{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T16:03:15Z","timestamp":1781625795567,"version":"3.54.5"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:00:00Z","timestamp":1657756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China and General Project Fund In The Field of Equipment Development Department","award":["61931004"],"award-info":[{"award-number":["61931004"]}]},{"name":"National Natural Science Foundation of China and General Project Fund In The Field of Equipment Development Department","award":["61403110308"],"award-info":[{"award-number":["61403110308"]}]},{"name":"Dalian University","award":["61931004"],"award-info":[{"award-number":["61931004"]}]},{"name":"Dalian University","award":["61403110308"],"award-info":[{"award-number":["61403110308"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>This paper aims to present a novel hybrid algorithm named SPSOA to address problems of low search capability and easy to fall into local optimization of seagull optimization algorithm. Firstly, the Sobol sequence in the low-discrepancy sequences is used to initialize the seagull population to enhance the population\u2019s diversity and ergodicity. Then, inspired by the sigmoid function, a new parameter is designed to strengthen the ability of the algorithm to coordinate early exploration and late development. Finally, the particle swarm optimization learning strategy is introduced into the seagull position updating method to improve the ability of the algorithm to jump out of local optimization. Through the simulation comparison with other algorithms on 12 benchmark test functions from different angles, the experimental results show that SPSOA is superior to other algorithms in stability, convergence accuracy, and speed. In engineering applications, SPSOA is applied to blind source separation of mixed images. The experimental results show that SPSOA can successfully realize the blind source separation of noisy mixed images and achieve higher separation performance than the compared algorithms.<\/jats:p>","DOI":"10.3390\/e24070973","type":"journal-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T00:17:10Z","timestamp":1657844230000},"page":"973","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5945-1859","authenticated-orcid":false,"given":"Qingyu","family":"Xia","sequence":"first","affiliation":[{"name":"Communication and Network Laboratory, Dalian University, Dalian 116622, China"},{"name":"School of Information Engineering, Dalian University, Dalian 116622, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanming","family":"Ding","sequence":"additional","affiliation":[{"name":"Communication and Network Laboratory, Dalian University, Dalian 116622, China"},{"name":"School of Information Engineering, Dalian University, Dalian 116622, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Zhang","sequence":"additional","affiliation":[{"name":"Communication and Network Laboratory, Dalian University, Dalian 116622, China"},{"name":"School of Information Engineering, Dalian University, Dalian 116622, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Communication and Network Laboratory, Dalian University, Dalian 116622, China"},{"name":"School of Information Engineering, Dalian University, Dalian 116622, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sen","family":"Li","sequence":"additional","affiliation":[{"name":"Communication and Network Laboratory, Dalian University, Dalian 116622, China"},{"name":"School of Information Engineering, Dalian University, Dalian 116622, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingda","family":"Li","sequence":"additional","affiliation":[{"name":"Communication and Network Laboratory, Dalian University, Dalian 116622, China"},{"name":"School of Information Engineering, Dalian University, Dalian 116622, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101104","DOI":"10.1016\/j.jocs.2020.101104","article-title":"Nature-inspired optimization algorithms: Challenges and open problems","volume":"46","author":"Yang","year":"2020","journal-title":"J. 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