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A modified particle swarm optimizer based on cloud model

In this paper, we introduce cloud model theory to the particle swarm optimization algorithm to improve the global search ability and make a faster convergence speed of the algorithm. Some modifications are presented. First, we adopt cloud model to initialize the positions and velocities for entire population in the initialization range. Second, inertia weight is dynamically, nonlinearly decreased as the search progresses by using the data set, which can be obtained by cloud model. Third, two random variants in the velocity rule are assigned with cloud model. Four, inertia weight and the two random variants are correlated by cloud model. The modified particle swarm optimization is tested on some benchmark functions and the results are compared with the result of the standard particle swarm optimization. Experimental results indicate that the modified particle swarm optimization outperforms the standard particle swarm optimization in the global search ability with a quicker convergent speed.