| 标准粒子群优化算法的收敛性分析 |
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| 引用本文： | 傅阳光,周成平,丁明跃.标准粒子群优化算法的收敛性分析[J].应用数学,2011,24(1). |
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| 作者姓名： | 傅阳光 周成平 丁明跃 |
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| 作者单位： | 1. 华中科技大学图像识别与人工智能研究所,湖北,武汉,430074
2. 华中科技大学图像识别与人工智能研究所,湖北,武汉,430074;华中科技大学生命科学与技术学院,湖北,武汉,430074 |
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| 基金项目： | Supported by National Natural Science Foundation of China (60672057,60471012) |
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| 摘 要： | 研究标准粒子群优化算法在经验区域的各个子区域内的收敛和发散行为,分析系统特征根与算法参数的关系,得到一系列结论.数值仿真实验展示不同子区域内的算法参数对粒子位置和粒子速度运动轨迹的不同影响,进一步验证本文结论的正确性.
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| 关 键 词： | 标准粒子群优化 粒子收敛 参数选择 特征根 数值仿真 |
Convergence Analysis of Standard Particle Swarm Optimization Algorithm |
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| FU Yangguang,ZHOU Chengping,DING Mingyue.Convergence Analysis of Standard Particle Swarm Optimization Algorithm[J].Mathematica Applicata,2011,24(1). |
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| Authors: | FU Yangguang ZHOU Chengping DING Mingyue |
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| Institution: | FU Yangguang1,ZHOU Chengping1,DING Mingyue1,2(1.Institute for Pattern Recognition and Artificial Intelligence,Huazhong University of Science and Technology,Wuhan 430074,China,2.College of Life Science and Technology,China) |
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| Abstract: | The convergence and divergence properties of the standard particle swarm optimization algorithm are studied in detail. At the same time,the relationship between the characteristic roots and algorithm parameters are analyzed. A series of conclusions are deduced.Finally,numerical simulation demonstrates the different effects of different algorithm parameters on the loci of particle position and particle velocity,which further illustrates the validity of conclusions given in this paper. |
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| Keywords: | Standard particle swarm optimization Particle convergence Parameter-selection Characteristic root Numerical simulation |
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