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The salp swarm algorithm (SSA) has a simple structure and fast processing speed and can gain significant results on objective functions with fewer local optima. However, it has poor exploration ability and is easy to suffer from the local optimal solutions, so it performs poorly on multimodal objective functions. Besides, its unfair balance of exploration and exploitation is another notable shortcoming. To ameliorate these shortcomings and enhance the algorithm\u2019s performance on multimodal functions, this research proposes simulated annealing (SA) improved salp swarm algorithm (SASSA). SASSA embeds the SA strategy into the followers\u2019 position updating method of SSA, performs a certain number of iterations of the SA strategy, and uses L\u00e9vy flight to realize the random walk in the SA strategy. SASSA and 23 original and improved competitive algorithms are compared on 30 IEEE CEC2017 benchmark functions. SASSA ranked first in the Friedman test. Compared with SSA, SASSA can obtain better solutions on 27 benchmark functions. The balance and diversity experiment and analysis of SSA and SASSA are carried out. SASSA\u2019s practicability is verified by solving five engineering problems and the fertilizer effect function problem. Experimental and statistical results reveal that the proposed SASSA has strong competitiveness and outperforms all the competitors. SASSA has excellent exploration ability, suitable for solving composition functions with multiple peaks. Meanwhile, SASSA brings about a good balance of exploration and exploitation and dramatically improves the quality of the solutions.<\/jats:p>","DOI":"10.1093\/jcde\/qwac021","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T20:15:32Z","timestamp":1644869732000},"page":"633-669","source":"Crossref","is-referenced-by-count":21,"title":["Performance optimization of annealing salp swarm algorithm: frameworks and applications for engineering design"],"prefix":"10.1093","volume":"9","author":[{"given":"Jiuman","family":"Song","sequence":"first","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengcheng","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali Asghar","family":"Heidari","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1417466191, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawen","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helong","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiling","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, 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