohishi Akihiro

  Department of Mechanical Engineering, Graduate School of Toyohashi University of Technology

Bibliographic Information

Other Title

• エリート牽引型進化法による多目的解析
• In the case of PSA
• PSA法を例として

Abstract

Under today's complicated and unstable society, it is becoming of special importance to make a rational decision from comprehensive point of view. Accordingly, many interests have been recently paid on the studies on multi-objective optimization. In this paper, we assert that it should be noted to distinguish the terminology between multi-objective optimization (MOP) and multi-objective analysis (MOA) from a rigid sense. Doing so, we can perform various tasks regarding MOA in more efficient manner. To promote such idea, we have first proposed a new method for MOA termed elite-induced MOEA that complementally combines conventional method and recent multi-objective evolutionary algorithm (MOEA). The former is applied to derive some Pareto optimal solutions as the elites while the latter to induce gradually the others targeting at the elites and dispersing on the Pareto front. Through this simple combination, the proposed method can derive the precise Pareto front and manipulate its distribution well. This provides a useful procedure for supporting decision making according to the preference of decision maker especially as a post-optimal analysis. These prospects are verified through numerical experiments.

Journal

• Transaction of the Japanese Society for Evolutionary Computation

  Transaction of the Japanese Society for Evolutionary Computation 3 (2), 22-30, 2012

  The Japanese Society for Evolutionary Computation

Keywords

• multi-objective analysis
• elite induction
• weighting method
• ε-constraint method
• PSA

Details

• CRID: 1390001205364405760
• NII Article ID: 130004566769
• DOI: 10.11394/tjpnsec.3.22
• ISSN: 21857385
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed