| 一种改进的微粒群优化算法 |
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| 引用本文： | 赵亚敏,许家栋.一种改进的微粒群优化算法[J].计算机工程与应用,2010,46(2):31-33. |
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| 作者姓名： | 赵亚敏 许家栋 |
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| 作者单位： | 西北工业大学 电子信息学院，西安 710129 |
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| 基金项目： | 陕西省自然科学研究计划资助项目(No.2006F15) |
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| 摘 要： | 基于惯性权重对微粒群优化算法（Particle Swarm Optimization，PSO）优化性能的显著影响，提出了一种改变惯性权重的方法以改进PSO算法的优化性能。算法中惯性权重的动态改变是通过对其进行PSO寻优来控制的。经过对标准函数的测试计算，无论是二维还是多维的问题，这种改变惯性权重的PSO算法的寻优结果的准确度和精度均得以提高，收到了良好的效果，尤其在高维情况下，显示出算法性能得到了明显改善。
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| 关 键 词： | 微粒群优化 惯性权重 算法性能 |
| 收稿时间： | 2009-1-17 |
| 修稿时间： | 2009-2-27
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Modified particle swarm optimization algorithm |
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| ZHAO Ya-min,XU Jia-dong.Modified particle swarm optimization algorithm[J].Computer Engineering and Applications,2010,46(2):31-33. |
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| Authors: | ZHAO Ya-min XU Jia-dong |
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| Affiliation: | School of Electronics and Information，Northwestern Polytechnical University，Xi’an 710129，China |
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| Abstract: | Based on the prominent effect of inertia weight to the performance of particle swarm optimization,a new PSO method with dynamically adjusted inertia weight is proposed. In this improved PSO method,the inertia weight is controlled by PSO optimization. Numerical results show the effectiveness of the proposed algorithm. Either to two dimension or to high dimension,both the accuracy and precision are improved. Especially for high dimension,performance of the algorithm is bettered distinctly. |
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| Keywords: | Particle Swarm Optimization(PSO) inertia weight optimization performance |
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