Abstract: To avoid the premature convergence or stagnation during particle swarm optimization process, on the basis of Fully-Informed Particle Swarm Optimization（FIPSO） in conjunction with social psychological principles, the novel Adaptive Partly Informed Particle Swarm Optimization（API-PSO） algorithm is proposed. In API-PSO algorithm, a particle adaptively selects swarm-shared experience according to performances of its neighboring particles. Experiment results show that API-PSO offers satisfactory convergence accuracy and speed. Probabilistic analysis on swarm diversity and convergence analysis of API-PSO is conducted which serves as the theoretic basis to select the algorithm parameters, solve swarm diversity lack, facilitate swarm evolution development and improve algorithm performance.

Key words: swarm intelligence, Particle Swarm Optimization（PSO）, premature convergence, population diversity

摘要： 针对粒子群优化过程中容易出现早熟收敛或停滞的问题，在全信息粒子群优化（FIPSO）算法的基础上结合社会心理学原理提出了一种新的粒子群优化算法——自适应信息选择粒子群优化算法（API-PSO）。在API-PSO算法中，粒子根据其邻域粒子不同表现，自适应地选择群体共享经验。实验表明，新的优化算法具有较好的收敛精度和收敛速度。分别对API-PSO算法的种群多样性和收敛性进行了数学分析，分析结果为合理选择算法参数，解决算法种群多样性匮乏，促进种群进化发展，改善算法性能提供了理论依据。

关键词: 群体智能, 粒子群优化, 早熟收敛, 种群多样性