{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:06:09Z","timestamp":1767182769441,"version":"3.37.3"},"reference-count":30,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003488","name":"Henan Polytechnic University","doi-asserted-by":"publisher","award":["61872126"],"award-info":[{"award-number":["61872126"]}],"id":[{"id":"10.13039\/501100003488","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872126"],"award-info":[{"award-number":["61872126"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2021,9,6]]},"abstract":"<jats:p>Traditional fuzzy clustering is sensitive to initialization and ignores the importance difference between features, so the performance is not satisfactory. In order to improve clustering robustness and accuracy, in this paper, a feature-weighted fuzzy clustering algorithm based on multistrategy grey wolf optimization is proposed. This algorithm cannot only improve clustering accuracy by considering the different importance of features and assigning each feature different weight but also can easily obtain the global optimal solution and avoid the impact of the initialization process by implementing multistrategy grey wolf optimization. This multistrategy optimization includes three components, a population diversity initialization strategy, a nonlinear adjustment strategy of the convergence factor, and a generalized opposition-based learning strategy. They can enhance the population diversity, better balance exploration and exploitation, and further enhance the global search capability, respectively. In order to evaluate the clustering performance of our clustering algorithm, UCI datasets are selected for experiments. Experimental results show that this algorithm can achieve higher accuracy and stronger robustness.<\/jats:p>","DOI":"10.1155\/2021\/7387153","type":"journal-article","created":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T18:20:12Z","timestamp":1631038812000},"page":"1-13","source":"Crossref","is-referenced-by-count":2,"title":["A Feature Weighted Fuzzy Clustering Algorithm Based on Multistrategy Grey Wolf Optimization"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0540-865X","authenticated-orcid":true,"given":"Yongli","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7663-470X","authenticated-orcid":true,"given":"Zhonghui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6700-9446","authenticated-orcid":true,"given":"Hao","family":"Chao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, Henan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-015-0822-y"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2016.09.165"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1007\/s10639-015-9385-5"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1080\/03155986.2016.1262582"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/0098-3004(84)90020-7"},{"issue":"2","key":"6","article-title":"Fuzzy C-mean clustering based on particle swarm optimization","volume":"44","author":"L. 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