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An enhanced slime mould algorithm with triple strategy for engineering design optimization

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Vydáno v:Journal of Computational Design and Engineering vol. 11, no. 6 (Dec 2024), p. 36
Hlavní autor: Wang, Shuai
Další autoři: Zhang, Junxing, Li, Shaobo, Wu, Fengbin, Li, Shaoyang
Vydáno:
Oxford University Press
Témata:
Design optimization
Algorithms
Strategy
Design engineering
Slime
Task complexity
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Abstrakt:This paper introduces an enhanced slime mould algorithm (EESMA) designed to address critical challenges in engineering design optimization. The EESMA integrates three novel strategies: the Laplace logistic sine map technique, the adaptive t-distribution elite mutation mechanism, and the ranking-based dynamic learning strategy. These enhancements collectively improve the algorithm’s search efficiency, mitigate convergence to local optima, and bolster robustness in complex optimization tasks. The proposed EESMA demonstrates significant advantages over many conventional optimization algorithms and performs on par with, or even surpasses, several advanced algorithms in benchmark tests. Its superior performance is validated through extensive evaluations on diverse test sets, including IEEE CEC2014, IEEE CEC2020, and IEEE CEC2022, and its successful application in six distinct engineering problems. Notably, EESMA excels in solving economic load dispatch problems, highlighting its capability to tackle challenging optimization scenarios. The results affirm that EESMA is a competitive and effective tool for addressing complex optimization issues, showcasing its potential for widespread application in engineering and beyond.
ISSN:2288-5048
DOI:10.1093/jcde/qwae089
Zdroj:Engineering Database