Computer Science ›› 2017, Vol. 44 ›› Issue (4): 281-287.doi: 10.11896/j.issn.1002-137X.2017.04.058

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New Improved Artificial Fish Swarm Algorithm

LIU Dong-lin and LI Le-le   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Aiming at the problems of easy to fall into the local optimum value,converging slowly in the later period and low solving accuracy that artificial fish swarm algorithm(AFSA) have,a new improved artificial fish algorithm (IAFSA) was proposed. Firstly,the new algorithm uses chaos transform to initialize the position of individual fish,and the fish is more evenly distributed in the specified area within the region,that keeps the fish population diversity,and it is conducive to global convergence.Secondly,the artificial fish with different function values in foraging behavior take different visual,and it not only improves the searching speed but also reduces the possibility of the artificial fish falling into local optimum.Finally,according to the relationship between physical and the activity,a physical transformation mo-del is construct,and the physical of artificial fish become weaken in the late stage of the algorithm,reducing the step timely is very important,which can improve the convergence speed and the accuracy of the algorithm.The algorithm is verified by standard test function and TSP of 14 cities,and the experimental results show that the improved algorithm has faster convergence speed and higher precision than the basic artificial fish swarm algorithm.

Key words: Sartificial fish swarm algorithm,Chaos transform,Foraging behavior,Physical transformation model

[1] LI X L.A new kind of Intelligent Optimization Method - Artificial Fish Swarm Algorithm [D].Hangzhou:Zhejiang University,2003.(in Chinese) 李晓磊.一种新型的智能优化方法-人工鱼群算法[D].杭州:浙江大学,2003.
[2] ZHU X H,NI Z W,CHENG M Y.Improved Artificial Fish Swarm Algorithm with Variable Step Size[J].Computer Scien-ce,2015,42(2):210-216.(in Chinese) 朱旭辉,倪志伟,程美英.变步长自适应的改进人工鱼群算法[J].计算机科学,2015,42(2):210-216.
[3] ZHANG Y J,LI Z W,FENG Z H,et al.An Improved Artificial Fish Swarm Algorithm Based on Dynamic Parameter Adjustment[J].Journal of Hunan University(Natural Science Edition),2012,39(5):77-82.(in Chinese) 张英杰,李志武,奉中华.一种基于动态参数调整的改进人工鱼群算法[J].湖南大学学报(自然科学版),2012,39(5):77-82.
[4] SUN W J,LU Y L,SUN S B,et al.Artificial Fish Swarm Algorithm Based on Constrained Optimization Problem and its Improvement [J].Journal of Jilin Institute of Chemical Technology,2014,31(11):74-78.(in Chinese) 孙王杰,卢月亮,孙书贝,等.基于约束优化问题的人工鱼群算法及其改进[J].吉林化工学院学报,2014,31(11):74-78.
[5] CHENG Y M,JIANG M Y.Adaptive Resource Allocation forMulti Users OFDM System Based on Improved Artificial Fish Swarm Algorithm [J].Computer Application Research,2009,26 (6):2092-2094.(in Chinese) 程永明,江铭炎.基于改进人工鱼群算法的多用户OFDM系统自适应资源分配[J].计算机应用研究,2009,26(6):2092-2094.
[6] YANG Z Q,LIU H,WANG A L.A Hybrid Intelligent Optimization Algorithm of artificial fish Swarm[J].Journal of Shandong Normal University(Natural Science Edition),2013,28(3):20-23.(in Chinese) 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),2013,28(3):20-23.
[7] XIU C B,ZHANG Y H.Hybrid Optimization Algorithm Based on Ant Colony and Artificial Fish Swarm [J].Computer Engineering,2008,34(14):206-207.(in Chinese) 修春波,张雨虹.基于蚁群与鱼群的混合优化算法[J].计算机工程,2008,34(14):206-207.
[8] WANG Z J,YU Y,PENG P Z,et al.Application of Chaotic Search in Artificial Fish Swarm Algorithm [J].Industrial Control Computer,2015(4):83-85.(in Chinese) 王兆嘉,俞毅,彭培真,等.混沌搜索在人工鱼群算法中的应用[J].工业控制计算机,2015(4):83-85.
[9] QU L D,HE D X.A Chaotic Artificial Fish Swarm Optimization Algorithm [J].Computer Engineering and Applications,2010 (22):40-42.(in Chinese) 曲良东,何登旭.一种混沌人工鱼群优化算法[J].计算机工程与应用,2010(22):40-42.
[10] LIU L Z,ZHOU Y Q.A new Hybrid Global Optimization Algorithm Based on Artificial Fish Swarm and Culture Algorithm [J].Computer Application Research,2009,26(12):4446-4448.(in Chinese) 刘凌子,周永权.一种基于人工鱼群和文化算法的新型混合全局优化算法[J].计算机应用研究,2009,26(12):4446-4448.
[11] ZHANG J L ,ZHOU Y Q.An Artificial Glowworm Swarm Optimization AlgorithmBased on Powell Local Optimization Method[J].Pattern recognition and artificial intelligence,2011,24 (5):680-684.(in Chinese) 张军丽,周永权.一种用Powell方法局部优化的人工萤火虫算法[J].模式识别与人工智能,2011,24(5):680-684.
[12] MA X M ,LIU N.Adaptive Field of View Artificial Fish Swarm Algorithm for Solving Shortest Path Problem[J].Journal of communication,2014(1):1-6.(in Chinese) 马宪民,刘妮.自适应视野的人工鱼群算法求解最短路径问题[J].通信学报,2014(1):1-6.
[13] ZHANG C,ZHANG F M,LI F,et al.Improved artificial fish swarm algorithm [C]∥2014 IEEE 9th Conference on Industrial Electronics and Applications (ICIEA).IEEE,2014.
[14] LI Z P,WANG Y,ZHANG C Z.An Artificial Fish Swarm Algorithm Based on Dynamic Swimming Modes [J].Computer Simulation,2015,32(4):208-215.(in Chinese) 李志平,王勇,张呈志.一种采用动态游动模式的鱼群算法[J].计算机仿真,2015,32(4):208-215.
[15] LI Y S,PAN J S,ZHANG Q D.Using Improved Artificial Fish Swarm Algorithm to Solve TSP Problem[J].Journal of Shijia-zhuang Railway University(Natural Science Edition),2011,24 (2):103-110.(in Chinese) 李跃松,樊金生,张巧迪.用改进的人工鱼群算法求解TSP问题[J].石家庄铁道大学学报(自然科学版),2011,24(2):103-110.

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