DyS-MPADE: A novel multipopulation adaptive differential evolution methodology based on dynamic subpopulation

Differential evolution (DE) is widely recognized in global optimization. However, it is not immune to the issue of premature convergence. To address this challenge, a dynamic subpopulation-based DE algorithm (i.e., DyS-MPADE) is presented in this paper. In DyS-MPADE, spectral hashing clustering is i...

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Bibliographic Details
Published inJournal of computational design and engineering Vol. 12; no. 3; pp. 204 - 225
Main Authors Huang, Chen, Zhu, Junyi, Xu, Mingyao
Format Journal Article
LanguageEnglish
Published Oxford University Press 19.03.2025
한국CDE학회
Subjects
기계공학
differential evolution
multipopulation
adaptive parameters
dynamic adjustment
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ISSN2288-5048
2288-4300
2288-5048
DOI10.1093/jcde/qwaf024

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Summary:Differential evolution (DE) is widely recognized in global optimization. However, it is not immune to the issue of premature convergence. To address this challenge, a dynamic subpopulation-based DE algorithm (i.e., DyS-MPADE) is presented in this paper. In DyS-MPADE, spectral hashing clustering is introduced to adaptively adjust the population structure. The innovative approach enhances SHADE’s capacity by balancing exploration and exploitation, thereby improving its convergence properties. The population of DyS-MPADE is decomposed into subpopulations, facilitating independent evolution and emulating geographical separation. The number of subpopulations is dynamically adjusted by clustering analysis of optimal and pessimal individuals. We conducted a thorough evaluation of DyS-MPADE using the CEC 2017 benchmark functions to assess its performance of different dimensions. Compared to DE, adaptive differential evolution with optional external archive (JADE), success-history based parameter adaptation for differential evolution (SHADE), Bernstein-Levy differential evolution algorithm (BDE), Bezier Search Differential Evolution Algorithm (BeSD), Egret swarm optimization algorithm (ESOA), Artificial rabbits optimization (ARO), Sea-horse optimizer (SHO), GRO, and DO, the average ranks of DyS-MPADE are 1.76 and 1.2 in 50 and 100 dimensions, respectively. The accuracy of DyS-MPADE is superior to SHADE and other state-of-the-art DE variants. Additionally, DyS-MPADE is applied to airport gate allocation problem, where it demonstrated better performance compared to other algorithms, indicating its significance for engineering applications. Graphical Abstract Graphical Abstract
ISSN:2288-5048
2288-4300
2288-5048
DOI:10.1093/jcde/qwaf024