{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T17:57:26Z","timestamp":1770832646402,"version":"3.50.1"},"reference-count":53,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"vor","delay-in-days":249,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","award":["202002064014"],"award-info":[{"award-number":["202002064014"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Based on Salp Swarm Algorithm (SSA) and Slime Mould Algorithm (SMA), a novel hybrid optimization algorithm, named Hybrid Slime Mould Salp Swarm Algorithm (HSMSSA), is proposed to solve constrained engineering problems. SSA can obtain good results in solving some optimization problems. However, it is easy to suffer from local minima and lower density of population. SMA specializes in global exploration and good robustness, but its convergence rate is too slow to find satisfactory solutions efficiently. Thus, in this paper, considering the characteristics and advantages of both the above optimization algorithms, SMA is integrated into the leader position updating equations of SSA, which can share helpful information so that the proposed algorithm can utilize these two algorithms\u2019 advantages to enhance global optimization performance. Furthermore, Levy flight is utilized to enhance the exploration ability. It is worth noting that a novel strategy called mutation opposition\u2010based learning is proposed to enhance the performance of the hybrid optimization algorithm on premature convergence avoidance, balance between exploration and exploitation phases, and finding satisfactory global optimum. To evaluate the efficiency of the proposed algorithm, HSMSSA is applied to 23 different benchmark functions of the unimodal and multimodal types. Additionally, five classical constrained engineering problems are utilized to evaluate the proposed technique\u2019s practicable abilities. The simulation results show that the HSMSSA method is more competitive and presents more engineering effectiveness for real\u2010world constrained problems than SMA, SSA, and other comparative algorithms. In the end, we also provide some potential areas for future studies such as feature selection and multilevel threshold image segmentation.<\/jats:p>","DOI":"10.1155\/2021\/6379469","type":"journal-article","created":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T21:35:09Z","timestamp":1631050509000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Hybrid SSA and SMA with Mutation Opposition\u2010Based Learning for Constrained Engineering Problems"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5933-074X","authenticated-orcid":false,"given":"Shuang","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3081-5185","authenticated-orcid":false,"given":"Qingxin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yuxiang","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4339-8464","authenticated-orcid":false,"given":"Heming","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Laith","family":"Abualigah","sequence":"additional","affiliation":[]},{"given":"Rong","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Di","family":"Wu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs11121421"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09909-3"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-04789-8"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/bf00113892"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008202821328"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2008.919004"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.220.4598.671"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2009.03.004"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.08.023"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-015-1870-7"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.04.126"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.COMPSTRUC.2012.09.003"},{"key":"e_1_2_9_13_2","unstructured":"MoghaddamF. 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