A Two-Stage Adaptive Differential Evolution Algorithm with Accompanying Populations

被引：0

作者：

Min, Chao ^{[1
,2
,3
]}

Zhang, Min ^{[1
]}

Zhang, Qingxia ^{[1
]}

Jiang, Zeyun ^{[2
,4
]}

Zhou, Liwen ^{[1
]}

机构：

[1] Southwest Petr Univ, Sch Sci, 8 Xindu Rd, Chengdu 610500, Peoples R China

[2] Southwest Petr Univ, Inst Artificial Intelligence, Xindu Rd, Chengdu 610500, Peoples R China

[3] Southwest Petr Univ, Natl Key Lab Oil & Gas Reservoir Geol & Exploitat, Xindu Rd, Chengdu 610500, Peoples R China

[4] Heriot Watt Univ, Sch Energy Geosci Infrastruct & Soc, Riccarton Mains Rd, Edinburgh EH14 4AS, Scotland

来源：

MATHEMATICS | 2025年 / 13卷 / 03期

关键词：

differential evolution; optimization algorithms; staged evolution; accompanying populations; adaptive parameters;

D O I：

10.3390/math13030440

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Stochastic simulations are often used to determine the crossover rates and step size of evolutionary algorithms to avoid the tuning process, but they cannot fully utilize information from the evolutionary process. A two-stage adaptive differential evolution algorithm (APDE) is proposed in this article based on an accompanying population, and it has unique mutation strategies and adaptive parameters that conform to the search characteristics. The global exploration capability can be enhanced by the accompanying population to achieve a balance between global exploration and local search. This algorithm has proven to be convergent with a probability of 1 using the theory of Markov chains. In numerical experiments, the APDE is statistically compared with nine comparison algorithms using the CEC2005 and CEC2017 standard set of test functions, and the results show that the APDE is statistically superior to the comparison methods.

引用

页数：28

共 30 条

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