| 基于粒子群优化的蚁群算法在TSP中的应用 |
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| 引用本文： | 柴宝杰,刘大为.基于粒子群优化的蚁群算法在TSP中的应用[J].计算机仿真,2009,26(8):89-91,136. |
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| 作者姓名： | 柴宝杰 刘大为 |
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| 作者单位： | 1. 牡丹江师范学院,黑龙江,牡丹江,157012
2. 中国石油信息技术服务中心,北京,100007 |
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| 摘 要： | 结合粒子群算法的问题,提出用混合蚁群算法来求解著名的旅行商问题.问题的核心是应用粒子群算法对蚁群算法的控制参数:启发式因子、信息素挥发系数、随机性选择阈值进行优化,以及运用蚁群系统算法寻找最短路径.新算法对于蚂蚁算法中的参数调整大大减低,减少了大量盲目的实验,力求在开发最优解和探究搜索空间上找到平衡点.对旅行商问题的仿真实验表明,新算法的优化质量和效率都优于传统蚁群算法和遗传算法,接近理论最佳值.新算法也可推广用于其他NP问题的求解.
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| 关 键 词： | 蚁群算法 蚁群系统 粒子群算法 旅行商问题 |
Application of an Ant Colony Algorithm in TSP Based on Particle Swarm |
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| CHAI Bao-jie,LIU Da-wei.Application of an Ant Colony Algorithm in TSP Based on Particle Swarm[J].Computer Simulation,2009,26(8):89-91,136. |
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| Authors: | CHAI Bao-jie LIU Da-wei |
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| Affiliation: | 1.Mudanjian Teacher's College;Mudanjiang Heilongjiang 157012;China;2.China Petroleum Information Technology Service Center;Beijing 100007;China |
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| Abstract: | Combined with the idea of the particle swarm optimization(PSO) algorithm,the ant colony optimization(ACO) algorithm is presented to solve the well known traveling salesman problem(TSP).The core of this algorithm is using PSO to optimize the control parameters of ACO which consist of heuristic factor,pheromone evaporation coefficient and the threshold of stochastic selection,and applying ant colony system to routing.The new algorithm effectively overcomes the influence of control parameters of ACO and decrea... |
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| Keywords: | Ant colony algorithm Ant colony system Particle swarm algorithm Traveling salesman problem |
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