| 一种改进的粒子群优化算法 |
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| 引用本文： | 洪贵汕,许波.一种改进的粒子群优化算法[J].茂名学院学报,2013(4):75-78. |
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| 作者姓名： | 洪贵汕 许波 |
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| 作者单位： | 广东石油化工学院计算机与电子信息学院,广东茂名,525000 |
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| 基金项目： | 广东高校石油化工故障诊断与信息化控制工程技术开发中心开放基金 |
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| 摘 要： | 为了克服传统粒子群算法（Pso）的早熟和局部最优问题，通过分析基于惯性权重的粒子群优化在粒子寻优过程中的可行性，提出了一种变惯性权重的改进PSo算法，并对经典的测试函数进行TN试。实验结果证明，与传统PSO算法以及基于惯性权重的PSO相比，改进算法的寻优效果较好，全局搜索能力有显著提高，并能有效地避免早熟收敛问题。
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| 关 键 词： | 粒子群优化算法 局部最优 群体智能 算法设计 |
An Improved Particle Swarm Optimization Algorithm |
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| HONG Gui-shan
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XU Bo.An Improved Particle Swarm Optimization Algorithm[J].Journal of Maoming College,2013(4):75-78. |
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| Authors: | HONG Gui-shan XU Bo |
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| Institution: | (College of Computer and Electronic Information,Guangdong University of Petrochemical Technology,Maoming 525000,China) |
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| Abstract: | In order to overcome the problems of the immmture and local optimization in traditional PSO, based on the inertia weight PSO and the limited particle optimization process, a variation of inertia weight improved PSO algorithm is proposed, with classic test functions. And the results prove that improved algorithm optimization is better in global search capability and pmature convergence prevention than the tra- ditional P~ algorithm and the inertia- weight- based PSO. |
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| Keywords: | Particle Swarm Optimization algorithm (PSO algorithm) local optimum swarm intelligence algorithm design |
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