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To solve multi-robot path planning optimization problems, we embed two differential evolution (DE) strategies into COA, a self-adaptive differential evolution-based coati optimization algorithm (SDECOA) is proposed. Among these strategies, the proposed algorithm adaptively selects more suitable strategies for different problems, effectively balancing global and local search capabilities. To validate the algorithm\u2019s effectiveness, we tested it on CEC2020 benchmark functions and 48 CEC2020 real-world constrained optimization problems. In the latter\u2019s experiments, the algorithm proposed in this paper achieved the best overall results compared to the top five algorithms that won in the CEC2020 competition. Finally, we applied SDECOA to optimization multi-robot online path planning problem. Facing extreme environments with multiple static and dynamic obstacles of varying sizes, the SDECOA algorithm consistently outperformed some classical and state-of-the-art algorithms. Compared to DE and COA, the proposed algorithm achieved an average improvement of 46% and 50%, respectively. Through extensive experimental testing, it was confirmed that our proposed algorithm is highly competitive. The source code of the algorithm is accessible at: <jats:uri xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/ww2.mathworks.cn\/matlabcentral\/fileexchange\/164876-HDECOA\">https:\/\/ww2.mathworks.cn\/matlabcentral\/fileexchange\/164876-HDECOA<\/jats:uri>.<\/jats:p>","DOI":"10.1017\/s0263574725000049","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T11:04:56Z","timestamp":1738580696000},"page":"959-996","source":"Crossref","is-referenced-by-count":12,"title":["Self-adaptive differential evolution-based coati optimization algorithm for multi-robot path planning"],"prefix":"10.1017","volume":"43","author":[{"given":"Lun","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guo","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4404-952X","authenticated-orcid":false,"given":"Yongquan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifang","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huajuan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuxi","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2025,2,3]]},"reference":[{"key":"S0263574725000049_ref2","doi-asserted-by":"crossref","unstructured":"[2] Tournassoud, P. . 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