{"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/pontogammarus-maeoticus-swarm-optimization-a","title":"Pontogammarus Maeoticus Swarm Optimization: A Metaheuristic Optimization Algorithm","arxiv_id":"1807.01844","date":"2018-07-05","proceeding":null,"authors":["Benyamin Ghojogh","Saeed Sharifian"],"abstract":"Nowadays, metaheuristic optimization algorithms are used to find the global\noptima in difficult search spaces. Pontogammarus Maeoticus Swarm Optimization\n(PMSO) is a metaheuristic algorithm imitating aquatic nature and foraging\nbehavior. Pontogammarus Maeoticus, also called Gammarus in short, is a tiny\ncreature found mostly in coast of Caspian Sea in Iran. In this algorithm,\nglobal optima is modeled as sea edge (coast) to which Gammarus creatures are\nwilling to move in order to rest from sea waves and forage in sand. Sea waves\nsatisfy exploration and foraging models exploitation. The strength of sea wave\nis determined according to distance of Gammarus from sea edge. The angles of\nwaves applied on several particles are set randomly helping algorithm not be\nstuck in local bests. Meanwhile, the neighborhood of particles change\nadaptively resulting in more efficient progress in searching. The proposed\nalgorithm, although is applicable on any optimization problem, is experimented\nfor partially shaded solar PV array. Experiments on CEC05 benchmarks, as well\nas solar PV array, show the effectiveness of this optimization algorithm.","url_abs":"http://arxiv.org/abs/1807.01844v1","url_pdf":"http://arxiv.org/pdf/1807.01844v1.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":"pontogammarus-maeoticus-swarm-optimization-a","repo_url":"https://github.com/bghojogh/PMSO-Metaheuristic-Optimization","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"metaheuristic-optimization","task_name":"Metaheuristic Optimization"},{"task_slug":"sand","task_name":"Sand"}],"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}