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改进粒子群优化算法求解旅行商问题 被引量：23

Modified particle swarm optimization algorithm for traveling salesman problem
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摘要 提出了一种改进粒子群学习算法,在改进的算法中,粒子不仅根据自身和同伴中最好的个体调整自己的飞行速度,而且按照一定的概率向其他个体学习。这种强化后的学习行为更符合自然界生物的学习规律,更有利于粒子发现问题的全局最优解。同时借鉴单点调整算法思想,提出了调整因子和调整序概念用以重构粒子群算法。最后,用改进后的粒子群算法求解旅行商问题,数字仿真表明了算法的有效性。 A modified particle swarm optimization （MPSO）algorithm is proosed to solve the traveling salesman problem（TSP）.The concepts of adjustment operator and adjustment sequence are introduced to reconstruct particle swarm optimizaton（PSO）algorithm by using the ideas of single node regulating algorithm.Numerical simulation results show the effectiveness of the proposed method.
作者 王翠茹 张江维 王玥 衡军山
机构地区 华北电力大学计算机科学与技术学院 河北省电力设计院
出处 《华北电力大学学报（自然科学版）》 CAS 北大核心 2005年第6期47-51,59,共6页 Journal of North China Electric Power University：Natural Science Edition
基金 华北电力大学青年教师基金项目(522-93207401).
关键词 粒子群优化算法 旅行商问题 组合优化 NP问题 particle swarm optimization algorithm traveling salesman problem combination optimization NP problem
分类号 TP31 [自动化与计算机技术—计算机软件与理论]
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参考文献11

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二级参考文献21

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共引文献69

同被引文献175

引证文献23

二级引证文献112

华北电力大学学报（自然科学版）
2005年 第6期

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