{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:49:34Z","timestamp":1761598174474,"version":"3.37.3"},"reference-count":36,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T00:00:00Z","timestamp":1582156800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program Projects of China","award":["2018YFC1504700","2018JM6029"],"award-info":[{"award-number":["2018YFC1504700","2018JM6029"]}]},{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"publisher","award":["2018YFC1504700","2018JM6029"],"award-info":[{"award-number":["2018YFC1504700","2018JM6029"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2020,2,20]]},"abstract":"<jats:p>The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi\/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mi>\u03b8<\/mml:mi><\/mml:math>-dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances.<\/jats:p>","DOI":"10.1155\/2020\/5132803","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T18:34:31Z","timestamp":1582223671000},"page":"1-26","source":"Crossref","is-referenced-by-count":12,"title":["Multi\/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism"],"prefix":"10.1155","volume":"2020","author":[{"given":"Wusi","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information Technology and Software, Northwest University, Xi\u2019an 710127, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9550-9779","authenticated-orcid":true,"given":"Li","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Technology and Software, 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