A Cooperative Approach to Quantum-behaved Particle Swarm Optimization
Particle swarm optimization (PSO) algorithm was proposed by Kennedy and Eberhart in 1995, which can be used to solve a wide array of different optimization problem. The PSO idea is inspired by natural concepts such as fish schooling, bird flocking and human social relations. Some experimental results show that PSO has greater “global search ability, but the “local search ability around the optimum is not very good. In order to enhance the “local search ability of the traditional PSO, Sun et proposed Quantum-behaved Particle Swarm Optimization algorithm (QPSO), but the convergence of the particle in QPSO is limited. Then an improvement methods for the QPSO, that is, Cooperative Quantum-behaved Particle Swarm Optimization (CQPSO) algorithm, is introduced bydeeply analyzing the QPSO. Experiments for several benchmark problems show that CQPSO can overcome the fault of QPSO and increase the optimization power of the particle swarm.
Particle Swarm Optimization Cooperative Convergence Global Search Local Search Qutuam-behaved
Yan Kang Wenbo Xu Jun Sun
the School of Information Technology, Southern Yangtze University, Wuxi Jiangsu 214122, China
国际会议
武汉
英文
2007-09-21（万方平台首次上网日期，不代表论文的发表时间）