关键词: 粒子群优化算法, 惯性权值, 粒距, 动态区间

Abstract: The standard Particle Swarm Optimization (PSO) algorithm cannot adapt to the complex and nonlinear optimization process, because the same inertia weight is used to update the velocity of particles. In order to solve this problem, a strategy of inertia weight adjustment based on particle spacing and dynamic interval (PSSIW) was put forward. According to the particle spacing, the inertia weight was chosen, and the convergence rate of the algorithm were controlled by dynamic change of interval. Four different dynamic intervals were built in this paper. Sphere, Ackley and Rastrigrin functions were used to evaluate the intervals on the new PSO performance. Compared with the standard PSO algorithm, the new PSO algorithm has the ability to escape from the local minimum, so it is a global particle swarm optimization algorithm.

Key words: Particle Swarm Optimization (PSO) algorithm, inertia weight, particle spacing, dynamic interval