Computer Science ›› 2013, Vol. 40 ›› Issue (6): 247-251.
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GE Yu,LIANG Jing and WANG Xue-ping
| [2] Karaboga D,Basturk B．On the performance of artificial bee colo-ny(ABC) algorithm[J]．Applied Soft Computing,2008,8(1):687-697 [3] 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,2011,8(1):6-12 [4] 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 [5] ztürk C,Karabǒga D,Grkemli B．Artificial bee colony algorithm for dynamic deployment of wireless sensor networks[J]．Turkish Journal of Electrical Engineering and Computer Sciences,2012,0(2):1-8 [6] 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,2010,5(12):1913-1916 [7] 文献 [8] 文献 [9] 文献 [10] f11．31E－842．99E－162．65E－155．21E－161．26E－33 f46．75E－4－－－2．5E－1 f502．70E－161．22E－157．40E－177．22E－17 f6004．95E－1001．37E－16 f78．88E－162．94E－142．41E－82．96E－143．41E－13 f87．85E－63．82E－46．04E+2－－结束语 本文改进人工蜂群算法,在对算法分析的基础上,指出跟随蜂搜索方案的不足,基于极值优化策略重新设计了跟随蜂搜索方案,并具体实现了新搜索方案下跟随蜂的组元变异算子和最差组元判定规则。实验结果表明,本文改进方法通过提高跟随蜂的搜索效率,有效避免了算法停滞,使算法的收敛速度和求解精度得到了提高,是一种简单高效的改进方法。 Karaboga D．An idea based on honey bee swarm for numerical optimization[R]．Kayseri:Erciyes University,2005[2]Karaboga D,Basturk B．On the performance of artificial bee colo-ny(ABC) algorithm[J]．Applied Soft Computing,2008,8(1):687-697[3]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,2011,8(1):6-12[4]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[5]ztürk C,Karabǒga D,Grkemli B．Artificial bee colony algorithm for dynamic deployment of wireless sensor networks[J]．Turkish Journal of Electrical Engineering and Computer Sciences,2012,0(2):1-8[6]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,2010,5(12):1913-1916[7]Wu Bin,Fan Shu-hai．Improved artificial bee colony algorithm with chaos[A]∥Computer Science for Environmental Enginee-ring and Ecoinformatics,2011[C]．Berlin:Springer,2011:51-56[8]Rajasekhar A,Abraham A,Pant M．Levy mutated artificial bee colony algorithm for global optimization[A]∥IEEE International Cnference on Systems,Man and Cybernetics,2011[C]．Anchorage:IEEE,2011:655-662[9]Guo Peng,Cheng Wen-ming,Liang Jian．Global artificial bee co-lony search algorithm for numerical function optimization[A]∥7th International Conference on Natural Computation,2011[C]．Shanghai:IEEE,2011:1280-1283[10]暴励,曾建潮．一种双种群差分蜂群算法[J]．控制理论与应用,2011,8(2):266-272 [11] Boettcher S,Percus A G．Extremal optimization:Methods de-rived from co-evolution[C]∥Proc．of the Genetic and Evolutiona-ry Computation Conf．San Francisco:Morgan Kaufmann．1999:825-832 [12] 齐洁,汪定伟．极值优化算法综述[J]．控制与决策,2007,2(10):1081-1090 [13] 陈泯融．基于极值动力学的优化方法及其应用研究[D]．上海:上海交通大学,2008 [14] 骆剑平,陈泯融．混合蛙跳算法及其改进算法的混合轨迹及收敛性分析[J]．信号处理,2010,6(9):1428-1433 |
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