| 基于最速下降最优解参考的粒子群算法 |
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| 引用本文： | 李灏,丁晓东.基于最速下降最优解参考的粒子群算法[J].计算机工程与应用,2006,42(16):44-45. |
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| 作者姓名： | 李灏 丁晓东 |
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| 作者单位： | 东华大学应用数学系,上海,200051 |
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| 摘 要： | 将现代智能优化算法(微粒群算法)和最速下降法有机的结合起来,构造出一种混合优化算法,该算法既有微粒群算法的优点,又有最速下降的较高收敛性和精度,数值计算表明算法对于求解连续可微函数的全局优化问题是非常有效的。
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| 关 键 词： | 智能优化算法 最速下降法 连续可微函数 全局最优 混合算法 |
| 文章编号： | 1002-8331-（2006）16-0044-02 |
| 收稿时间： | 2005-09 |
| 修稿时间： | 2005-09 |
Steepest-Descent Optimization Swarm Reference Based PSO Algorithm |
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| Li Hao,Ding Xiaodong.Steepest-Descent Optimization Swarm Reference Based PSO Algorithm[J].Computer Engineering and Applications,2006,42(16):44-45. |
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| Authors: | Li Hao Ding Xiaodong |
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| Affiliation: | Department of Applied Mathematics, Donghua University, Shanghai 200051 |
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| Abstract: | Based on steepest descent algorithm and modern intelligent algorithm(particle swarm algorithm),this paper pro-poses a hybrid algorithm.The paper has not only the fast convergence of the steepest descent algorithm and precision,but also the global convergence of particle swarm algorithm.Numerical experiments have proved that this hybrid algorithm is very reasonable. |
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| Keywords: | modern intelligent algorithm steepest descent algorithm continuous-differential function global convergence hybrid algorithm |
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