关键词: 粒子群优化算法, 多维异步, 随机扰动, 惯性权重

Abstract: Particle swarm optimization has the disadvantages of being easily trapped into a local optimal solution and searching with lower efficiency in multi-dimensional space. With reference to the strategy of concave function to the inertia weight, the authors proposed a method of multidimensional asynchronism and stochastic disturbance to improve the ability to search for global optimum as well as solve the limitation of dimensionality problem. The experimental results of four classic benchmark functions show that the algorithm can keep the balance between the global search and local search, which effectively improves the success probability of searching with higher precision.

Key words: Particle Swarm Optimization algorithm (PSO), multidimensional asynchronism, stochastic disturbance, inertia weight