Wei Wan¹, Gaige Wang^1,2,3,*, Junyu Dong¹

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1495-1516, 2022, DOI:10.32604/cmes.2021.017987 - 30 December 2021

Abstract Adaptive Cross-Generation Differential Evolution (ACGDE) is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms (EAs). However, its convergence and diversity are not satisfactory compared with the latest algorithms. In order to adapt to the current environment, ACGDE requires improvements in many aspects, such as its initialization and mutant operator. In this paper, an enhanced version is proposed, namely SIACGDE. It incorporates a strengthened initialization strategy and optimized parameters in contrast to its predecessor. These improvements make the direction of crossgeneration mutation more clearly and the ability of searching More >

Ti-Hung Chen¹, Ming-Feng Yeh^2,*

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 407-420, 2020, DOI:10.32604/iasc.2020.013917

Abstract On the basis of JADE (adaptive differential evolution with optional external archive) and the modified differential evolution with p-best crossover (MDE_pBX), this study attempts to propose a modified mutation strategy termed "DE/(pbest)/1" for the differential evolution (DE) algorithm, where “(pbest)” represents the mean of p top-best vectors. Two modified parameter adaptation mechanisms are also proposed to update the crossover rate and the scale factor, respectively, in an adaptive manner. The DE variant with the proposed mutation strategy and two modified adaptation mechanisms is termed adaptive differential evolution with mean-of-pbest mutation strategy, denoted by ADE_pBM is comparable to or More >