| 用自适应离散微粒群算法求解旅行商问题 |
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| 引用本文： | 何静,刘跃华,周伟林. 用自适应离散微粒群算法求解旅行商问题[J]. 计算技术与自动化, 2012, 31(1): 82-85 |
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| 作者姓名： | 何静 刘跃华 周伟林 |
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| 作者单位： | 湖南商学院计算机与电子工程学院,湖南长沙,410205 |
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| 基金项目： | 湖南省教育厅科研项目(11C0745);湖南省社科基金项目(11YBB229) |
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| 摘 要： | 提出一种求解GTSP问题的自适应离散PSO算法,同时考虑到多种算法的混合,利用调节算子和交换序对PSO算法进行改进.通过对Buramal14,Oliver30和Eil51等测试数据进行实验,证明新算法不仅收敛速度快、鲁棒性更好,而且新的算法对于Burma14和Oliver30更易求得它们的最优解。
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| 关 键 词： | 微粒群优化 自适应 旅行商问题 |
Study on Solving TSP Problem by Using Adaptive Discrete Particle Swarm Optimization |
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| HE Jing,LIU Yue-hu,ZHOU Wei-lin. Study on Solving TSP Problem by Using Adaptive Discrete Particle Swarm Optimization[J]. Computing Technology and Automation, 2012, 31(1): 82-85 |
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| Authors: | HE Jing LIU Yue-hu ZHOU Wei-lin |
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| Affiliation: | (School of Computer and Electronic Engineering,Hunan University of Commerce,Changsha 410205,China) |
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| Abstract: | An adaptive discrete particle swarm optimization(PSO) method is presented to solve the generalized traveling salesman problem(GTSP).New concepts of adjustment operator and adjustment sequence were proposed to rebuild PSO algorithm.In addition,improvement was done in considering the influence of individual’s cooperation.This algorithm is applied to Buramal14,Oliver30 and Eil51,numerical results show that the APSO algorithm not only converges quickly but also has a high robustness.It has a high probability to find the optimal solution for burma114 and Oliver30. |
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| Keywords: | particle swarm optimization adaptive traveling salesman problem |
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