| 基于蚁群信息机制的粒子群算法 |
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| 引用本文： | 段玉红,高岳林.基于蚁群信息机制的粒子群算法[J].计算机工程与应用,2008,44(31):81-83. |
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| 作者姓名： | 段玉红 高岳林 |
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| 作者单位： | 1.宁夏大学 数学与计算机学院，银川 750021 2.北方民族大学 信息与系统科学研究所，银川 750021 |
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| 基金项目： | 国家社会科学基金，宁夏自然科学基金 |
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| 摘 要： | 针对粒子群算法应用于复杂函数优化时可能出现过早收敛于局部最优解的情况，提出了一种改进的算法。通过构造单个粒子的多个进化方向和类似于蚂蚁群算法信息素表的选择机制，保留了粒子的多种可能进化方向。提高了粒子间的多样性差异，从而改善算法能力。改进后的混合粒子群算法的性能优于带线性递减权重的粒子群算法。
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| 关 键 词： | 粒子群优化 蚁群算法 局部搜索 |
| 收稿时间： | 2007-12-7 |
| 修稿时间： | 2008-2-25
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Particle swarm optimization algorithm based on information of ant colony |
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| DUAN Yu-hong,GAO Yue-lin.Particle swarm optimization algorithm based on information of ant colony[J].Computer Engineering and Applications,2008,44(31):81-83. |
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| Authors: | DUAN Yu-hong GAO Yue-lin |
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| Affiliation: | 1.School of Mathematics and Computer，Ningxia University，Yinchuan 750021，China 2.Research Institute of Information and System Computation Science，North National University，Yinchuan 750021，China |
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| Abstract: | To improve the PSO algorithm which is a new population based optimization algorithm against trapping into local minima,a hybrid PSO algorithm combing ant colony strategy with PSO(APSO) is presented.In APSO some potential evolution directions are constructed for each particle in PSO,at the same time a strategy is presented to choose which one may be the local best for PSO evolution process just like pheromone table in ant colony algorithm.It is shown by tested with well-known benchmark functions that APSO algorithms are better than PSO algorithms with linearly decreasing weight. |
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| Keywords: | Particle Swarm Optimization(PSO) ant colony algorithm local searching |
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