正弦选择概率模型的全局最优引导人工蜂群算法
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摘要
针对人工蜂群算法收敛速度慢以及蜜源的选择概率区分度不够等缺点,提出了一种新的改进人工蜂群算法。依据当前最优蜜源、最差蜜源、当前蜜源建立正弦选择概率模型,并结合全局最优引导策略,构成新算法。概率模型以最优、最差蜜源适应值之差为尺度,衡量当前蜜源适应值所占比重,随后将比重值带入sin函数,即可得当前蜜源的选择概率值。在30、100维上,22个基准测试函数的仿真实验结果表明,正弦选择概率模型能克服后期蜜源区分度不够的问题,为观察蜂跟随雇佣蜂提供正确的引导。与知名的改进人工蜂群算法比较,该算法具有很好的优势。
A new improved artificial bee colony algorithm is proposed for the disadvantages of slow convergence speed and inadequate distinguishing of nectar source selection probability. The new algorithm establishes the sinusoidal selection probability model based on the current optimal source,the worst source and the current source of honey,and combines the global optimal guidance strategy. The probability model takes the difference between the optimal and the worst adaptation values as the scale to measure the proportion of the current function values of the honey source,and then the proportion values are substituted into the sin function to obtain the selection probability values of the current honey source. To verify the effectiveness of the new algorithm,based on 22 benchmark functions,this paper compares the algorithm with 6 mainstream improved artificial bee colonies in 30 and 100 dimensions. Experimental results show that the sinusoidal selection probability model can overcome the problem of insufficient nectar source discrimination and provide correct guidance for onlookerbees following Employbees. Compared with other improved artificial bee colony algorithm,this algorithm has unique advantages.
A new improved artificial bee colony algorithm is proposed for the disadvantages of slow convergence speed and inadequate distinguishing of nectar source selection probability. The new algorithm establishes the sinusoidal selection probability model based on the current optimal source,the worst source and the current source of honey,and combines the global optimal guidance strategy. The probability model takes the difference between the optimal and the worst adaptation values as the scale to measure the proportion of the current function values of the honey source,and then the proportion values are substituted into the sin function to obtain the selection probability values of the current honey source. To verify the effectiveness of the new algorithm,based on 22 benchmark functions,this paper compares the algorithm with 6 mainstream improved artificial bee colonies in 30 and 100 dimensions. Experimental results show that the sinusoidal selection probability model can overcome the problem of insufficient nectar source discrimination and provide correct guidance for onlookerbees following Employbees. Compared with other improved artificial bee colony algorithm,this algorithm has unique advantages.
引文
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[2]李文敬,李沛武.基于IPQSO优化SVM在径流预报中的应用[J].南昌工程学院学报,2018,37(3):54-59.
[3]Wang H,Wu Z,Rahnamayan S,et al.Multi-strategy ensemble artificial bee colony algorithm[J].Information Sciences,2014,279:587-603.
[4]陈越,吕善翔,王梦蛟,等.一种基于人工蜂群算法的混沌信号盲分离方法[J].物理学报,2015,64(09):216-224.
[5]李广明,胡志辉.基于人工蜂群算法的混沌信号盲提取[J].物理学报,2016,65(23):50-57.
[6]张曦,赵嘉,李沛武,等.改进萤火虫优化的软子空间聚类算法[J].南昌工程学院学报,2018,37(4):61-67.
[7]李冰,孙辉,王坤,等.自适应人工蜂群算法在梯级水库优化调度中的应用[J].水电能源科学,2016,34(08):59-62.
[8]马瑞,吴洪涛,丁力.基于人工蜂群算法的无人直升机LQG/LTR控制律优化设计[J].控制与决策,2016,31(12):2248-2254.
[9]王志刚,尚旭东,夏慧明,等.多搜索策略协同进化的人工蜂群算法[J].控制与决策,2018,33(02):235-241.
[10]周新宇,吴志健,王明文.基于正交实验设计的人工蜂群算法[J].软件学报,2015,26(09):2167-2190.
[11]Karaboga D,Gorkemli B.A quick artificial bee colony(q ABC)algorithm and its performance on optimization problems[J].Applied Soft Computing,2014,23(5):227-238.
[12]Zhu G P,Kwong S.Gbest-guided artificial bee colony algorithm for numerical function optimization[J].Applied Mathematics&Computation,2010,217(7):3166-3173.
[13]沈艳霞,陈杰,吴定会.一种基于进化知识融合的多目标人工蜂群算法[J].控制与决策,2017,32(12):2176-2182.
[14]Xiang W L,Li Y Z,He R C,et al.A novel artificial bee colony algorithm based on the cosine similarity[J].Computers&Industrial Engineering,2018,115:54-68.
[15]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.
[16]王坤.改进人工蜂群算法在梯级水库群优化调度中的应用[D].南昌:南昌工程学院,2017.
[17]Cui L Z,Li G H,Lin Q Z,et al.A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation[J].Information Sciences,2016,367-368:1012-1024.
[18]Gao W,Liu S.A modified artificial bee colony algorithm[J].Computers&Operations Research,2012,39(3):687-697.
[19]Gao W F,Liu S Y,Huang L L.A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning[J].IEEE Transactions on Cybernetics,2013,43(3):1011-1024.
[20]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(C):140-157.
[21]Gao W F,Chan F T S,Huang L,et al.Bare bones artificial bee colony algorithm with parameter adaptation and fitness-based neighborhood[J].Information Sciences,2015,316(C):180-200.