Author:
Zhao Lihong,Ren Yeqing,Zeng Youqian,Cui Zhihua,Zhang Wensheng
Abstract
AbstractThe 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’s 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.
Funder
National Natural Science Foundation of China
Shanxi Provincial Key Research and Development Project
National Key Research and Development Program of China
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
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