High-performance differential evolution algorithm guided by information from individuals with potential

Li Tian

doi:10.1016/j.asoc.2020.106531

2020-10-01

PRE

Li Tian

Zhichao Li

Xuefeng Yan *

DOI：10.1016/j.asoc.2020.106531

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摘要

En 中文

In the differential evolution (DE) algorithm, many adaptive methods have been studied in terms of fitness values. However, few studies exist on the information from individuals with potential, which presents a large difference in fitness values from that of previous individuals and contains much evolution information. This study proposes a high-performance DE (PDE) algorithm guided by information from individuals with potential. In PDE, all individuals are divided into individuals with potential and individuals without potential according to their improvement in fitness values. The experience learned from the generation of individuals with potential is used to guide future individuals. At each generation, the selection probability of each strategy in the strategy pool is determined by the strategy's contribution to the improvement in fitness values when generating individuals with potential. The parameters are randomly generated with two distributions, and the location parameters of the two distributions are adjusted on the basis of the improvement in fitness values of individuals with potential. Different individuals (with or without potential) may have different characteristics and evolution methods. Therefore, the generation process of individuals with potential is separated into two cases according to whether they are from previous individuals with or without potential. The study results of the two cases are applied to guide the evolution of current individuals with and without potential. The proposed algorithm is evaluated by comparing it with five advanced DE variants on CEC2005 and seven up-to-date evolutionary algorithms on CEC2014. Comparison results demonstrate the competitive performance of the proposed algorithm. The PDE is also applied to estimate the parameters of a kinetic model of p-xylene oxidation process. (C) 2020 Elsevier B.V. All rights reserved.

Keyword:

Differential algorithm

Potential

Parameter adaptation

Mutation strategy adaptation