{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-swarm-optimization-algorithm-inspired-in","title":"A swarm optimization algorithm inspired in the behavior of the social-spider","arxiv_id":"1406.3282","date":"2014-06-12","proceeding":null,"authors":["Erik Cuevas","Miguel Cienfuegos","Daniel Zaldivar","Marco Perez"],"abstract":"Swarm intelligence is a research field that models the collective behavior in\nswarms of insects or animals. Several algorithms arising from such models have\nbeen proposed to solve a wide range of complex optimization problems. In this\npaper, a novel swarm algorithm called the Social Spider Optimization (SSO) is\nproposed for solving optimization tasks. The SSO algorithm is based on the\nsimulation of cooperative behavior of social-spiders. In the proposed\nalgorithm, individuals emulate a group of spiders which interact to each other\nbased on the biological laws of the cooperative colony. The algorithm considers\ntwo different search agents (spiders): males and females. Depending on gender,\neach individual is conducted by a set of different evolutionary operators which\nmimic different cooperative behaviors that are typically found in the colony.\nIn order to illustrate the proficiency and robustness of the proposed approach,\nit is compared to other well-known evolutionary methods. The comparison\nexamines several standard benchmark functions that are commonly considered\nwithin the literature of evolutionary algorithms. The outcome shows a high\nperformance of the proposed method for searching a global optimum with several\nbenchmark functions.","url_abs":"http://arxiv.org/abs/1406.3282v1","url_pdf":"http://arxiv.org/pdf/1406.3282v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-swarm-optimization-algorithm-inspired-in","repo_url":"https://github.com/tinagui/Swarm-Intelligence-in-Bioinformatics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"evolutionary-algorithms","task_name":"Evolutionary Algorithms"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}