DOAJ Open Access 2019

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

Tam BUI Trung NGUYEN Hiroshi HASEGAWA

Lihat Sumber DOI

Abstrak

In recent decades, new optimization algorithms have attracted much attention from researchers in both gradientand 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.

Topik & Kata Kunci

Engineering machinery, tools, and implements Mechanical engineering and machinery

Penulis (3)

Tam BUI

Trung NGUYEN

Hiroshi HASEGAWA

Format Sitasi

APA MLA BibTeX

BUI, T., NGUYEN, T., HASEGAWA, H. (2019). Opposition-based learning for self-adaptive control parameters in differential evolution for optimal mechanism design. https://doi.org/10.1299/jamdsm.2019jamdsm0072

Akses Cepat

Lihat di Sumber doi.org/10.1299/jamdsm.2019jamdsm0072

Informasi Jurnal

Tahun Terbit: 2019
Sumber Database: DOAJ
DOI: 10.1299/jamdsm.2019jamdsm0072
Akses: Open Access ✓