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Several species fall under the \u201cPortia\u201d genus, with habitats spanning regions in Africa, Asia, and Australia. Demonstrating the ability to tackle new challenges, these spiders can learn and adapt their strategies based on prior experiences. This study introduces the Portia Spider Algorithm (PSA), a swarm-based technique inspired by the unique predatory strategies of the Portia spider. We conducted rigorous assessments of PSA performance against 23 classical test functions, 29 CEC2017 test cases, and 5 engineering optimization tasks. To demonstrate the effectiveness of the PSA, outcomes were juxtaposed with those of renowned algorithms. This paper explores the mechanics, advantages, and potential applications of PSA within the vast domain of computational optimization.<\/jats:p>\n                <jats:p><jats:bold>Graphical Abstract<\/jats:bold><\/jats:p>","DOI":"10.1007\/s10462-023-10683-1","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T05:02:24Z","timestamp":1706677344000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Portia spider algorithm: an evolutionary computation approach for engineering application"],"prefix":"10.1007","volume":"57","author":[{"given":"Vu Hong Son","family":"Pham","sequence":"first","affiliation":[]},{"given":"Nghiep Trinh","family":"Nguyen Dang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"10683_CR1","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.eswa.2017.07.043","volume":"90","author":"M Abd Elaziz","year":"2017","unstructured":"Abd Elaziz M, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. 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