An efficient weighted slime mould algorithm for engineering optimization

• Published in: Journal of big data 2024-12, Vol.11 (1), p.141-35, Article 141
• Main Authors: Sun, Qibo, Wang, Chaofan, Chen, Yi, Heidari, Ali Asghar, Chen, Huiling, Liang, Guoxi
• Format: Article
• Language: English
• Subjects: Algorithms; Big Data; Big Data and Artificial Intelligence in Emerging Engineering Fields; Communications Engineering; Compression springs; Computational Science and Engineering; Computer Science; Covariance matrix; Covariance matrix adaptation; Data Mining and Knowledge Discovery; Database Management; Design optimization; Engineering; Engineering design; Heuristic methods; Information Storage and Retrieval; Mathematical Applications in Computer Science; Networks; Optimization; Parameters; Population (statistical); Pressure vessel design; Pressure vessels; Rank tests; Searching; Slime; Slime mould algorithm; Statistical methods; Swarm intelligence; Task complexity
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-024-01000-w

In engineering applications, optimal parameter design is crucial. While Slime Mould Algorithm (SMA) excels in parameter discovery under constrained conditions, it faces challenges in achieving global convergence and avoiding local opsecttimal traps in complex tasks. This paper introduces an enhanced variant of SMA, termed CCHSMA, which integrates a Chaotic Local Search (CLS) mechanism to improve initial population diversity and combines Covariance Matrix Adaptation (CMA) and Harris Hawks Optimization (HHO) strategies to enhance global search efficiency. CCHSMA aims to improve search quality and reduce the likelihood of getting trapped in local optima. We evaluated CCHSMA's effectiveness by benchmarking it against the standard SMA and its variants using 30 CEC2017 test functions, and compared its performance with seven notable meta-heuristic algorithms and ten advanced swarm intelligence variants. The experimental results demonstrate that CCHSMA outperforms the other algorithms tested on the benchmark functions. To further validate its practical utility, CCHSMA's performance was also benchmarked against leading algorithms in real-world engineering applications. This paper uses detailed statistical methods, including the Wilcoxon signed-rank test and the Friedman test, to validate the comparative results. Our findings show that CCHSMA outperforms other algorithms in solving complex engineering optimization problems such as tension/compression spring design, pressure vessel design, and three-bar truss design, proving to be a robust tool for complex engineering optimization. Its enhanced initial population diversity and improved global search efficiency are essential for effectively addressing diverse engineering challenges.