关键词: 粒子群算法, 初始化策略, 动态调节, 惯性权重

Abstract: For the use of classical linear decreasing strategy to determine the inertia weight in the actual operation process optimization and particle nonlinear changes of mismatch, this paper proposes an improved particle swarm optimization. The algorithm uses the initial population policy position random initialization times, and then introduces random factor to inertia weight, based on particle fitness size dynamically adjusts inertia weight, better guides the search for particles, improves the convergence precision and proves it is capable of global convergence with probability 1. In order to verify the performance of the optimization algorithm, by 8 classic test functions, PSO, decreasing inertia weight particle swarm optimization and the proposed algorithm are tested and compared with different dimensions. The results show higher optimization accuracy of the algorithm.

Key words: particle swarm algorithm, initialization strategy, dynamic adjustment, inertia weight