Multi-Strategy Enhanced Slime Mould Algorithm for Optimization Problems
The slime mould algorithm (SMA) simulates the mechanism by which slime moulds optimize paths through chemical signaling and morphological changes, enabling efficient exploration and exploitation of the solution space. While SMA is simple and flexible, it faces challenges such as slow convergence and...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10835057/ |
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| author | Zaixin Duan Xuezhong Qian Wei Song |
| author_facet | Zaixin Duan Xuezhong Qian Wei Song |
| author_sort | Zaixin Duan |
| collection | DOAJ |
| description | The slime mould algorithm (SMA) simulates the mechanism by which slime moulds optimize paths through chemical signaling and morphological changes, enabling efficient exploration and exploitation of the solution space. While SMA is simple and flexible, it faces challenges such as slow convergence and a tendency to become trapped in local optima. To address these limitations, this paper introduces an enhanced algorithm that integrates bloch sphere-based Elite Population Initialization with an adaptive search operator strategy based on cauchy inverse cumulative distribution(QCMSMA). The proposed algorithm employs a Bloch sphere-based elite population initialization strategy, which utilizes quantum state mapping to enhance diversity and incorporates elite selection to guarantee high-quality initial solutions, ultimately improving optimization performance. An adaptive search operator leveraging the Cauchy inverse cumulative distribution is employed to dynamically adjust step sizes, improving exploration and efficiency. Additionally, a local Gaussian perturbation mutation strategy is incorporated to mitigate the risk of premature convergence to local optima. The QCMSMA algorithm was rigorously evaluated using 23 benchmark functions and the CEC2017 test suite. Comparative analysis against several well-known optimization algorithms was performed, accompanied by statistical assessments using the Wilcoxon rank-sum test and Friedman ranking analysis. Specifically, QCMSMA ranked first in overall performance compared to the other algorithms. Experimental results indicate that QCMSMA consistently outperforms its counterparts in terms of optimization efficiency, convergence speed, and stability. Finally, the algorithm was applied to a real-world unmanned aerial vehicle (UAV) path planning problem, where QCMSMA achieved the best optimization performance compared to other algorithms, ranking first in terms of solution quality. This result demonstrates its practical engineering applicability and effectiveness in solving complex optimization tasks. |
| format | Article |
| id | doaj-art-74d232f7800447dc815e91e77def2f0b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-74d232f7800447dc815e91e77def2f0b2025-01-15T00:02:36ZengIEEEIEEE Access2169-35362025-01-01137850787110.1109/ACCESS.2025.352750910835057Multi-Strategy Enhanced Slime Mould Algorithm for Optimization ProblemsZaixin Duan0https://orcid.org/0009-0005-0730-268XXuezhong Qian1https://orcid.org/0009-0001-3221-1000Wei Song2https://orcid.org/0000-0002-3148-5827School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, ChinaThe slime mould algorithm (SMA) simulates the mechanism by which slime moulds optimize paths through chemical signaling and morphological changes, enabling efficient exploration and exploitation of the solution space. While SMA is simple and flexible, it faces challenges such as slow convergence and a tendency to become trapped in local optima. To address these limitations, this paper introduces an enhanced algorithm that integrates bloch sphere-based Elite Population Initialization with an adaptive search operator strategy based on cauchy inverse cumulative distribution(QCMSMA). The proposed algorithm employs a Bloch sphere-based elite population initialization strategy, which utilizes quantum state mapping to enhance diversity and incorporates elite selection to guarantee high-quality initial solutions, ultimately improving optimization performance. An adaptive search operator leveraging the Cauchy inverse cumulative distribution is employed to dynamically adjust step sizes, improving exploration and efficiency. Additionally, a local Gaussian perturbation mutation strategy is incorporated to mitigate the risk of premature convergence to local optima. The QCMSMA algorithm was rigorously evaluated using 23 benchmark functions and the CEC2017 test suite. Comparative analysis against several well-known optimization algorithms was performed, accompanied by statistical assessments using the Wilcoxon rank-sum test and Friedman ranking analysis. Specifically, QCMSMA ranked first in overall performance compared to the other algorithms. Experimental results indicate that QCMSMA consistently outperforms its counterparts in terms of optimization efficiency, convergence speed, and stability. Finally, the algorithm was applied to a real-world unmanned aerial vehicle (UAV) path planning problem, where QCMSMA achieved the best optimization performance compared to other algorithms, ranking first in terms of solution quality. This result demonstrates its practical engineering applicability and effectiveness in solving complex optimization tasks.https://ieeexplore.ieee.org/document/10835057/Slime mould algorithmmetaheuristicsquantum bloch sphere strategylocal Gaussian perturbation strategyUAV path planning |
| spellingShingle | Zaixin Duan Xuezhong Qian Wei Song Multi-Strategy Enhanced Slime Mould Algorithm for Optimization Problems IEEE Access Slime mould algorithm metaheuristics quantum bloch sphere strategy local Gaussian perturbation strategy UAV path planning |
| title | Multi-Strategy Enhanced Slime Mould Algorithm for Optimization Problems |
| title_full | Multi-Strategy Enhanced Slime Mould Algorithm for Optimization Problems |
| title_fullStr | Multi-Strategy Enhanced Slime Mould Algorithm for Optimization Problems |
| title_full_unstemmed | Multi-Strategy Enhanced Slime Mould Algorithm for Optimization Problems |
| title_short | Multi-Strategy Enhanced Slime Mould Algorithm for Optimization Problems |
| title_sort | multi strategy enhanced slime mould algorithm for optimization problems |
| topic | Slime mould algorithm metaheuristics quantum bloch sphere strategy local Gaussian perturbation strategy UAV path planning |
| url | https://ieeexplore.ieee.org/document/10835057/ |
| work_keys_str_mv | AT zaixinduan multistrategyenhancedslimemouldalgorithmforoptimizationproblems AT xuezhongqian multistrategyenhancedslimemouldalgorithmforoptimizationproblems AT weisong multistrategyenhancedslimemouldalgorithmforoptimizationproblems |