趋同(经济学)
水准点(测量)
人口
数学优化
帕累托原理
计算机科学
算法
多样性(政治)
多目标优化
数学
人口学
大地测量学
社会学
经济增长
人类学
经济
地理
作者
Wenji Li,Yi‐Feng Qiu,Zhaojun Wang,Qinchang Zhang,Ruitao Mai,Biao Xu,Yue Zhang,Jiafan Zhuang,Zhun Fan
标识
DOI:10.1109/docs60977.2023.10294499
摘要
Many real-world optimization problems can be formulated as a kind of constrained multi-objective optimization problems (CMOPs). The main difficulty in solving these problems is to take feasibility, convergence and diversity into account simultaneously. To address this issue, this paper proposes a push and pull search algorithm based on early convergence followed by diversity (PPS-CFD). The proposed algorithm is composed of three different stages, each respectively focusing on convergence, diversity and feasibility. In the first stage, the population rapidly converges to the unconstrained Pareto front (UPF) in $M$ directions, where $M$ is the number of objectives of CMOPs. In the second stage, the population further converges to the UPF and meanwhile its diversity is enhanced. In the last stage, constraints are taken into account to pull the population from the UPF to the constrained Pareto front (CPF). In addition, a search strategy based on objective space division is proposed at the last two stages. Finally, the proposed PPS-CFD is tested on fourteen benchmark problems, compared with other six algorithms, to demonstrate its superiority.
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