| 人工鱼群与微粒群混合优化算法* |
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| 引用本文： | 姚祥光,周永权,李咏梅.人工鱼群与微粒群混合优化算法*[J].计算机应用研究,2010,27(6):2084-2086. |
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| 作者姓名： | 姚祥光 周永权 李咏梅 |
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| 作者单位： | 1. 广西大学,计算机与电子信息学院,南宁,530004
2. 广西民族大学,数学与计算机科学学院,南宁,530006 |
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| 基金项目： | 广西自然科学基金资助项目(0832082,0991086)；国家民委科研基金资助项目(08GX01) |
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| 摘 要： | 针对人工鱼群算法局部搜索不精确、微粒群优化算法易发生过早收敛等问题，提出一种新的人工鱼群与微粒群混合优化算法。算法的主要思想是先利用人工鱼群的全局收敛性快速寻找到满意的解域，再利用粒子群算法进行快速的局部搜索，所得混合算法具有局部搜索速度快，而且具有全局收敛性能。最后，以五个标准函数和一个应用实例进行测试，测试结果表明，提出的算法在一定程度上避免了陷入局部极小，加快了收敛速度且提高了搜索精度。
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| 关 键 词： | 微粒群算法 人工鱼群算法 混合算法 测试函数 |
Hybrid algorithm with artificial fish swarm algorithm and PSO |
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| YAO Xiang-guang,ZHOU Yong-quan,LI Yong-mei.Hybrid algorithm with artificial fish swarm algorithm and PSO[J].Application Research of Computers,2010,27(6):2084-2086. |
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| Authors: | YAO Xiang-guang ZHOU Yong-quan LI Yong-mei |
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| Affiliation: | (1.College of Computer & Electron Information, Guangxi University, Nanning 530004, China; 2. College of Mathematics & Computer Science, Guangxi University for Nationalities, Nanning 530006, China ) |
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| Abstract: | The artificial fish swarm algorithm (AFSA) has a stronger robustness, and has a imprecision of solution. The particle swarm optimization algorithm (PSO) is simple and effective, and is easy in premature convergence. This paper presented a new hybrid evolutionary algorithm with artificial fish swarm algorithm and particle swarm optimization. This algorithm has the advantages of both AFSA and PSO. First, algorithm looked for satisfactory solution space with AFSA, later its search exacted solution with PSO. By doing experiments on five benchmark functions and a applicable examples, the results show that the algorithm avoids trapping into local optimum in a certain extent and improves the precision of convergence. |
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| Keywords: | particle swarm optimization artificial fish swarm algorithm hybrid algorithm test functions |
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