关键词: 量子行为粒子群优化, 分段Logistics映射, 变异

Abstract: In order to solve the premature convergence problem of Quantum-behaved Particle Swarm Optimization（QPSO）, a logistics Chaotic Mutation Quantum-behaved Particle Swarm Optimization（CMQPSO） is presented. Particles in population are first initialized using segmental Logistics chaotic mapping, and then particles are divided into two sub population-top population and bottom population based on their fitness values. Particles in top population are scattered with Gaussian disturbance when particles accumulate to a certain degree. Particles in bottom population are chosen by mutation probability and mutated with Logistics chaotic mapping, which in return, improve diversity of particles. Algorithm’s local and global search performance are well balanced with the introduction of mutation mapping and division of population. Results on Benchmark functions show that the proposed algorithm shows better search and convergence performance than standard QPSO and other algorithms. Effects of stagnation limit [Cσ] and proportion coefficient [S] on algorithm’s performance are analyzed in detail. And rational scope of the parameters is determined.

Key words: Quantum-behaved Particle Swarm Optimization（QPSO）, segmental Logistics mapping, mutation