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Particle Swarm Optimization with Adaptive Parameters

Particle swarm optimization is an effective evolution algorithm for global optimizing. Based on analysis of particle movements during evolution, parameter p is brought up to control the value of C1 and C2, which effects convergence rate of PSO. Aiming at solving different problems, corresponding p is adopted to improve performance. Particle confidence coefficient q is applied to weigh proper emphasize on itself best solution and global solution. Adaptive value of q is introduced to PSO to satisfy specific situation for each particle. Finally, performance of PSO with parameters p and q is testified by optimizing benchmark functions.