設計変数の適応的離散化を用いた実数値GAの効率的探索法

• 近藤 俊樹

  東京理科大学大学院工学研究科

• 立川 智章

  東京理科大学工学部

書誌事項

タイトル別名

• Efficient Search Method Using Adaptive Discretization of Design Variables on Real-Coded Genetic Algorithms

抄録

In this paper, we propose a new adaptive discretization method of design variables on real-coded genetic algorithms(RCGAs) for improving convergence performance while maintaining diversity.The convergence can be accelerated by setting the appropriate number of discrete classes in RCGAs. However, it is difficult to decide it in advance in most of the practical optimization problems.In addition, the diversity may be lost if the number of discrete classes is too small.In order to overcome these difficulties, we use a simple index which is based on the standard deviation to adaptively determine the number of discrete classes in each design variable.Since the proposed method merely rounds the value of the design variable after applying genetic operators such as crossover and mutation, it can be applied to various RCGAs.Here, we use NSGA-II as an RCGA and investigate the performance efficiency of convergence and diversity by using nineteen benchmark problems, including engineering problems.The convergence and diversity performance are evaluated using GD and IGD, respectively.The results of the numerical experiments show that the proposed method can obtain good convergence while maintaining diversity.

収録刊行物

• 進化計算学会論文誌

  進化計算学会論文誌 8 (3), 88-99, 2018

  進化計算学会

キーワード

• multiobjective optimization
• real-coded genetic algorithms
• discretization

詳細情報

• CRID: 1390001205365240192
• NII論文ID: 130006342157
• DOI: 10.11394/tjpnsec.8.88
• ISSN: 21857385
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可