| 改进型粒子蚁群算法的应用研究 |
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| 引用本文： | 高博,卢辉斌.改进型粒子蚁群算法的应用研究[J].计算机安全,2010(11):11-13. |
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| 作者姓名： | 高博 卢辉斌 |
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| 作者单位： | 燕山大学信息与工程学院; |
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| 摘 要： | 粒子蚁群算法综合了蚁群算法和粒子群算法的特点,在局部最优和全局最优解之间取得平衡。新算法在蚂蚁迭代过程中,每隔一定代数将数据引入粒子群运算以提高收敛速度。根据对TSP的eil51问题进行仿真结果可以看出,与通常蚁群算法相比,该算法不仅精度上较为满意,而且效率极高,具有良好的应用前景。
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| 关 键 词： | 蚁群算法 粒子群算法 精英策略 旅行商问题 |
Study on the Application of Improved Ant Colony Optimization with Particle Swarm Optimization |
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| GAO Bo,LU Hui-bin.Study on the Application of Improved Ant Colony Optimization with Particle Swarm Optimization[J].Network & Computer Security,2010(11):11-13. |
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| Authors: | GAO Bo LU Hui-bin |
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| Affiliation: | GAO Bo1,LU Hui-bin2(School of Information and Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China) |
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| Abstract: | Ant Colony Algorithms Optimization(ACO) combined with Particle swarm optimization(PSO) integrates each advantage,finds the balance between the local and global best solutions.In every a few generations of ACO,the new algorithm leads the data into PSO in order to accelerate the convergence. The simulation results of eil51 in TSP shows that,compared with the normal ACO,it has amazing efficiency under the appropriate precision and could be widely used |
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| Keywords: | ant colony algorithms particle swarm algorithms excellence mechanism TSP |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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