自适应人工鱼群算法

计算机工程与科学

自适应人工鱼群算法

赵莉莉，戴月明

（江南大学物联网工程学院,江苏无锡 214122）

收稿日期:2015-08-26 修回日期:2015-10-20 出版日期:2016-09-25 发布日期:2016-09-25
基金资助:
国家863计划（2013AA040405）

A novel artificial fish swarm algorithm based on Log-Linear model and Gauss-Cauchy mutation

ZHAO Li-li,DAI Yue-ming

（School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China）

Received:2015-08-26 Revised:2015-10-20 Online:2016-09-25 Published:2016-09-25

摘要/Abstract

摘要：

针对基本人工鱼群算法在寻优过程中易在非全局极值点附近大量聚集，导致寻优精度降低、收敛速度过慢、人工鱼群多样性降低等问题，提出了一种基于Log-Linear模型的Gauss-Cauchy自适应人工鱼群算法。首先，在基本人工鱼群算法中引入Log-Linear模型来优化人工鱼的三个行为；其次，在算法中引入自适应调整人工鱼视野和步长的策略，随着算法的进行提高了人工鱼的搜索范围和寻优精度；再次，利用Gauss-Cauchy变异来提高人工鱼的多样性。仿真实验结果表明，该算法与其他改进算法相比，有效地提高了收敛速度和寻优精度，保持了人工鱼群的多样性。

关键词: 人工鱼群算法, Log-Linear模型, Gauss-Cauchy变异, 自适应, 优化

Abstract:

Aiming at some defects of the traditional artificial fish swarm algorithm (AFSA), such as low optimization precision and long running time, we propose a new adaptive artificial fish swarm algorithm based on Log-Linear model and Gauss-Cauchy mutation. Firstly, we use the Log-Linear model to improve the three typical behaviors of the artificial fish, which are foraging, clustering and rear-end collision. Secondly, we adopt a policy which can adaptively adjust vision and the step length of the artificial fish in the new algorithm. Thirdly, we leverage the Gauss-Cauchy mutation to keep individual diversity and to avoid falling into the local extremum. Simulation results show that compared with other algorithms, the proposed algorithm has a better convergence speed and stability.

Key words: artificial fish swarm algorithm(AFSA);Log-Linear model;Gauss-Cauchy mutation, adaptive, optimization

赵莉莉，戴月明. 自适应人工鱼群算法[J]. 计算机工程与科学.

ZHAO Li-li,DAI Yue-ming. A novel artificial fish swarm algorithm based on Log-Linear model and Gauss-Cauchy mutation [J]. Computer Engineering & Science.

参考文献

［1］Li Xiao-lei,Shao Zhi-jiang,Qian Ji-xin.An optimizing method based on autonomous animats:Fish-swarm algorithm［J］.Systems Engineering-Theory & Practice,2002,22(11):32-38.(in Chinese)
［2］Qu Liang-dong,He Deng-xu.Artificial fish-school algorithm based on adaptive Guass mutation［J］.Computer Engineering,2009,35(15):182-189.(in Chinese)
［3］Qu Liang-dong,He Deng-xu.Artificial fish-school algorithm based on adaptive Cauchy mutation and its applications［J］.Microelectronics & Computer,2010,27(10):74-78.(in Chinese)
［4］Wang Bo.Artificial fish-school algorithm based on adaptive t distribution mixed mutation［J］.Computer Engineering & Science,2013,35(4):120-124.(in Chinese)
［5］Danial Y,Babak N,Alireza S-M,et al.mNAFSA:A novel approach for optimization in dynamic environments with global changes［J］.Swarm and Evolutionary Computation,2014,18(10):38-53.
［6］Zhehuang H,Yidong C.Log-Linear model based on behavior selection method for artificial fish swarm algorithm［J］.Computational Intelligence and Neuroscience,2015,2015(1):1-10.
［7］Yanbin G,Lianwu G,Tingjun W.Triaxial accelerometer error cofficients identification with a novel artificial fish swarm algorithm［J］.Journal of Sensors,2015,2015(1):1-17.
［8］Zhu Xu-hui,Ni Zhi-wei,Cheng Mei-ying.Self-adaptive improved artificial fish swarm algorithm with changing step ［J］.Computer Science,2015,42(2):210-215.(in Chinese)
［9］Jiang Ming-yan,Yuan Dong-feng.Artificial fish swarm algorithm and its application［M］.Beijing:Science Press,2012.(in Chinese)
［10］Wang Zi-kun.Foundation and application of probability theory［M］.Beijing:Science Press,1979.(in Chinese)
［11］Hsing C T,Yong H L.Modification of the fish swarm algorithm with particle swarm optimization formulation and communication behavior［J］.Applied Soft Computing,2011,11(8):5367-5374.
［12］Wang Pei-chong,Li Li-rong,He Yi-chao,et al.Improved artificial fish swarm algorithm based on logistic model［J］.China Sciencepaper,2013,8(7):668-671.(in Chinese)
附中文参考文献：
［1］李晓磊,邵志江,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.
［2］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.
［3］曲良东,何登旭.基于自适应柯西变异人工鱼群算法及其应用［J］.微电子学与计算机,2010,27(10):74-78.
［4］王波.基于自适应t分布混合变异的人工鱼群算法［J］.计算机工程与科学,2013,35(4):120-124.
［8］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):210-215.
［9］江铭炎,袁东风.人工鱼群算法及其应用［M］.北京:科学出版社,2012.
［10］王梓坤.概率论基础及其应用［M］.北京:科学出版社,1979.
［12］王培崇,李丽荣,贺毅朝，等.一种基于Logistic模型的人工鱼群算法［J］.中国科技论文,2013,8(7):668-671.

[1]	陈侨安1，李峰1，曹越1，龙明盛1,2. 基于运行数据分析的Spark任务参数优化[J]. J4, 20160101, 38(01): 11-19.
[2]	周兰凤1，赵鹏飞1，彭俊杰2. 基于云环境下一种小文件传输策略研究[J]. J4, 20160101, 38(01): 20-27.
[3]	罗仕杭, 何庆. 多策略融合改进的均衡优化算法及其应用[J]. 计算机工程与科学, 2023, 45(08): 1508-1520.
[4]	康宇晗, 时洋, 陈照云, 文梅. 面向迈创+MatrixZone异构系统的深度学习编程框架[J]. 计算机工程与科学, 2023, 45(07): 1149-1158.
[5]	何子贤, 方贤文. 基于字符串匹配算法的业务流程低频日志噪声过滤方法[J]. 计算机工程与科学, 2023, 45(07): 1209-1215.
[6]	罗晓霞, 邓勇, 叶鸥. 复杂场景中多阶段自适应帽子检测算法[J]. 计算机工程与科学, 2023, 45(07): 1253-1262.
[7]	白杉, 冯秀芳. 基于注意力增强的中心差分自适应图卷积的骨架行为识别[J]. 计算机工程与科学, 2023, 45(07): 1263-1273.
[8]	张一楠, 丁建完. 基于WLS-MBO算法的串联机器人动力学参数辨识[J]. 计算机工程与科学, 2023, 45(07): 1274-1281.
[9]	柴岩, 朱玉, 任生. 多策略协同的改进鲸鱼优化算法[J]. 计算机工程与科学, 2023, 45(07): 1308-1319.
[10]	黄学雨, 罗华. 融合正余弦策略的算术优化算法[J]. 计算机工程与科学, 2023, 45(07): 1320-1330.
[11]	赖明澈, 吕方旭, 张庚, 许超龙. 面向112 Gbps PAM4串行接收机的低误码协同自适应均衡器[J]. 计算机工程与科学, 2023, 45(06): 951-960.
[12]	吴付坤, 曹轶, 董庆利, . 基于CAD几何模型的曲面流线可视分析方法[J]. 计算机工程与科学, 2023, 45(06): 961-969.
[13]	黄学雨, 罗华. 自适应变异蝴蝶优化算法[J]. 计算机工程与科学, 2023, 45(06): 1123-1133.
[14]	陈冬林, 马一帆, 邹安琪, . 基于可再生能源消纳的数据中心负荷调度研究综述[J]. 计算机工程与科学, 2023, 45(05): 790-801.
[15]	于家斌, 陈志豪, 邓维, 许继平, 赵峙尧, 王小艺. 一种无人巡航船遍历多目标点的路径规划算法研究[J]. 计算机工程与科学, 2023, 45(05): 840-848.