基于联合青睐概率的多目标粒子群优化算法研究
A Multi-Objective Particle Swarm Optimization Algorithm Based on Total Preferable Probability
摘要: 本文提出一种基于联合青睐概率精英竞争学习的多目标粒子群优化算法。该算法首先设计了一种联合青睐概率评价指标，将个体的收敛性信息与分布性信息进行综合度量，并根据当前种群的联合青睐概率选取粒子构成精英竞争组，用于提供高质量的学习对象。其次，构建了一种基于混合概率准则和参考向量关联的环境选择方案，用于协同改进算法的收敛性和多样性。数值实验表明所提出的算法具有很好的收敛性和多样性，在搜索效率方面表现良好，尤其在超多目标优化问题中显示出良好的潜力。
Abstract: This paper proposes a multi-objective particle swarm optimization algorithm based on elite competitive learning with total preferable probability. The proposed algorithm first develops a total preferable probability indicator to comprehensively evaluate the convergence and distribution characteristics of individuals. Based on the total preferable probabilities of the current population, a set of particles is selected to form an elite competition group, which provides high-quality learning exemplars for particle evolution. Furthermore, an environmental selection strategy combining total preferable probability criterion with reference-vector association is designed to achieve a coordinated improvement in convergence and diversity. Numerical experiments demonstrate that the proposed algorithm exhibits competitive convergence and diversity performance, together with favorable search efficiency, and shows particularly promising potential for solving many-objective optimization problems.
文章引用：赵杉杉, 冯敏. 基于联合青睐概率的多目标粒子群优化算法研究[J]. 应用数学进展, 2026, 15(10): 262-278. https://doi.org/10.12677/aam.2026.1510413

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