基于平均熵的自适应人工蜂群算法
|
摘要
针对基本人工蜂群算法容易陷入局部最优和早熟等问题,提出一种改进的人工蜂群算法(ASABC)。利用平均熵机制初始化种群,增加种群的多样性,避免算法陷入早熟;同时,采用自适应调节邻域搜索步长的策略来提高算法的局部搜索能力,提升算法的计算精度;为了平衡算法的全局搜索能力和局部搜索能力,引入自适应比例选择策略来代替人工蜂群算法的适应度比例选择方法。对8个标准测试函数的仿真实验结果表明,与3种常见的智能优化方法相比,改进的算法具有显著的局部搜索能力和较快的收敛速度。
In order to overcome the shortcomings that artificial bee colony(ABC)traps into local optima and premature easily,an improved artificial bee colony algorithm named ASABC algorithm was proposed.The new algorithm adopts mean entropy tactic to initialize population,which can increase the diversity of population and avoid the stagnation and premature.At the same time,the new algorithm adopts the strategy which can adjust the neighbour seletion step size adaptively to improve the local search ability and calculation precision.To balance the global search ability and the local search ability,the self-adaptive proportion selection strategy is used to replace the fitness proportion selection method of the ABC algorithm.The results of the simulation experiment on a suite of eight benchmark functions show that the new algorithm has remarkable local search ability and a faster convergence rate compared with three common intelligent optimization algorithms.
In order to overcome the shortcomings that artificial bee colony(ABC)traps into local optima and premature easily,an improved artificial bee colony algorithm named ASABC algorithm was proposed.The new algorithm adopts mean entropy tactic to initialize population,which can increase the diversity of population and avoid the stagnation and premature.At the same time,the new algorithm adopts the strategy which can adjust the neighbour seletion step size adaptively to improve the local search ability and calculation precision.To balance the global search ability and the local search ability,the self-adaptive proportion selection strategy is used to replace the fitness proportion selection method of the ABC algorithm.The results of the simulation experiment on a suite of eight benchmark functions show that the new algorithm has remarkable local search ability and a faster convergence rate compared with three common intelligent optimization algorithms.
引文
[1]Karaboga D.An idea based on honey bee swarm for numerical optimization[R].Kayseri:Erciyes University,2005
[2]Karaboga D,Bastjurk B.On the performance of artificial bee colonY(ABC)algorithm[J].Applied Soft Computing,2008,8(1):687-697
[3]Horng M H.Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation[J].Expert Systems with Application,2011,38(11):13785-13791
[4]Gao Wei-feng,Liu San-yang.A modified artificial bee colony algorithm[J].Computers&Operations Research,2012,37(3):687-697
[5]Akay B,Karaboga D.A modified artificial bee colony algorithm for real-parameter optimization[J].Information Sciences,2012,192(1):120-142
[6]胡珂,李迅波,王振林.改进的人工蜂群算法性能[J].计算机应用,2011,31(4):1107-1110Hu Ke,Li Xun-bo,Wang Zhen-lin.Performance of an improved artificial bee colony algorithm[J].Journal of Computer Applications,2011,31(4):1107-1110
[7]Irani R,Nasimi R.Application of artificial bee colony-based neural network in bottom hole pressure prediction in underbalanced drilling[J].Journal of Petroleum Science and Engineering,2011,78(1):6-12
[8]Karaboga D,Oztruk C.A novel clustering aPproach:artificial bee colony(ABC)algorithm[J].Applied Soft Computing,2011,11(1):652-657
[9]Singh A.An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem[J].Applied Soft Computing,2009,9(2):625-631
[10]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
[11]罗钧,肖向海,付丽,等.基于分段搜索策略的改进蜂群算法[J].控制与决策,2012,27(9):1402-1405Luo Jun,Xiao Xiang-hai,Fu Li,et al.Modified artificial bee colony algorithm based on segmental-search strategy[J].Control and Decision,2012,27(9):1402-1405
[12]向万里,马寿峰.基于轮盘赌反向选择机制的蜂群优化算法[J].计算机应用研究,2013,30(1):86-89Xiang Wan-li,Ma Shou-feng.Artificial bee colony based on reverse selection of roulette[J].Application Research of Computers,2013,30(1):86-89
[13]喻金平,郑杰,梅宏标.基于改进人工蜂群算法的K均值聚类算法[J].计算机应用,2014,34(4):1065-1069Yu Jin-ping,Zheng Jie,Mei Hong-biao.K-means clustering algorithm based on improved artificial bee colony algorithm[J].Journal of Computer Applications,2014,34(4):1065-1069
[14]张乐,刘忠,张建强,等.基于人工蜂群算法优化的改进高斯过程模型[J].国防科技大学学报,2014,36(1):154-160Zhang Le,Liu Zhong,Zhang Jian-qiang,et al.Optimized improved Gaussian process model based on artificial bee colony algorithm[J].Journal of National University of Defense Technology,2014,36(1):154-160
[15]郭莹,张长胜,张斌.一种求解SAT问题的人工蜂群算法[J].东北大学学报(自然科学版),2014,35(1):29-32Guo Ying,Zhang Chang-sheng,Zhang Bin.An Artificial Bee Colony Algorithm for Solving SAT Problem[J].Journal of Northeastern University(Natural Science),2014,35(1):29-32
[16]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):127-135Qin Quan-de,Cheng Shi,Li Li,et al.Artificial bee colony algorithm:a survey[J].CAAI Transactions on Intelligent Systems,2014,9(2):127-135
[17]Wolpert D H,Macready W G.No free lunch theorems for optimization[J].IEEE Transactions on Evolutionary Computation,1997,1(1):67-82
[18]杨小芹,黎明,周琳霞.基于熵的双群体遗传算法研究[J].模式识别与人工智能,2005,18(3):286-289Yang Xiao-qin,Li Ming,Zhou Lin-xia.Entropy Based Genetic Algorithm with Dual Subpopulations[J].Pattern Recognition and Artificial Intelligence,2005,18(3):286-289
[19]Chun J S,Kim M K,Jung H K.Shape Optimization of Electromagnetic Devices Using Immune Algorithm[J].IEEE Trans on Magnetics,1997,33(2):1876-1879
[20]Bessaou M,Siarry P.A Genetic Algorithm with Real-Value Coding to Optimize Multimodal Continuous Functions[J].Structure Multidiscipline Optimization,2001,23(1):63-74
[2]Karaboga D,Bastjurk B.On the performance of artificial bee colonY(ABC)algorithm[J].Applied Soft Computing,2008,8(1):687-697
[3]Horng M H.Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation[J].Expert Systems with Application,2011,38(11):13785-13791
[4]Gao Wei-feng,Liu San-yang.A modified artificial bee colony algorithm[J].Computers&Operations Research,2012,37(3):687-697
[5]Akay B,Karaboga D.A modified artificial bee colony algorithm for real-parameter optimization[J].Information Sciences,2012,192(1):120-142
[6]胡珂,李迅波,王振林.改进的人工蜂群算法性能[J].计算机应用,2011,31(4):1107-1110Hu Ke,Li Xun-bo,Wang Zhen-lin.Performance of an improved artificial bee colony algorithm[J].Journal of Computer Applications,2011,31(4):1107-1110
[7]Irani R,Nasimi R.Application of artificial bee colony-based neural network in bottom hole pressure prediction in underbalanced drilling[J].Journal of Petroleum Science and Engineering,2011,78(1):6-12
[8]Karaboga D,Oztruk C.A novel clustering aPproach:artificial bee colony(ABC)algorithm[J].Applied Soft Computing,2011,11(1):652-657
[9]Singh A.An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem[J].Applied Soft Computing,2009,9(2):625-631
[10]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
[11]罗钧,肖向海,付丽,等.基于分段搜索策略的改进蜂群算法[J].控制与决策,2012,27(9):1402-1405Luo Jun,Xiao Xiang-hai,Fu Li,et al.Modified artificial bee colony algorithm based on segmental-search strategy[J].Control and Decision,2012,27(9):1402-1405
[12]向万里,马寿峰.基于轮盘赌反向选择机制的蜂群优化算法[J].计算机应用研究,2013,30(1):86-89Xiang Wan-li,Ma Shou-feng.Artificial bee colony based on reverse selection of roulette[J].Application Research of Computers,2013,30(1):86-89
[13]喻金平,郑杰,梅宏标.基于改进人工蜂群算法的K均值聚类算法[J].计算机应用,2014,34(4):1065-1069Yu Jin-ping,Zheng Jie,Mei Hong-biao.K-means clustering algorithm based on improved artificial bee colony algorithm[J].Journal of Computer Applications,2014,34(4):1065-1069
[14]张乐,刘忠,张建强,等.基于人工蜂群算法优化的改进高斯过程模型[J].国防科技大学学报,2014,36(1):154-160Zhang Le,Liu Zhong,Zhang Jian-qiang,et al.Optimized improved Gaussian process model based on artificial bee colony algorithm[J].Journal of National University of Defense Technology,2014,36(1):154-160
[15]郭莹,张长胜,张斌.一种求解SAT问题的人工蜂群算法[J].东北大学学报(自然科学版),2014,35(1):29-32Guo Ying,Zhang Chang-sheng,Zhang Bin.An Artificial Bee Colony Algorithm for Solving SAT Problem[J].Journal of Northeastern University(Natural Science),2014,35(1):29-32
[16]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):127-135Qin Quan-de,Cheng Shi,Li Li,et al.Artificial bee colony algorithm:a survey[J].CAAI Transactions on Intelligent Systems,2014,9(2):127-135
[17]Wolpert D H,Macready W G.No free lunch theorems for optimization[J].IEEE Transactions on Evolutionary Computation,1997,1(1):67-82
[18]杨小芹,黎明,周琳霞.基于熵的双群体遗传算法研究[J].模式识别与人工智能,2005,18(3):286-289Yang Xiao-qin,Li Ming,Zhou Lin-xia.Entropy Based Genetic Algorithm with Dual Subpopulations[J].Pattern Recognition and Artificial Intelligence,2005,18(3):286-289
[19]Chun J S,Kim M K,Jung H K.Shape Optimization of Electromagnetic Devices Using Immune Algorithm[J].IEEE Trans on Magnetics,1997,33(2):1876-1879
[20]Bessaou M,Siarry P.A Genetic Algorithm with Real-Value Coding to Optimize Multimodal Continuous Functions[J].Structure Multidiscipline Optimization,2001,23(1):63-74