North University of China/Department of Computer Science and Technology, Taiyuan, China

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Abstract—Based on the classical particle optimization algorithm and the quantum behavioral theory, this paper proposes an improved QPSO algorithm---GLQPSO to perfect the global and local convergence speed ability and speed of classical particle swarm. To achieve this purpose, the author introduces an improved Logistic chaotic mapping theory [1] to conduct chaotic search for the initial particle and chaotic remolding of the locally optimized particle swarm. The test of the classical function has proved the success of this effort.

Index Terms—Terms—quantum behavior, PSO algorithm, chaotic mapping thoughts, local search

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Cite: Yan-xia JIN, Jing XUE, Zhi-bin SHI, "An Improved Algorithm of Quantum Particle Swarm Optimization," Journal of Software vol. 9, no. 11, pp. 2789-2795, 2014.