| 基于吸引排斥机制的粒子群优化算法 |
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| 引用本文： | 赵鹏军,刘三阳,李超.基于吸引排斥机制的粒子群优化算法[J].计算机应用,2009,29(2):542-544. |
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| 作者姓名： | 赵鹏军 刘三阳 李超 |
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| 作者单位： | 商洛学院数学与计算科学系；西安电子科技大学理学院 西安电子科技大学 商洛学院 |
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| 摘 要： | 针对标准粒子群优化算法在处理复杂函数优化问题时容易陷入局部最优、收敛精度低的缺点,提出了一种改进的PSO算法，该算法把生物学中的吸引排斥思想引入到PSO算法中，充分利用粒子间的相互影响，修正了其速度更新公式，从而维持了群体的多样性，增强了粒子跳出局部最优解的能力。实验仿真结果表明,改进的PSO算法提高了进化后期的收敛速度，有效避免了PSO算法的早熟收敛问题，而且具有较高的收敛精度。
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| 关 键 词： | 粒子群优化 早熟收敛 吸引排斥机制 复杂函数 |
| 收稿时间： | 2008-08-13 |
Attraction-repulsion mechanism-based particle swarm optimization algorithm |
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| ZHAO Peng-jun,LIU San-yang,LI Chao.Attraction-repulsion mechanism-based particle swarm optimization algorithm[J].journal of Computer Applications,2009,29(2):542-544. |
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| Authors: | ZHAO Peng-jun LIU San-yang LI Chao |
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| Affiliation: | ZHAO Peng-jun1,2,LIU San-yang2,LI Chao11.Department of Mathematics,Shangluo University,Shangluo Shaanxi 726000,China,2.School of Science,Xidian University,Xi'an Shaanxi 710071 |
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| Abstract: | Standard Particle Swarm Optimization(PSO) algorithm falls into local optima easily and has low convergence accuracy when it is used to address the problem of complex functions optimization.In order to overcome the shortcomings,an improved PSO algorithm was proposed.The proposed algorithm integrated the attraction-repulsion mechanism in the field of biology into PSO algorithm and took full advantage of the mutual influence between particles to modify velocity updating formula,and thus maintained population d... |
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| Keywords: | particle swarm optimization premature convergence attraction-repulsion mechanism complex functions |
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