改进的量子粒子群多目标优化算法  

Quantum particle swarm optimization method for multi-objective optimization

作　　者：许昆[1] 李智勇[1] 

机构地区：[1]湖南大学计算机与通信学院,湖南长沙410082

出　　处：《计算机工程与设计》2009年第1期164-167,178,共5页Computer Engineering and Design

基　　金：国家自然科学基金项目(60373062)

摘　　要：针对粒子群优化算法容易陷入局部极值点的问题,提出了一种新的量子比特粒子群算法,该算法采用Pareto支配关系来更新粒子的个体最优值和局部最优值;定义极大极小距离,并采用该距离方法裁减非支配解。实验结果表明该算法能更好地接近Pareto前沿且具有更好的分布性,更适合于求解复杂高维优化问题,是一种非常有潜力的多目标优化方法。To improve performance of original particle swarm optimization algorithm and avoid trapping to local excellent situations, a quantum-bit particle swarm optimization （QBPSO） algorithm for multi-objective optimization problems is presented. QBPSO adopts the non-dominated storing method for solutions population and use a new population diversity preserving strategy which is based on the Pareto max-rain distance. The multidimensional 0-1knapsack problems are tested and the results show that the proposed method can efficiently find Pareto optimal solutions that are closer to Pareto font and better on distribution. Especially, this proposed method is outstanding on more complex high-dimensional optimization problems.

关 键 词：粒子群优化 多目标优化 量子 多维0-1背包问题 极大极小距离 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]