平衡搜索的改进人工蜂群算法
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摘要
针对人工蜂群(ABC)算法局部搜索能力弱的问题,提出一种平衡搜索的人工蜂群算法(BSABC).首先,采用一种基于对数函数的的适应度评价方式,用于减小选择压力,在一定程度上避免陷入局部最优.其次,受微分进化算法的启发,提出一种新的搜索策略,通过当前最优个体指导进化方向,使候选解的产生倾向于当前最优解,同时避免陷入局部最优.对6个经典测试函数进行仿真实验,并与经典的改进人工蜂群算法对比测试,结果表明:所提出的算法在收敛速度和收敛精度上都有显著的提升.
Aim at the drawback of artificial bee colony(ABC)algorithm with weak local search capability,an artificial bee colony algorithm based on balanced search(BSABC)is proposed.Firstly,improved fitness evaluation methods based on the logarithmic function is introduced to minimize selection pressure and avoid to fall into local optimum to a certain extent.Secondly,enlightened by the differential evolution algorithm,a novel search strategy is proposed,which conducts the evolution direction of the candidate solution,tending to the current optimal solution,and at the same time avoiding to fall into the local optimum.The simulating experiments were conducted on a benchmark suite of 6 test functions,the results demonstrate that BSABC has significant enhancement in convergent speed and convergent accuracy compared with the basic ABC algorithm.
Aim at the drawback of artificial bee colony(ABC)algorithm with weak local search capability,an artificial bee colony algorithm based on balanced search(BSABC)is proposed.Firstly,improved fitness evaluation methods based on the logarithmic function is introduced to minimize selection pressure and avoid to fall into local optimum to a certain extent.Secondly,enlightened by the differential evolution algorithm,a novel search strategy is proposed,which conducts the evolution direction of the candidate solution,tending to the current optimal solution,and at the same time avoiding to fall into the local optimum.The simulating experiments were conducted on a benchmark suite of 6 test functions,the results demonstrate that BSABC has significant enhancement in convergent speed and convergent accuracy compared with the basic ABC algorithm.
引文
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[13]王志刚,王明刚.基于符号函数的多搜索策略人工蜂群算法[J].控制与决策,2016,31(11):2037-2044.DOI:10.13195/j.kzyjc.2015.1046.
[14]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,2014,9(2):127-135.DOI:10.3969/j.issn.1673-4785.201309064.
[15]柴文光.CPSO支持向量机红外瓦斯传感器动态补偿[J].华侨大学学报(自然科学版),2016,37(3):316-319.DOI:10.11830/ISSN.1000-5013.2016.03.0316.
[2] KARABOGA D,BASTURK B.On the performance of artificial bee colony(ABC)algorithm[J].Applied Soft Computing,2008,8(1):687-697.DOI:10.1016/j.asoc.2007.05.007.
[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.DOI:10.1016/j.amc.2010.08.049.
[4] BANHARNSAKUN A,ACHALAKUL T,SIRINAOVAKUL B.The best-so-far selection in artificial bee colony algorithm[J].Applied Soft Computing,2011,11(2):2888-2901.DOI:10.1016/j.asoc.2010.11.025.
[5]高卫峰,刘三阳,黄玲玲.受启发的人工蜂群算法在全局优化问题中的应用[J].电子学报,2012,40(12):2396-2403.DOI:10.3969/j.issn.0372-2112.2012.12.007.
[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(9):1554-1558.
[7] LI Junqing,PAN Quanke,TASGETIREN M F.A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities[J].Applied Mathematical Modelling,2014,38(3):1111-1132.DOI:10.1016/j.apm.2013.07.038.
[8] KIRAN M S,HAKLI H,GUNDUZ M,et al.Artificial bee colony algorithm with variable search strategy for continuous optimization[J].Information Sciences,2015,300:140-157.DOI:10.1016/j.ins.2014.12.043.
[9] GAO Weifeng,LIU Sanyang,HUANG Lingling.Enhancing artificial bee colony algorithm using more informationbased search equations[J].Information Sciences,2014,270(1):112-133.DOI:10.1016/j.ins.2014.02.104.
[10]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,2016,11(2):172-179.DOI:10.11992/tis.201506024.
[11] YI Wenchao,GAO Liang,ZHOU Yinzhi,et al.Differential evolution algorithm with variable neighborhood search for hybrid flow shop scheduling problem[C]∥International Conference on Computer Supported Cooperative Work in Design.[S.l.]:IEEE Press,2016:233-238.DOI:10.1109/CSCWD.2016.7565994.
[12] SUGANTHAN P N,HANSEN N,LIANG J J,et al.Problem definitions and evaluation criteria for the CEC 2005special session on real-parameter optimization[R].Singapore:Nanyang Technological University,2005:341-357.
[13]王志刚,王明刚.基于符号函数的多搜索策略人工蜂群算法[J].控制与决策,2016,31(11):2037-2044.DOI:10.13195/j.kzyjc.2015.1046.
[14]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,2014,9(2):127-135.DOI:10.3969/j.issn.1673-4785.201309064.
[15]柴文光.CPSO支持向量机红外瓦斯传感器动态补偿[J].华侨大学学报(自然科学版),2016,37(3):316-319.DOI:10.11830/ISSN.1000-5013.2016.03.0316.
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