Self-Adaptive Differential Evolution with Gauss Distribution for Optimal Mechanism Design

被引：0
|
作者：
Nguyen, Van-Tinh [1 ]
Tran, Vu-Minh [1 ]
Bui, Ngoc-Tam [2 ]
机构：
[1] Hanoi Univ Sci & Technol, Sch Mech Engn, Hanoi 10000, Vietnam
[2] Shibaura Inst Technol, Dept Machinery & Control Syst, Tokyo 1358548, Japan
来源：
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 10期
关键词：
differential evolution; Gauss distribution; mutation procedure; optimization algorithm; PARTICLE SWARM OPTIMIZATION; MUTATION; PARAMETERS; ALGORITHM;
D O I：
10.3390/app13106284
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
Differential evolution (DE) is one of the best evolutionary algorithms (EAs). In recent decades, many techniques have been developed to enhance the performance of this algorithm, such as the Improve Self-Adaptive Differential Evolution (ISADE) algorithm. Based on the analysis of the aspects that may improve the performance of ISADE, we proposed a modified ISADE version with applying the Gauss distribution for mutation procedure. In ISADE, to determine the scaling factor (F), the population is ranked, then, based on the rank number, population size, and current generation, the formula of the Sigmoid function is used. In the proposed algorithm, F is amplified by a factor which is generated based on Gaussian distribution. It has the potential to enhance the variety of population. In comparison with several reference algorithms regarding converging speed and the consistency of optimal solutions, the simulation results reveal the performance of the suggested algorithm is exceptional.
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页数：25
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