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

Bui, T, Nguyen, TT and Hasegawa, H (2019) Opposition-based learning for self-adaptive control parameters in differential evolution for optimal mechanism design. Journal of Advanced Mechanical Design, Systems and Manufacturing, 13 (4). ISSN 1881-3054

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Open Access URL: https://doi.org/10.1299/jamdsm.2019jamdsm0072 (Published version)

Abstract

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. © 2019 The Japan Society of Mechanical Engineers

Item Type: Article
Subjects: T Technology > T Technology (General)
Divisions: Maritime & Mechanical Engineering
Publisher: The Japan Society of Mechanical Engineers
Date Deposited: 20 Mar 2020 10:51
Last Modified: 20 Mar 2020 11:00
DOI or Identification number: 10.1299/jamdsm.2019jamdsm0072
URI: http://researchonline.ljmu.ac.uk/id/eprint/12560

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