Opposition-based learning for self-adaptive control parameters in differential evolution for optimal mechanism design

Tam Bui, Trung Nguyen, Hiroshi Hasegawa

研究成果: Article査読

3 被引用数 (Scopus)


In recent decades, new optimization algorithms have attracted much attention from researchers in both gradient- and evolution-based optimal methods. Many strategy techniques are employed to enhance the effectiveness of optimal methods. One of the newest techniques is opposition-based learning (OBL), which shows more power in enhancing various optimization methods. This research presents a new edition of the Differential Evolution (DE) algorithm in which the OBL technique is applied to investigate the opposite point of each candidate of self-adaptive control parameters. In comparison with conventional optimal methods, the proposed method is used to solve benchmark-test optimal problems and applied to real optimizations. Simulation results show the effectiveness and improvement compared with some reference methodologies in terms of the convergence speed and stability of optimal results.

ジャーナルJournal of Advanced Mechanical Design, Systems and Manufacturing
出版ステータスPublished - 2019

ASJC Scopus subject areas

  • 機械工学
  • 産業および生産工学