| 粒子群优化算法的改进 |
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| 引用本文： | 任小波,杨忠秀.粒子群优化算法的改进[J].计算机工程,2010,36(7):205-207. |
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| 作者姓名： | 任小波 杨忠秀 |
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| 作者单位： | 宁波工程学院电子与信息工程学院,宁波,315016 |
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| 基金项目： | 宁波市自然科学基金资助项目(2008A610002,2009A610090);;浙江教育厅基金资助项目(Y200803228) |
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| 摘 要： | 针对粒子群优化算法搜索精度不高、对高维函数优化性能不佳的问题，提出一种改进的粒子群优化算法。以递增方式对粒子进行释放增强可利用的种群信息，通过释放粒子引导极值变化加强算法的运算效率。实验结果表明，与其他算法相比，改进算法具有更强的寻优能力和搜索精度，且适于高维复杂函数的优化。
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| 关 键 词： | 粒子群优化 大规模函数优化 释放粒子 极值变化 |
| 修稿时间： | |
Improvement of Particle Swarm Optimization Algorithm |
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| REN Xiao-bo,YANG Zhong-xiu.Improvement of Particle Swarm Optimization Algorithm[J].Computer Engineering,2010,36(7):205-207. |
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| Authors: | REN Xiao-bo YANG Zhong-xiu |
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| Affiliation: | (College of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315016) |
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| Abstract: | Aiming at the problem that searching precision of Particle Swarm Optimization(PSO) is low and optimized performance is not well for high-dimension function, this paper proposes an improved PSO algorithm. The algorithm uses an orderliness increasing mode to set particle free, enhances the useful population information, leads extreme change through release particle to strengthen computational efficiency of algorithm. Experimental results show that improved algorithm has more powerful optimizing ability and higher optimizing precision compared with other algorithms. |
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| Keywords: | Particle Swarm Optimization(PSO) large-scale function optimization release particle extreme change |
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