Abstract: Concerning the premature convergence problem in Particle Swarm Optimization (PSO) algorithm, a Diversity Feedback and Control PSO (DFCPSO) algorithm was proposed. In the process of search, the algorithm dynamically adjusted the algorithm parameters according to the feedback information of diversity; as a result, the distribution of iterations in the diversity curve was improved. When the population diversity or the variance of the population's fitness dropped to the given thresholds, the proposed algorithm would let the particle swarm initialize based on the repulsion of the global best position and fly away from the gathering area efficiently to search again, hence the diversity was controlled in a reasonable range, which avoided premature convergence. The experimental results on several well-known benchmark functions show that DFCPSO has stronger global optimization ability in the complicated problems and multi-modal optimization when being compared with the existing Diversity-Controlled PSO (DCPSO).

Key words: Particle Swarm Optimization (PSO), premature convergence, diversity, global convergence

摘要： 针对粒子群优化(PSO)算法的早熟收敛问题，提出了一种多样性反馈与控制的粒子群优化 (DFCPSO)算法。该算法在搜索过程中根据多样性反馈信息，动态调整算法参数，改善了搜索次数在多样性曲线上的分布情况。当多样性或群体适应度方差下降到给定的阈值时，通过基于最优点排斥的初始化操作，高效率发散，使粒子飞离聚集区域，重新开始搜索，从而使种群多样性保持在合理范围内，避免了早熟收敛现象。对多个标准测试函数的实验结果表明，与当前多样性控制的粒子群优化(DCPSO)算法相比，DFCPSO算法在复杂优化问题和多模态优化问题中具有更强的全局搜索能力。

关键词: 粒子群优化, 早熟收敛, 多样性, 全局最优