Abstract:

Based on the cloud adaptive theory, the particle swarm optimization algorithm is improved and the particle swarm is divided into three populations. It  modifies the inertia weight using a cloud method, and meanwhile modifies the “social” and “cognitive” sections, and introduces the notion of mean, and proposes an improved cloud adaptive theory particle swarm optimization algorithm named MCAPSO. The greatest advantage of the method is that the algorithm in the later iteration, when the different value between an individual optimal to some particle corresponding of the fitness value and a global optimal corresponding to the fitness value is significant, overcomes the shortcoming that the algorithm does not benefit from converges to the optimal solution. Numerical experience shows that, MCAPSO runs  less iteration to find the optimal solution, and the average time is lower. The average time cost is reduced accordingly.

Key words: particle swarm optimization(PSO);mean;cloud theory;adaptive inertia weight adjusting