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Syst."],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The number of solutions obtained is too large to provide a set of solutions with good performance in the nearby area of the true Pareto front when problem-specific preferences are unavailable. Therefore, this paper proposes a knee point-driven many-objective pigeon-inspired optimization algorithm (KnMAPIO). An environmental selection strategy based on knee-oriented dominance is proposed to improve selection pressure and population diversity. In addition, a new velocity updating equation with Gaussian distribution, Cauchy distribution and Levy distribution is proposed in this paper to provide new search directions and reduce the possibility of falling into local optima. Two types of experiments are carried out in this paper: one is to compare the proposed method with four other algorithms on the knee-oriented benchmark PMOPs to verify the algorithm\u2019s performance in detecting the knee points and the knee region; another is to compare the proposed method with eight other state-of-the-art algorithms on the classic benchmark DTLZ and WFG. The results of both experiments verify the effectiveness of the proposed algorithm and the ability to approximate to the true Pareto front.<\/jats:p>","DOI":"10.1007\/s40747-022-00706-9","type":"journal-article","created":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T07:02:46Z","timestamp":1648710166000},"page":"4277-4299","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A knee point-driven many-objective pigeon-inspired optimization algorithm"],"prefix":"10.1007","volume":"8","author":[{"given":"Lihong","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Yeqing","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Youqian","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Zhihua","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Wensheng","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"issue":"2","key":"706_CR1","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MNET.011.2000331","volume":"35","author":"ZH Cui","year":"2021","unstructured":"Cui ZH, Zhao YR, Cao Y, Cai XJ, Zhang WS, Chen JJ (2021) Malicious code detection under 5G HetNets based on multi-objective RBM model. 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