关键词: 粒子群优化算法, 混合蛙跳算法, 群体爬山策略, 全局收敛性, 函数优化

Abstract: Aiming at the disadvantages that the Particle Swarm Optimization（PSO） algorithm easily fall into local optimization and low search accuracy for solving high-dimensional and complex optimization problems, this paper proposes a hybrid particle swarm optimization algorithm, named CMCPSO, which borrows the idea of colony mountain climbing from Shuffled Frog Leaping Algorithm（SFLA）, and the global convergence of CMCPSO is also proved. Solving four typical high-dimensional continuous optimization functions shows that CMCPSO not only maintains the fast convergence of PSO algorithm, but also absorbs the advantages of SFLA algorithm for local refined search and maintaining the diversity of the population, it also has a good global convergence property.

Key words: Particle Swarm Optimization（PSO）, shuffled frog leaping algorithm, colony mountain climbing strategy, global convergence, function optimization