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However, SMA suffers from issues such as easily getting trapped in local optima and slow convergence, which pose challenges when applied to practical problems. Therefore, this study proposes an improved SMA, named HESMA, by incorporating the covariance matrix adaptation evolution strategy (CMA-ES) and storing the best position of each individual (SBP). On one hand, CMA-ES enhances the algorithm\u2019s local exploration capability, addressing the issue of the algorithm being unable to explore the vicinity of the optimal solution. On the other hand, SBP enhances the convergence speed of the algorithm and prevents it from diverging to other inferior solutions. Finally, to validate the effectiveness of our proposed algorithm, this study conducted experiments on 30 IEEE CEC 2017 benchmark functions and compared HESMA with 12 conventional metaheuristic algorithms. The results demonstrated that HESMA indeed achieved improvements over SMA. Furthermore, to highlight the performance of HESMA further, this study compared it with 13 advanced algorithms, and the results showed that HESMA outperformed these advanced algorithms significantly. Next, this study applied HESMA to five engineering optimization problems, and the experimental results revealed that HESMA exhibited significant advantages in solving real-world engineering optimization problems. These findings further support the effectiveness and practicality of our algorithm in addressing complex engineering design challenges.<\/jats:p>","DOI":"10.1093\/jcde\/qwae054","type":"journal-article","created":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T06:22:09Z","timestamp":1718173329000},"page":"151-183","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing slime mould algorithm for engineering optimization: leveraging covariance matrix adaptation and best position management"],"prefix":"10.1093","volume":"11","author":[{"given":"Jinpeng","family":"Huang","sequence":"first","affiliation":[{"name":"Institute of Big Data and Information Technology, Wenzhou University , Wenzhou 325035 , China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7027-6542","authenticated-orcid":false,"given":"Yi","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Big Data and Information Technology, Wenzhou University , Wenzhou 325035 , China"}]},{"given":"Ali Asghar","family":"Heidari","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, \u00a0 Tehran 1439957131 , Iran"}]},{"given":"Lei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University , Chengdu, Sichuan 610065 , China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7714-9693","authenticated-orcid":false,"given":"Huiling","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Big Data and Information Technology, Wenzhou University , Wenzhou 325035 , China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4754-6644","authenticated-orcid":false,"given":"Guoxi","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Wenzhou Polytechnic , Wenzhou 325035 , 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