A segmented differential evolution with enhanced diversity and semi-adaptive parameter control
Abstract Differential evolution (DE) is widely recognized as one of the most potent optimization algorithms, capable of effectively addressing a broad spectrum of optimization challenges. Nevertheless, even the most advanced variants of DE share some common challenges. This paper introduces a novel...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-025-01883-z |
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| author | Huarong Xu Zhiyu Zhang Qianwei Deng Shengke Lin |
| author_facet | Huarong Xu Zhiyu Zhang Qianwei Deng Shengke Lin |
| author_sort | Huarong Xu |
| collection | DOAJ |
| description | Abstract Differential evolution (DE) is widely recognized as one of the most potent optimization algorithms, capable of effectively addressing a broad spectrum of optimization challenges. Nevertheless, even the most advanced variants of DE share some common challenges. This paper introduces a novel multi-stage semi-adaptive DE algorithm with enhanced diversity (MSA-DE), offering several key contributions: first, the algorithm is structured into three distinct stages, each employing a unique new mutation strategy and designed a new evolutionary scheme based on this segmentation, to better balance exploration and development at all stages of the process. Secondly, building on the idea of parameter restriction in the evolutionary stage of delineation, a semi-adaptive parameter control method based on the fitness of the irrelevant function is proposed which effectively solves the instability problem of excessive fluctuations in the convergence of adaptive parameters. Thirdly, new diversity maintenance mechanisms are proposed, including population initialization, shrinkage, and updating, which better ameliorated the conflicting issues of search range and search rate that existed at all stages of the DE variant. Finally, comprehensive experiments were conducted on the CEC2013, CEC2014, and CEC2017 benchmark test suites to rigorously assess the accuracy, convergence rate, and overall effectiveness of each module. The results show that MSA-DE exhibits strong competitiveness in single-objective optimisation problems. In addition, the experimental results demonstrate the superiority of the algorithm for real-world engineering problems. |
| format | Article |
| id | doaj-art-11bba2b3302741ec82404e662ea7e2e6 |
| institution | DOAJ |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-11bba2b3302741ec82404e662ea7e2e62025-08-20T03:07:55ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-04-0111615610.1007/s40747-025-01883-zA segmented differential evolution with enhanced diversity and semi-adaptive parameter controlHuarong Xu0Zhiyu Zhang1Qianwei Deng2Shengke Lin3College of Computer and Information Engineering, Xiamen University of TechnologyCollege of Computer and Information Engineering, Xiamen University of TechnologyCollege of Computer and Information Engineering, Xiamen University of TechnologyCollege of Computer and Information Engineering, Xiamen University of TechnologyAbstract Differential evolution (DE) is widely recognized as one of the most potent optimization algorithms, capable of effectively addressing a broad spectrum of optimization challenges. Nevertheless, even the most advanced variants of DE share some common challenges. This paper introduces a novel multi-stage semi-adaptive DE algorithm with enhanced diversity (MSA-DE), offering several key contributions: first, the algorithm is structured into three distinct stages, each employing a unique new mutation strategy and designed a new evolutionary scheme based on this segmentation, to better balance exploration and development at all stages of the process. Secondly, building on the idea of parameter restriction in the evolutionary stage of delineation, a semi-adaptive parameter control method based on the fitness of the irrelevant function is proposed which effectively solves the instability problem of excessive fluctuations in the convergence of adaptive parameters. Thirdly, new diversity maintenance mechanisms are proposed, including population initialization, shrinkage, and updating, which better ameliorated the conflicting issues of search range and search rate that existed at all stages of the DE variant. Finally, comprehensive experiments were conducted on the CEC2013, CEC2014, and CEC2017 benchmark test suites to rigorously assess the accuracy, convergence rate, and overall effectiveness of each module. The results show that MSA-DE exhibits strong competitiveness in single-objective optimisation problems. In addition, the experimental results demonstrate the superiority of the algorithm for real-world engineering problems.https://doi.org/10.1007/s40747-025-01883-zDifferential evolutionMulti-stageSemi-adaptive parameterPopulation diversity |
| spellingShingle | Huarong Xu Zhiyu Zhang Qianwei Deng Shengke Lin A segmented differential evolution with enhanced diversity and semi-adaptive parameter control Complex & Intelligent Systems Differential evolution Multi-stage Semi-adaptive parameter Population diversity |
| title | A segmented differential evolution with enhanced diversity and semi-adaptive parameter control |
| title_full | A segmented differential evolution with enhanced diversity and semi-adaptive parameter control |
| title_fullStr | A segmented differential evolution with enhanced diversity and semi-adaptive parameter control |
| title_full_unstemmed | A segmented differential evolution with enhanced diversity and semi-adaptive parameter control |
| title_short | A segmented differential evolution with enhanced diversity and semi-adaptive parameter control |
| title_sort | segmented differential evolution with enhanced diversity and semi adaptive parameter control |
| topic | Differential evolution Multi-stage Semi-adaptive parameter Population diversity |
| url | https://doi.org/10.1007/s40747-025-01883-z |
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