Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization
In recent years, the prevalence of Many-Objective Optimization Problems (MaOPs) in practical applications has been increasing. However, traditional multi-objective optimization algorithms, such as Multiple Objective Particle Swarm Optimization (MOPSO), often face challenges of dimensionality and sel...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10824798/ |
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| author | Yunfei Yi ZhiYong Wang Yunying Shi Zhengzhuo Song Binbin Zhao |
| author_facet | Yunfei Yi ZhiYong Wang Yunying Shi Zhengzhuo Song Binbin Zhao |
| author_sort | Yunfei Yi |
| collection | DOAJ |
| description | In recent years, the prevalence of Many-Objective Optimization Problems (MaOPs) in practical applications has been increasing. However, traditional multi-objective optimization algorithms, such as Multiple Objective Particle Swarm Optimization (MOPSO), often face challenges of dimensionality and selection pressure when handling MaOPs. To overcome these challenges, this study proposes a Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization (CDA-MOPSO) algorithm. This algorithm introduces a convergence metric to assess the convergence status and solution distribution quality of the particle swarm during iterations. Based on this metric, Convergence-Aware Learning Factor Adjustment (CALFA), Convergence-Oriented Dimension Variation Strategy (CODVS), and Convergence-Driven Archive Maintenance (CDAM) operations are proposed. Additionally, evolutionary search is further conducted on the external archive to enhance algorithm performance. To validate the performance of the CDA-MOPSO algorithm, extensive experiments are conducted using standard test problems such as DTLZ and WFG. Experimental results demonstrate that the CDA-MOPSO algorithm exhibits superior convergence and solution distribution characteristics across multiple standard test functions, particularly in handling many-objective optimization problems, outperforming traditional multi-objective algorithms significantly. In conclusion, the CDA-MOPSO algorithm provides a novel solution for many-objective optimization problems, offering strong convergence capability and solution diversity, with broad prospects for practical applications. |
| format | Article |
| id | doaj-art-1a479b57d49443be996a924648196a8c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1a479b57d49443be996a924648196a8c2025-01-10T00:00:58ZengIEEEIEEE Access2169-35362025-01-01135129514410.1109/ACCESS.2025.352585010824798Convergence-Driven Adaptive Many-Objective Particle Swarm OptimizationYunfei Yi0ZhiYong Wang1https://orcid.org/0009-0005-2648-2655Yunying Shi2Zhengzhuo Song3Binbin Zhao4https://orcid.org/0009-0006-1102-4632School of Big Data and Computer, Hechi University, Yizhou, Hechi, ChinaCollege of Computer Science and Engineering, Guangxi Normal University, Guilin, ChinaSchool of Big Data and Computer, Hechi University, Yizhou, Hechi, ChinaCollege of Computer Science and Engineering, Guangxi Normal University, Guilin, ChinaSchool of Automation, Guangxi University of Science and Technology, Liuzhou, ChinaIn recent years, the prevalence of Many-Objective Optimization Problems (MaOPs) in practical applications has been increasing. However, traditional multi-objective optimization algorithms, such as Multiple Objective Particle Swarm Optimization (MOPSO), often face challenges of dimensionality and selection pressure when handling MaOPs. To overcome these challenges, this study proposes a Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization (CDA-MOPSO) algorithm. This algorithm introduces a convergence metric to assess the convergence status and solution distribution quality of the particle swarm during iterations. Based on this metric, Convergence-Aware Learning Factor Adjustment (CALFA), Convergence-Oriented Dimension Variation Strategy (CODVS), and Convergence-Driven Archive Maintenance (CDAM) operations are proposed. Additionally, evolutionary search is further conducted on the external archive to enhance algorithm performance. To validate the performance of the CDA-MOPSO algorithm, extensive experiments are conducted using standard test problems such as DTLZ and WFG. Experimental results demonstrate that the CDA-MOPSO algorithm exhibits superior convergence and solution distribution characteristics across multiple standard test functions, particularly in handling many-objective optimization problems, outperforming traditional multi-objective algorithms significantly. In conclusion, the CDA-MOPSO algorithm provides a novel solution for many-objective optimization problems, offering strong convergence capability and solution diversity, with broad prospects for practical applications.https://ieeexplore.ieee.org/document/10824798/Many-objective optimization problemsparticle swarm optimizationconvergence |
| spellingShingle | Yunfei Yi ZhiYong Wang Yunying Shi Zhengzhuo Song Binbin Zhao Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization IEEE Access Many-objective optimization problems particle swarm optimization convergence |
| title | Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization |
| title_full | Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization |
| title_fullStr | Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization |
| title_full_unstemmed | Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization |
| title_short | Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization |
| title_sort | convergence driven adaptive many objective particle swarm optimization |
| topic | Many-objective optimization problems particle swarm optimization convergence |
| url | https://ieeexplore.ieee.org/document/10824798/ |
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