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

摘要： 在简要介绍基本PSO算法的基础上，提出一种根据不同粒子距离全局最优点的距离对基本PSO算法的惯性权重进动态调整的新型粒子群算法（DPSO），并对新算法进行描述。以典型优化问题的实例仿真验证了DPSO算法的有效性。

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)