关键词: 全局最优性, 动态粒子群算法(DPSO), 收敛性, 粒子群算法(PSO算法)

Abstract: Particle swarm optimization (PSO) is a new population-based intelligence algorithm and exhibits good performance on optimization. In fact, PSO is a random evolution algorithm. However, during the evolution of the algorithm, the magnitude of inertia weight has impact on the exploration and convergence of PSO, which is a contradiction. In this paper, a new PSO algorithm, called as DPSO, is proposed in which the inertia weight of every particle will be changed dynamically with the distance between the particle and the current optimal position. Experiments on benchmark functions show that DPSO outperform standard PSO

Key words: global optimality, DPSO, convergence, PSO