关键词: 粒子群优化, 早熟收敛, 多样性, 全局最优

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