Abstract
Both Particle Swarm Optimization (PSO) and its improved version of Quantum-behaved Particle Swarm Optimization (QPSO) are the novel swarm intelligence optimization algorithms. However, the above two algorithm makes the search process easy to fall into local optimum and premature convergence because of the existence of particle waiting effect. For overcoming the shortcoming of QPSO and improving its search ability, considering that the feature of Levy flight is different that of Brown motion used by QPSO, we proposed a method of based on Levy flight quantum-behaved particle swarm optimization (LFQPSO). In order to test the performance of the proposed algorithm in our work, we apply it to the benchmark function test and compare it with standard PSO and QPSO algorithm, which shows that the LFQPSO outperforms the standard PSO and QPSO algorithm.
