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Analysis of the effects of the random weights of particle swarm optimization

Like other evolutionary optimization algorithms, the particle swarm optimization uses the random parameters/weights to achieve good optimization performance. Since the random parameters play important roles in the optimization procedure, it is necessary to investigate the effects of the random weights on the optimization performance. This paper investigated the effects of the random parameters on the optimization performance based on simulations. The simulation results show that the different choices of random weights have big effects on the optimization performance, especially for high-dimensional optimization problems. For the high-dimensional optimization problems, the optimization performance is better if more components of the random vectors are same with each other.