信息与控制2016，Vol.45Issue(2)：157-164,8.DOI:10.13976/j.cnki.xk.2016.0157
改进的量子行为粒子群优化算法及其应用
Improved Quantum-behaved Particle Swarm Optimization Algorithm and Its Application
摘要
Abstract
To enhance the performance of quantum-behaved particle swarm optimization,we propose an improved algorithm that uses quantum potential as well as an optimization mechanism and establishes the center of the potential well.In each iteration,we first calculate the fitness of each particle and then take the first K parti-cles with the greatest fitness as the candidate set.We use a roulette strategy to select a particle from the can-didate set as the center of the potential well and move other particles toward the center of the well.During op-timization,the K value is monotonically decreased to achieve a balance between exploration and exploitation. We apply the proposed approach in the extremum optimization of benchmark functions and weight optimization of a quantum-inspired neural network.Experimental results demonstrate that the optimization ability of the proposed algorithm is quite competitive with that of the original algorithm.关键词
量子计算/粒子群优化/轮盘赌策略/算法设计Key words
quantum computing/particle swarm optimization/roulette strategy/algorithm design分类
信息技术与安全科学引用本文复制引用
肖红,李盼池..改进的量子行为粒子群优化算法及其应用[J].信息与控制,2016,45(2):157-164,8.基金项目
国家自然科学基金资助项目（61170132）；黑龙江省自然科学基金资助项目（F2015021）；黑龙江省教育厅科学技术研究资助项目 （）
