| 一种人工鱼群混合智能优化算法 |
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| 引用本文： | 杨增桥,刘弘,王爱霖.一种人工鱼群混合智能优化算法[J].山东师范大学学报(自然科学版),2013(3):20-23,29. |
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| 作者姓名： | 杨增桥 刘弘 王爱霖 |
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| 作者单位： | [1]山东师范大学信息科学与工程学院,济南250014 [2]山东省分布式计算机软件新技术重点实验室,济南250014 |
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| 基金项目： | 国家自然科学基金资助项目（60970004;61272094）;国家教育部博士点基金资助项目（20093704110002）;山东省自然科学基金资助项目（ZZ2008G02,ZR2010lQIJ011）;山东省重点实验室项目. |
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| 摘 要： | 针对人工鱼群算法一般在初期拥有较快的收敛性，后期收敛较慢的特性，笔者提出一种改进的人工鱼群算法——GPAFSA．该算法将杂交PSO算法引入到人工鱼群算法中，在人工鱼群算法陷入局部最优时，通过使用杂交PSO算法，克服陷入局部最优的缺陷，实现全局最优．仿真实验表明，该算法在收敛性、全局寻优方面比原始算法有很大提高．
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| 关 键 词： | 人工鱼群算法 杂交PSO算法 混合智能优化算法 |
AN ARTIFICIAL FISH SWARM MIXED INTELLIGENT OPTIMIZATION ALGORITHMS |
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| Yang Zengqiao Liu Hong Wang Ailin.AN ARTIFICIAL FISH SWARM MIXED INTELLIGENT OPTIMIZATION ALGORITHMS[J].Journal of Shandong Normal University(Natural Science),2013(3):20-23,29. |
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| Authors: | Yang Zengqiao Liu Hong Wang Ailin |
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| Institution: | Yang Zengqiao Liu Hong Wang Ailin ( 1 ) School of Information Science and Engineering;Shandong Normal University,250014, Jinan, China; 2) Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Shandong Normal University, 250014, Jinan,China ) |
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| Abstract: | The artificial fish swarm algorithm （AFSA） converges fast in the early stage while slowly in the late stage. In order to solve this problem, this paper proposes an improved artificial fish swarm algorithm - GPAFSA. The GPAFSA introduces hybrid PSO algorithm into the AFSA. When the AFSA traps into a local optimum, it uses the hybrid PSO algorithm to overcome the defects of optimum. The simulation shows that the algorithm has compared to the original algorithm. falling into a local optimum in order to achieve a global a better performance in convergence and global optimization |
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| Keywords: | artificial fish swarm algorithm hybrid particle swarm optimization algorithm （HPSO） hybrid intelligent optimization algorithm |
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