[1]彭晓华,刘利强.混沌搜索策略的改进人工蜂群算法[J].智能系统学报编辑部,2015,10(6):927-933.[doi:10.11992/tis.201507032]
　PENG Xiaohua,LIU Liqiang.Improved artificial bee colony algorithm based on chaos searching strategy[J].CAAI Transactions on Intelligent Systems,2015,10(6):927-933.[doi:10.11992/tis.201507032]
点击复制

混沌搜索策略的改进人工蜂群算法

《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷: 10 期数: 2015年第6期 页码: 927-933 栏目: 学术论文—机器学习 出版日期: 2015-12-25
Title:
Improved artificial bee colony algorithm based on chaos searching strategy
作者:
彭晓华1, 刘利强2
1. 辽宁工程技术大学基础教学部, 辽宁葫芦岛 125105;
2. 辽宁工程技术大学电气与控制工程学院, 辽宁葫芦岛 125105
Author(s):
PENG Xiaohua1, LIU Liqiang2
1. Ministry of basic education, Liaoning University of engineering and Technology, Huludao 125105, China;
2. College of electrical and control engineering, Liaoning University of engineering and Technology, Huludao 125105, China
关键词:
人工蜂群算法; 混沌搜索策略; 载波映射; 局部蜜源搜索; 蜂群多样性; 混沌-决策变量; 收敛性能; 仿真实验
Keywords:
artificial bee colony algorithm; chaotic search strategy; carrier mapping; local search nectar; the swarm diversity; chaos-decision variable; convergence performance; simulation experiment
分类号:
TP301.6
DOI:
10.11992/tis.201507032
摘要:
针对人工蜂群算法的蜂群缺乏多样性、全局和局部搜索能力差及收敛速度较慢,提出一种基于混沌搜索策略的改进人工蜂群算法。该算法通过载波映射,由混沌-决策变量的变换,产生新的邻域点,为采蜜蜂和被招募的观察蜂提供了更广阔的搜索空间和更优质的位置蜜源,增强蜂群多样性;同时,引进侦查蜂局部蜜源搜索较好地解决了算法易陷入局部极小的问题,改善了人工蜂群算法的收敛性能。最后由6个标准测试函数的仿真验证,得到基于混沌搜索策略的人工蜂群算法性能明显优于标准人工蜂群算法。
Abstract:
The current artificial bee colony algorithm results in the swarm lacking diversity, and the global and local search abilities and convergence speed are slow. We propose an improved artificial bee colony algorithm based on a chaotic search strategy. We map the algorithm with the carrier using a chaos decision variable transformation, generating new neighborhood points, and recruiting bees within a broader search space and from better source locations, while enhancing swarm diversity. In addition, the investigation of a local honey bee search better solved the algorithm problem of the local minimum and improved the convergence property of the artificial bee colony algorithm. The most recent six simulation validations of the standard test functions using the proposed artificial bee colony algorithm, based on the chaotic search strategy, are significantly better than the performance results of the current artificial bee colony algorithm.
参考文献/References:
[1] KARABOGA D, AKAY B. A comparative study of artificial bee colony algorithm[J]. Applied Mathematics and Computation, 2009, 214(1):108-132.
[2] KARABOGA D, OZTURK C. A novel clustering approach:artificial bee colony(ABC) algorithm[J]. Applied Soft Computing, 2011, 11(1):652-657.
[3] ZHU Guopu, KWONG S. Gbest-guided artificial bee colony algorithm for numerical function optimization[J]. Applied Mathematics and Computation, 2010, 217(7):3166-3173.
[4] SZETO W Y, WU Yongzhong, HO S C. An artificial bee colony algorithm for the capacitated vehicle routing problem[J]. European Journal of Operational Research, 2011, 215(1):126-135.
[5] 罗钧, 李研. 具有混沌搜索策略的蜂群优化算法[J]. 控制与决策, 2010, 25(12):1913-1916. LUO Jun, LI Yan. Artificial bee colony algorithm with chaotic-search strategy[J]. Control and Decision, 2010, 25(12):1913-1916.
[6] 宁爱平, 张雪英. 人工蜂群算法的收敛性分析[J]. 控制与决策, 2013, 28(10):1554-1558. NING Aiping, ZHANG Xueying. Convergence analysis of artificial bee colony algorithm[J]. Control and Decision, 2013, 28(10):1554-1558.
[7] 王冰. 基于局部最优解的改进人工蜂群算法[J]. 计算机应用研究, 2014, 31(4):1024-1026. WANG Bing. Improved artificial bee colony algorithm based on local best solution[J]. Application Research of Computers, 2014, 31(4):1024-1026.
[8] 伍大清, 郑建国. 基于混合策略自适应学习的并行粒子群优化算法[J]. 控制与决策, 2013, 28(7):1087-1093. WU Daqing, ZHENG Jianguo. Improved parallel particle swarm optimization algorithm with hybrid strategy and self-adaptive learning[J]. Control and Decision, 2013, 28(7):1087-1093.
[9] 胥小波, 郑康锋, 李丹, 等. 新的混沌粒子群优化算法[J]. 通信学报, 2012, 33(1):24-30, 37. XU Xiaobo, ZHENG Kangfeng, LI Dan, et al. New chaos-particle swarm optimization algorithm[J]. Journal on Communications, 2012, 33(1):24-30, 37.
[10] 匡芳君, 徐蔚鸿, 金忠. 自适应Tent混沌搜索的人工蜂群算法[J]. 控制理论与应用, 2014, 31(11):1502-1509. KUANG Fangjun, XU Weihong, JIN Zhong. Artificial bee colony algorithm based on self-adaptive tent chaos search[J]. Control Theory & Applications, 2014, 31(11):1502-1509.
[11] 王辉. 改进的蜂群算法[J]. 计算机工程与设计, 2011, 32(11):3869-3872. WANG Hui. Improved artificial bee colony algorithm[J]. Computer Engineering and Design, 2011, 32(11):3869-3872.
[12] 王辉. 一种带共享因子的人工蜂群算法[J]. 计算机工程, 2011, 37(22):139-142. WANG Hui. Artificial bee colony algorithm with sharing factor[J]. Computer Engineering, 2011, 37(22):139-142.
[13] 刘三阳, 张平, 朱明敏. 基于局部搜索的人工蜂群算法[J]. 控制与决策, 2014, 29(1):123-128. LIU Sanyang, ZHANG Ping, ZHU Mingmin. Artificial bee colony algorithm based on local search[J]. Control and Decision, 2014, 29(1):123-128.
[14] 彭泓, 丁玉成. 基于遗传交叉因子的蝙蝠算法的改进[J]. 激光杂志, 2015, 36(2):23-26. PENG Hong, DING Yucheng. Improved bats algorithm optimization based on genetic hybrid genes[J]. Laser Journal, 2015, 36(2):23-26.
[15] GAO Weifeng, LIU Sanyang. A modified artificial bee colony algorithm[J]. Computers & Operations Research, 2012, 39(3):687-697.
[16] OMKAR S N, SENTHILNATH J, RAHUL K, et al. Artificial bee colony(ABC) for multi-objective design optimization of composite structures[J]. Applied Soft Computing, 2011, 11(1):489-499.
[17] KARABOGA D, AKAY B. Artificial bee colony(ABC) algorithm on training artificial neural networks[C]//Proceedings of IEEE 15th Signal Processing and Communications Applications. Eskisehir:IEEE, 2007:1-4.
[18] KARABOGA N. A new design method based on artificial bee colony algorithm for digital IIR filters[J]. Journal of the Franklin Institute, 2009, 346(4):328-348.
[19] 王瑞琪, 李珂, 张承慧. 基于混沌多目标遗传算法的微网系统容量优化[J]. 电力系统保护与控制, 2011, 39(22):16-22. WANG Ruiqi, LI Ke, ZHANG Chenghui. Optimization allocation of microgrid capacity based on chaotic multi-objective genetic algorithm[J]. Power System Protection and Control, 2011, 39(22):16-22.
[20] 暴励, 曾建潮. 一种双种群差分蜂群算法[J]. 控制理论与应用, 2011, 28(2):266-272. BAO Li, ZENG Jianchao. A bi-group differential artificial bee colony algorithm[J]. Control Theory & Applications, 2011, 28(2):266-272.
相似文献/References:
[1]刘敏,邹杰,冯星,等.人工蜂群算法的无人机航路规划与平滑[J].智能系统学报编辑部,2011,6(4):344.
　LIU Min,ZOU Jie,FENG Xing,et al.Smooth trajectory planning of an unmanned aerial vehicleusing an artificial bee colony algorithm[J].CAAI Transactions on Intelligent Systems,2011,6():344.
[2]高珊,马良,张惠珍.基于人工蜂群算法的电子商务多Agent自动谈判模型[J].智能系统学报编辑部,2015,10(3):476.[doi:10.3969/j.issn.1673-4785.201405023]
　GAO Shan,MA Liang,ZHANG Huizhen.Multi-Agent automated negotiation model for E-commerce based on the artificial bee colony algorithm[J].CAAI Transactions on Intelligent Systems,2015,10():476.[doi:10.3969/j.issn.1673-4785.201405023]
[3]陈杰,沈艳霞,陆欣.基于信息反馈和改进适应度评价的人工蜂群算法[J].智能系统学报编辑部,2016,11(2):172.[doi:10.11992/tis.201506024]
　CHEN Jie,SHEN Yanxia,LU Xin.Artificial bee colony algorithm based on information feedback and an improved fitness value evaluation[J].CAAI Transactions on Intelligent Systems,2016,11():172.[doi:10.11992/tis.201506024]
[4]秦全德,程适,李丽,等.人工蜂群算法研究综述[J].智能系统学报编辑部,2014,9(2):127.[doi:10.3969/j.issn.1673-4785.201309064]
　QIN Quande,CHENG Shi,LI Li,et al.Artificial bee colony algorithm: a survey[J].CAAI Transactions on Intelligent Systems,2014,9():127.[doi:10.3969/j.issn.1673-4785.201309064]
[5]张平,刘三阳,朱明敏.基于人工蜂群算法的贝叶斯网络结构学习[J].智能系统学报编辑部,2014,9(3):325.[doi:10.3969/j.issn.1673-4785.201310014]
　ZHANG Ping,LIU Sanyang,ZHU Mingmin.Structure learning of Bayesian networks by use of the artificial bee colony algorithm[J].CAAI Transactions on Intelligent Systems,2014,9():325.[doi:10.3969/j.issn.1673-4785.201310014]
[6]刘永波.投资组合优化的可行性规则人工蜂群算法[J].智能系统学报编辑部,2014,9(4):491.[doi:10.3969/j.issn.1673-4785.201308047]
　LIU Yongbo.An artificial bee colony algorithm with the feasibility rulefor portfolio investment optimizations[J].CAAI Transactions on Intelligent Systems,2014,9():491.[doi:10.3969/j.issn.1673-4785.201308047]
[7]刘晓芳,柳培忠,骆炎民,等.一种增强局部搜索能力的改进人工蜂群算法[J].智能系统学报编辑部,2017,12(5):684.[doi:10.11992/tis.201612026]
　LIU Xiaofang,LIU Peizhong,LUO Yanmin,et al.Improved artificial bee colony algorithm based on enhanced local search[J].CAAI Transactions on Intelligent Systems,2017,12():684.[doi:10.11992/tis.201612026]

备注/Memo

收稿日期:2015-04-30;改回日期:。
基金项目:国家自然科学基金资助项目(51274118);辽宁省教育厅基金资助项目(L2012119).
作者简介:彭晓华,女,1963年生,教授,博士,主要研究方向为煤层瓦斯渗流理论研究、智能控制理论方法与应用研究。参加国家自然基金项目2项,主持和参加省教育厅科学研究基金项目各一项,主持或参加其他科研项目10余项。通过省市和学校鉴定的科研课题多项,获科研成果10余项。发表学术论文20余篇。刘利强,男,1988年生,硕士研究生,主要研究方向为智能检测与故障诊断。
通讯作者:刘利强.E-mail:2965131477@qq.com.

更新日期/Last Update: 1900-01-01
Copyright © 《 智能系统学报》 编辑部
地址：（150001）黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话：0451- 82534001、82518134　邮箱：tis@vip.sina.com