[1]WU Changyou.An improved artificial fish swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(3):465-469.[doi:10.3969/j.issn.1673-4785.201404010]
Copy

An improved artificial fish swarm optimization algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 3 Page number: 465-469 Column: 学术论文—人工智能基础 Public date: 2015-06-25
Title:
An improved artificial fish swarm optimization algorithm
Author(s):
WU Changyou
School of Management Science and Engineering, Shandong Institute of Business And Technology, Yantai 264005, China
Keywords:
artificial fish swarm optimization algorithm; prey; swarm; follow; moving step length; mutation strategy
CLC:
TP18
DOI:
10.3969/j.issn.1673-4785.201404010
Abstract:
In this paper, the basic principles of artificial fish’s behaviors of prey, swarm, follow and bulletin board set were analyzed. Investigations were conducted to explore the reasons why it is difficult to produce the initial artificial fish swarm, and why it always falls into local optional solution. The proposed solution improves the artificial fish algorithm with the method of the produce of initial artificial fish swarm, in the artificial fish’s behaviors of prey, swarm and follow introduced the adaptive mobile step length with mutation strategy into the artificial fish at the same time, avoiding fish caught in local optima, improving the ability of global optimization. Finally, through the experiment of the 4 test functions concluded that as for the function of f1, f2 and f4, while the improved artificial fish swarm algorithm and artificial fish swarm algorithm have reached the optimal value, but the convergence of the improved artificial fish swarm algorithm is faster. As to the function of f3, the standard artificial fish swarm algorithm run in to the optimal solution in several times’ operation and the global optimal solution cannot be found. Therefore, the experiment shows the effectiveness and accuracy of the improved algorithm.
References:
[1] 李晓磊,钱积新.基于分解协调的人工鱼群优化算法研究[J]. 电路与系统学报, 2003, 8(1): 1-6.LI Xiaolei, QIAN Jixin. Studies on artificial fish swarm optimization algorithm based on decomposition and coordination techniques[J]. Journal of Circuits and Systems, 2003, 8(1): 1-6.
[2] 李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践, 2002, 22(11): 32-38.LI Xiaolei, SHAO Zhijiang, QIAN Jixin. An optimizing method based on autonomous animals fish-swarm algorithm[J]. Systems Engineering—Theory & Practice, 2002, 22(11): 32-38.
[3] 李晓磊,路飞,田国会,等.组合优化问题的人工鱼群算法应用[J].山东大学学报:工学版, 2004, 34(5): 64-67.LI Xiaolei, LU Fei, TIAN Guohui, et al. Applications of artificial fish school algorithm in combinatorial optimization problems[J]. Journal of Shangdong University: Engineering Science, 2004, 34(5): 64-67.
[4] 范玉军,王冬冬,孙明明.改进的人工鱼群算法[J]. 重庆师范大学学报, 2007, 24(3): 23-26.FAN Yujun, WANG Dongdong, SUN Mingming. Improved artificial fish-school algorithm[J]. Journal of Chongqing Normal University, 2007, 24(3): 23-26.
[5] 刘佳,刘丽娜,李靖.基于模拟退火算法的改进人工鱼群算法研究[J].计算机仿真, 2011, 28(10): 195-198.LIU Jia, LIU Lina, LI Jing. Research of improved artificial fish swarm algorithm based on simulated annealing algorithm[J]. Computer Simulation, 2011, 28(10): 195-198.
[6] 柳毅.求解模糊需求可回程取货车辆路径问题的改进工鱼群算法[J].模式识别与人工智能, 2010, 23(4): 560-564.LIU Yi. Improved artificial fish swarm algorithm for vehicle routing problem with backhaul and fuzzy demand[J]. Pattern Recognition & Artificial Intelligence, 2010, 23(4): 560-564.
[7] 张严,楚晓丽.一种改进的人工鱼群算法[J].计算机系统应用, 2011, 20(5): 199-201.ZHANG Yan, CHU Xiaoli. Advanced artificial fish swarm algorithm[J]. Computer Systems & Applications, 2011, 20(5): 199-201.
[8] 曲良东,何登旭.基于自适应高斯变异的人工鱼群算法[J].计算机工程, 2009, 35(15): 182-189.QU Liangdong, HE Dengxu. Artificial fish-school algorithm based on adaptive Gauss mutation[J]. Computer Engineering, 2009, 35(15): 182-189.
[9] 彭勇,唐国磊,薛志春.基于改进人工鱼群算法的梯级水库群优化调度[J].系统工程理论与实践, 2011, 31(6): 1118-1126.PENG Yong, TANG Guolei, XUE Zhichun. Optimal operation of cascade reservoirs based on improved artificial fish swarm algorithm[J]. Systems Engineering—Theory & Practice, 2011, 31(6): 1118-1126.
[10] SHEN Wei, GUO Xiaopen, WU Chao, et al. Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm[J]. Knowledge-Based Systems, 2011, 24(3): 378-385.
[11] WANG Cuiru, ZHOU Chunlei, MA Jianwei. An improved artificial fish-swarm algorithm and its application in feed-forward neural networks[C]//Proceedings of 2005 International Conference on Machine Learning and Cybernetics. Guangzhou, China, 2005, 5: 2890-2894.
[12] FARZI S. Efficient job scheduling in grid computing with modified artificial fish swarm algorithm[J]. International Journal of Computer Theory and Engineering, 2009, 1(1): 13-18.
[13] LUO Yi, ZHANG Juntao, LI Xinxin. The optimization of PID controller parameters based on artificial fish swarm algorithm[C]//2007 IEEE International Conference on Automation and Logistics. Ji’nan, China, 2007: 1058-1062.
[14] 吴昌友,王福林,马力.一种新的改进粒子群优化算法[J].控制工程, 2010, 17(5): 359-362.WU Changyou, WANG Fulin, MA Li. An improved particle swarm optimization algorithm[J]. Control Engineering of China, 2010, 17(5): 359-362.
Similar References:

Memo

-

Last Update: 2015-07-15

Copyright © CAAI Transactions on Intelligent Systems