{"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-systematic-and-meta-analysis-survey-of","title":"A Systematic and Meta-analysis Survey of Whale Optimization Algorithm","arxiv_id":"1903.08763","date":"2019-03-20","proceeding":null,"authors":["Hardi M. Mohammed","Shahla U. Umar","Tarik A. Rashid"],"abstract":"Whale Optimization Algorithm (WOA) is a nature-inspired meta-heuristic\noptimization algorithm, which was proposed by Mirjalili and Lewis in 2016. This\nalgorithm has shown its ability to solve many problems. Comprehensive surveys\nhave been conducted about some other nature-inspired algorithms, such as ABC,\nPSO, etc.Nonetheless, no survey search work has been conducted on WOA.\nTherefore, in this paper, a systematic and meta analysis survey of WOA is\nconducted to help researchers to use it in different areas or hybridize it with\nother common algorithms. Thus, WOA is presented in depth in terms of\nalgorithmic backgrounds, its characteristics, limitations, modifications,\nhybridizations, and applications. Next, WOA performances are presented to solve\ndifferent problems. Then, the statistical results of WOA modifications and\nhybridizations are established and compared with the most common optimization\nalgorithms and WOA. The survey's results indicate that WOA performs better than\nother common algorithms in terms of convergence speed and balancing between\nexploration and exploitation. WOA modifications and hybridizations also perform\nwell compared to WOA. In addition, our investigation paves a way to present a\nnew technique by hybridizing both WOA and BAT algorithms. The BAT algorithm is\nused for the exploration phase, whereas the WOA algorithm is used for the\nexploitation phase. Finally, statistical results obtained from WOA-BAT are very\ncompetitive and better than WOA in 16 benchmarks functions. WOA-BAT also\noutperforms well in 13 functions from CEC2005 and 7 functions from CEC2019.","url_abs":"http://arxiv.org/abs/1903.08763v3","url_pdf":"http://arxiv.org/pdf/1903.08763v3.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-systematic-and-meta-analysis-survey-of","repo_url":"https://github.com/Hardi-Mohammed/WOA-BAT-modification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"survey","task_name":"Survey"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"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}