Proposal of Functional-Specialization Multi-Objective Real-Coded Genetic Algorithm: FS-MOGA

DOI Web Site 1 Citations 11 References
  • Hamada Naoki
    Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
  • Tanaka Masaharu
    Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
  • Sakuma Jun
    Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
  • Kobayashi Shigenobu
    Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
  • Ono Isao
    Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

Bibliographic Information

Other Title
  • 機能分担多目的実数値ＧＡ：　ＦＳ－ＭＯＧＡの提案

Abstract

This paper presents a Genetic Algorithm (GA) for multi-objective function optimization. To find a precise and widely-distributed set of solutions in difficult multi-objective function optimization problems which have multimodality and curved Pareto-optimal set, a GA would be required conflicting behaviors in the early stage and the last stage of search. That is, in the early stage of search, GA should perform local-Pareto-optima-overcoming search which aims to overcome local Pareto-optima and converge the population to promising areas in the decision variable space. On the other hand, in the last stage of search, GA should perform Pareto-frontier-covering search which aims to spread the population along the Pareto-optimal set. NSGA-II and SPEA2, the most widely used conventional methods, have problems in local-Pareto-optima-overcoming and Pareto-frontier-covering search. In local-Pareto-optima-overcoming search, their selection pressure is too high to maintain the diversity for overcoming local Pareto-optima. In Pareto-frontier-covering search, their abilities of extrapolation-directed sampling are not enough to spread the population and they cannot sample along the Pareto-optimal set properly. To resolve above problems, the proposed method adaptively switches two search strategies, each of which is specialized for local-Pareto-optima-overcoming and Pareto-frontier-covering search, respectively. We examine the effectiveness of the proposed method using two benchmark problems. The experimental results show that our approach outperforms the conventional methods in terms of both local-Pareto-optima-overcoming and Pareto-frontier-covering search.

Journal

Citations (1)*help

See more

References(11)*help

See more

Related Projects

See more

Keywords

Details 詳細情報について

Report a problem

Back to top