| 基于高斯扰动的粒子群优化算法 |
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| 引用本文： | 朱德刚,孙 辉,赵 嘉,余 庆.基于高斯扰动的粒子群优化算法[J].计算机应用,2014,34(3):754-759. |
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| 作者姓名： | 朱德刚 孙 辉 赵 嘉 余 庆 |
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| 作者单位： | 1. 南昌航空大学 信息工程学院，南昌330063
2. 南昌工程学院 信息工程学院，南昌330099 |
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| 基金项目： | 国家自然科学基金资助项目;江西省自然科学基金资助项目;江西省自然科学基金资助项目;江西教育厅科技项目 |
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| 摘 要： | 针对标准粒子群优化(PSO)算法易陷入局部最优、进化后期收敛速度慢和收敛精度低的缺点，提出一种基于高斯扰动的粒子群优化算法。该算法采用对粒子个体最优位置加入高斯扰动策略，有效地防止算法陷入局部最优，加快收敛并提高收敛精度。在固定评估次数的情况下，对8个常用的经典基准测试函数在30维上进行了仿真。实验结果表明，所提算法在收敛速度和寻优精度上优于一些知名的粒子群优化算法。
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| 关 键 词： | 粒子群优化算法 高斯扰动 快速收敛 全局搜索 |
| 收稿时间： | 2013-08-12 |
| 修稿时间： | 2013-10-30 |
Particle swarm optimization algorithm based on Gaussian disturbance |
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| ZHU Degang SUN Hui ZHAO Jia YU Qing.Particle swarm optimization algorithm based on Gaussian disturbance[J].journal of Computer Applications,2014,34(3):754-759. |
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| Authors: | ZHU Degang SUN Hui ZHAO Jia YU Qing |
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| Affiliation: | 1. School of Information Engineering, Nanchang Hangkong University, Nanchang Jiangxi 330063, China;
2. School of Information Engineering, Nanchang Institute of Technology, Nanchang Jiangxi 330099, China |
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| Abstract: | As standard Particle Swarm Optimization (PSO) algorithm has some shortcomings, such as getting trapped in the local minima, converging slowly and low precision in the late of evolution, a new improved PSO algorithm based on Gaussian disturbance (GDPSO) was proposed. Gaussian disturbance was put into in the personal best positions, which could prevent falling into local minima and improve the convergence speed and accuracy. While keeping the same number of function evaluations, the experiments were conducted on eight well-known benchmark functions with dimension of 30. The experimental results show that the GDPSO algorithm outperforms some recently proposed PSO algorithms in terms of convergence speed and solution accuracy. |
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| Keywords: | Particle Swarm Optimization (PSO) algorithm Gaussian disturbance fast convergence global search |
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