中文摘要：

针对量子粒子群优化算法（QPSO）存在着保持种群多样性差、容易陷入局部最优等缺陷,将耗散操作算子引入到QPSO量子角度更新中,提出了改进的耗散量子粒子群优化算法（DQPSO）。为验证算法的有效性,将DQPSO算法应用于标准函数优化问题。仿真结果表明,改进的耗散量子粒子群算法的优化性能优于传统的量子进化算法（QEA）和QPSO算法。可见,在量子角度更新策略中引入耗散操作算子能够使算法更好地保持种群的多样性、摆脱局部最优的限制、提高算法的搜索能力。

英文摘要：

As quantum particle swarm optimization（ QPSO） existing the defects that can’t keep the diversity of the swarm much better and easy to local the optimum,to overcome the weaknesses,dissipation operator was induced into the quantum angle update strategy,and then proposed a modified dissipation quantum particle swarm optimization（ DQPSO） . The simulated results in solving benchmark function optimization problems show that DQPSO is superior to traditional quantum evolutionary algorithm （ QEA） and QPSO. We can conclude that dissipation operator is valid for keeping the diversity of the swarm and avoiding the algorithm lie in the local optimum and improving the search ability of the algorithm.