Improved Quantum-behaved Particle Swarm Optimization Algorithm with Memory and Singal Step Searching Strategy for Continuous Optimization Problems

Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which has been applied widely for continuous optimization problems. In this paper, we propose an improved quantum-behaved particle swarm optimization with memory according to the means of best position of particles and using sigal step seaching strategy for sovle the multidimentional problem. At the same time, Gaussian distribution was used for the stochastic coefficients and uniformal distribution was used for the weight of all the best particles. The proposed improved QPSO is tested on several benchmark functions and compared with standard PSO, standard SFLA, RQPSO and WQPSO. The experiment results show the superiority of our aogorithm(called MSQPSO).

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