关键词: 粒子群优化算法, 梯度搜索, 繁殖法, 自适应, 惯性权重

Abstract: The paper proposes an improved adaptive Particle Swarm Optimization（PSO） algorithm which integrates gradient search method, breeding method and the all-time optimal location information of the first N particles. With chaotic inertia weight, the renewal of each particle’s speed and position is considered with not only the information of its own all-time and global optimal location information, but also the information of other all-time optimal location information, while the weights of other particles’ all-time optimal location information change adaptively with the number of iterations; meanwhile, particles location updates in their negative gradient direction and the particle locations increase linearly with iterations; when the particles stop updating, cross may be hold in local optimum position. The experimental results verify that the algorithm has better convergence speed and convergence precision than those relevant algorithms.

Key words: Particle Swarm Optimization（PSO） algorithm, gradient search, breeding method, self-adaption, inertia weight