Multiple Elite Individual Guided Piecewise Search-Based Differential Evolution

被引：13

作者：

Gupta, Shubham ^{[1
]}

Singh, Shitu ^{[2
]}

Su, Rong ^{[1
]}

Gao, Shangce ^{[3
]}

Bansal, Jagdish Ch ^{[2
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

[2] South Asian Univ, Dept Math, New Delhi 110021, India

[3] Univ Toyama, Fac Engn, Toyama 9308555, Japan

来源：

IEEE-CAA JOURNAL OF AUTOMATICA SINICA | 2023年 / 10卷 / 01期

基金：

日本学术振兴会;

关键词：

Control parameters; differential Evolution; metaheuristic algorithms; mutation operator; CONTROL PARAMETERS; FUNCTION OPTIMIZATION; ALGORITHM; MUTATION; ENSEMBLE;

D O I：

10.1109/JAS.2023.123018

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The differential evolution (DE) algorithm relies mainly on mutation strategy and control parameters' selection. To take full advantage of top elite individuals in terms of fitness and success rates, a new mutation operator is proposed. The control parameters such as scale factor and crossover rate are tuned based on their success rates recorded over past evolutionary stages. The proposed DE variant, MIDE, performs the evolution in a piecewise manner, i.e., after every predefined evolutionary stages, MIDE adjusts its settings to enrich its diversity skills. The performance of the MIDE is validated on two different sets of benchmarks: CEC 2014 and CEC 2017 (special sessions & competitions on real-parameter single objective optimization) using different performance measures. In the end, MIDE is also applied to solve constrained engineering problems. The efficiency and effectiveness of the MIDE are further confirmed by a set of experiments.

引用

页码：135 / 158

页数：24

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