Abstract

The standard particle swarm optimization (for short, PSO) algorithm converges very fast, while it is very easy to fall into the local extreme point. With waiting effect among particles, the quantum-behaved particle swarm optimization (for short, QPSO) algorithm can prevent the particle prematurely from falling into local extreme point, but its convergence speed is slow. In this paper, a hybrid algorithm is proposed based on the advantages of PSO and QPSO (for short PSO-QPSO), in which the particle swarm is divided into double subgroups, and PSO and QPSO are used in each subgroup, and the integration of information between subgroups is realized by crossover operation. The testing functions show the PSO-QPSO's performance is far better than PSO and QPSO algorithms, and prove PSO-QPSO algorithm is feasible and effective.

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