{"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/empirical-study-of-artificial-fish-swarm","title":"Empirical Study of Artificial Fish Swarm Algorithm","arxiv_id":"1405.4138","date":"2014-05-16","proceeding":null,"authors":["Reza Azizi"],"abstract":"Artificial fish swarm algorithm (AFSA) is one of the swarm intelligence\noptimization algorithms that works based on population and stochastic search.\nIn order to achieve acceptable result, there are many parameters needs to be\nadjusted in AFSA. Among these parameters, visual and step are very significant\nin view of the fact that artificial fish basically move based on these\nparameters. In standard AFSA, these two parameters remain constant until the\nalgorithm termination. Large values of these parameters increase the capability\nof algorithm in global search, while small values improve the local search\nability of the algorithm. In this paper, we empirically study the performance\nof the AFSA and different approaches to balance between local and global\nexploration have been tested based on the adaptive modification of visual and\nstep during algorithm execution. The proposed approaches have been evaluated\nbased on the four well-known benchmark functions. Experimental results show\nconsiderable positive impact on the performance of AFSA.","url_abs":"http://arxiv.org/abs/1405.4138v1","url_pdf":"http://arxiv.org/pdf/1405.4138v1.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":"empirical-study-of-artificial-fish-swarm","repo_url":"https://github.com/nafiuny/AFSA-algorithm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"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}