Modified Particle Swarm Optimization for Engineering Optimization Problems and UAV Path Planning
Optimization problems aim to identify the best solution from a wide range of possibilities. The Particle Swarm Optimization (PSO) algorithm is widely recognized for its simplicity and efficiency, but it suffers from issues such as local optima trapping and degraded performance in high-dimensional pr...
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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/10965620/ |
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| author | Xing Zhang Gaoquan Gu Cunsheng Zhao |
| author_facet | Xing Zhang Gaoquan Gu Cunsheng Zhao |
| author_sort | Xing Zhang |
| collection | DOAJ |
| description | Optimization problems aim to identify the best solution from a wide range of possibilities. The Particle Swarm Optimization (PSO) algorithm is widely recognized for its simplicity and efficiency, but it suffers from issues such as local optima trapping and degraded performance in high-dimensional problems. To address these limitations, this paper proposes a modified PSO algorithm (MPSO). The MPSO incorporates several novel strategies: a Sigmoid-based nonlinear inertia weight decay function, which supports global exploration in the early stages and local refinement in later stages; a non-uniform mutation operator, which amplifies perturbation to enhance global search in the early phases and reduces perturbation to guide convergence in the later phases; and a sine-cosine disturbance strategy, which boosts solution diversity and accelerates global optimization and convergence. Compared to eight PSO variants, MPSO demonstrates superior performance across various benchmark functions in the CEC2017 and CEC2022 suites, particularly excelling in high-dimensional problems. Most of the test functions show better results than the competing algorithms. Finally, MPSO is applied to five engineering optimization problems and UAV path planning tasks, where the experimental results confirm its effectiveness in real-world applications. The algorithm consistently finds high-quality solutions, highlighting its exceptional performance and practical value in solving complex optimization challenges. |
| format | Article |
| id | doaj-art-22d8357e5d5240e082d6cff96dc60e0c |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-22d8357e5d5240e082d6cff96dc60e0c2025-08-20T01:52:14ZengIEEEIEEE Access2169-35362025-01-0113839568398210.1109/ACCESS.2025.356062410965620Modified Particle Swarm Optimization for Engineering Optimization Problems and UAV Path PlanningXing Zhang0Gaoquan Gu1https://orcid.org/0009-0000-8202-9058Cunsheng Zhao2College of Mechanical and Electrical Engineering, Quanzhou University of Information Engineering, Quanzhou, ChinaCollege of Naval Architecture and Ocean Engineering, Naval University of Engineering, Wuhan, ChinaCollege of Naval Architecture and Ocean Engineering, Naval University of Engineering, Wuhan, ChinaOptimization problems aim to identify the best solution from a wide range of possibilities. The Particle Swarm Optimization (PSO) algorithm is widely recognized for its simplicity and efficiency, but it suffers from issues such as local optima trapping and degraded performance in high-dimensional problems. To address these limitations, this paper proposes a modified PSO algorithm (MPSO). The MPSO incorporates several novel strategies: a Sigmoid-based nonlinear inertia weight decay function, which supports global exploration in the early stages and local refinement in later stages; a non-uniform mutation operator, which amplifies perturbation to enhance global search in the early phases and reduces perturbation to guide convergence in the later phases; and a sine-cosine disturbance strategy, which boosts solution diversity and accelerates global optimization and convergence. Compared to eight PSO variants, MPSO demonstrates superior performance across various benchmark functions in the CEC2017 and CEC2022 suites, particularly excelling in high-dimensional problems. Most of the test functions show better results than the competing algorithms. Finally, MPSO is applied to five engineering optimization problems and UAV path planning tasks, where the experimental results confirm its effectiveness in real-world applications. The algorithm consistently finds high-quality solutions, highlighting its exceptional performance and practical value in solving complex optimization challenges.https://ieeexplore.ieee.org/document/10965620/Particle swarm optimizationnon-uniform mutation operatortransformer prediction modelUAV road planningengineering optimization problem |
| spellingShingle | Xing Zhang Gaoquan Gu Cunsheng Zhao Modified Particle Swarm Optimization for Engineering Optimization Problems and UAV Path Planning IEEE Access Particle swarm optimization non-uniform mutation operator transformer prediction model UAV road planning engineering optimization problem |
| title | Modified Particle Swarm Optimization for Engineering Optimization Problems and UAV Path Planning |
| title_full | Modified Particle Swarm Optimization for Engineering Optimization Problems and UAV Path Planning |
| title_fullStr | Modified Particle Swarm Optimization for Engineering Optimization Problems and UAV Path Planning |
| title_full_unstemmed | Modified Particle Swarm Optimization for Engineering Optimization Problems and UAV Path Planning |
| title_short | Modified Particle Swarm Optimization for Engineering Optimization Problems and UAV Path Planning |
| title_sort | modified particle swarm optimization for engineering optimization problems and uav path planning |
| topic | Particle swarm optimization non-uniform mutation operator transformer prediction model UAV road planning engineering optimization problem |
| url | https://ieeexplore.ieee.org/document/10965620/ |
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