随机性
粒子群优化
稳健性(进化)
计算机科学
数学优化
分歧(语言学)
公制(单位)
多群优化
进化算法
多目标优化
补偿(心理学)
数学
工程类
统计
化学
哲学
精神分析
基因
生物化学
语言学
运营管理
心理学
作者
Honggui Han,Hao Zhou,Yanting Huang,Ying Hou
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2024-02-01
卷期号:54 (2): 1062-1074
被引量:1
标识
DOI:10.1109/tcyb.2023.3336870
摘要
Multiobjective particle swarm optimization (MOPSO) has been proven effective in solving multiobjective problems (MOPs), in which the evolutionary parameters and leaders are selected randomly to develop the diversity. However, the randomness would cause the evolutionary process uncertainty, which deteriorates the optimization performance. To address this issue, a robust MOPSO with feedback compensation (RMOPSO-FC) is proposed. RMOPSO-FC provides a novel closed-loop optimization framework to reduce the negative influence of uncertainty. First, Gaussian process (GP) models are established by dynamically updated archives to obtain the posterior distribution of particles. Then, the feedback information of particle evolution can be collected. Second, an intergenerational binary metric is designed based on the feedback information to evaluate the evolutionary potential of particles. Then, the particles with negative evolutionary directions can be identified. Third, a compensation mechanism is presented to correct the negative evolution of particles by modifying the particle update paradigm. Then, the compensated particles can maintain the positive exploration toward the true PF. Finally, the comparative simulation results illustrate that the proposed RMOPSO-FC can provide superior search capability of PFs and algorithmic robustness over multiple runs.
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