Computer Science ›› 2013, Vol. 40 ›› Issue (8): 252-257.
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GE Yu,LIANG Jing,WANG Xue-ping and XIE Xiao-chuan
[2] Karaboga D,Basturk B.On the performance of artificial bee co-lony(ABC)algorithm[J].Applied Soft Computing,2008,8(1):687-697 [3] Karaboga D,Akay B.A comparative study of artificial bee colony algorithm[J].Applied Mathematics and Computation,2009,214(1):108-132 [4] ,仅有Sphere和Schwefel’s 2.22函数的优化结果优于SABC,但SABC的求解复杂度明显大于文献[4](SABC为100维,文献[4]为50维),并且SABC对以上两函数的求解结果也达到了10-100以上精度,说明SABC与文献[4]的改进策略相比同样占有一定优势。 表4 SABC与参考文献的对比结果 函数算法 SABC文献[4]文献[8]文献[9]文献[10] Sphere7.7E-24201.3E-333.0E-16- Quartic4.1E-46.5E-22.5E-1-- Step01.2E-15--- Schwefel’s 2.213.8E-9619.67--- Schwefel’s 2.222.5E-1050--- SumSquares5.7E-247---3.3E-21 Griewank007.2E-172.7E-160 Rastrigin001.4E-1606.8E-14 Ackley8.8E-168.8E-163.4E-132.9E-145.3E-13结束语 为提高人工蜂群算法求解复杂函数优化问题的性能,本文对其进行改进,分析了原算法侦察蜂逃逸行为的不足,并提出了改进方案。具体地,设计了逃逸指标、自适应选择机制,改进了逃逸算子。数值实验结果表明:在函数优化问题求解中,本文改进方案能有效帮助算法逃离早熟收敛,使算法的收敛速度和求解精度得到提高,达到了预期效果。 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 co-lony(ABC)algorithm[J].Applied Soft Computing,2008,8(1):687-697[3]Karaboga D,Akay B.A comparative study of artificial bee colony algorithm[J].Applied Mathematics and Computation,2009,214(1):108-132[4]Li C Q,Niu P F,Xiao X J.Development and investigation of efcient articial bee colony algorithm for function optimization numerical function optimization[J].Applied Soft Computing,2012,12:320-332 [5] 罗钧,王强,付丽.改进蜂群算法在平面误差评定中的应用[J].光学精密工程,2012,0(2):422-430 [6] Horng M H.Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation[J].Expert Syst Appl,2011,38(11):13785-13791 [7] ztürk C,Karaboˇga D,Grkemli B.Artificial bee colony algorithm for dynamic deployment of wireless sensor networks[J].Turk J Electr Eng Comput Sci,2012,0(2):1-8 [8] 文献 [9] 文献 [10] Sphere7.7E-24201.3E-333.0E-16- Quartic4.1E-46.5E-22.5E-1-- Step01.2E-15--- Schwefel’s 2.213.8E-9619.67--- Schwefel’s 2.222.5E-1050--- SumSquares5.7E-247---3.3E-21 Griewank007.2E-172.7E-160 Rastrigin001.4E-1606.8E-14 Ackley8.8E-168.8E-163.4E-132.9E-145.3E-13结束语 为提高人工蜂群算法求解复杂函数优化问题的性能,本文对其进行改进,分析了原算法侦察蜂逃逸行为的不足,并提出了改进方案。具体地,设计了逃逸指标、自适应选择机制,改进了逃逸算子。数值实验结果表明:在函数优化问题求解中,本文改进方案能有效帮助算法逃离早熟收敛,使算法的收敛速度和求解精度得到提高,达到了预期效果。 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 co-lony(ABC)algorithm[J].Applied Soft Computing,2008,8(1):687-697[3]Karaboga D,Akay B.A comparative study of artificial bee colony algorithm[J].Applied Mathematics and Computation,2009,214(1):108-132[4]Li C Q,Niu P F,Xiao X J.Development and investigation of efcient articial bee colony algorithm for function optimization numerical function optimization[J].Applied Soft Computing,2012,12:320-332[5]罗钧,王强,付丽.改进蜂群算法在平面误差评定中的应用[J].光学精密工程,2012,0(2):422-430[6]Horng M H.Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation[J].Expert Syst Appl,2011,38(11):13785-13791[7]ztürk C,Karaboˇga D,Grkemli B.Artificial bee colony algorithm for dynamic deployment of wireless sensor networks[J].Turk J Electr Eng Comput Sci,2012,0(2):1-8[8]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[9]Wu B,Fan S H.Improved artificial bee colony algorithm with chaos[M].Computer science for environmental engineering and ecoinformatics,Berlin:Springer,2011:51-56[10]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,2012,7(9):1402-1405 [11] 张超群,郑建国,王翔.蜂群算法研究综述[J].计算机应用研究,2011,28(9):3201-3205 [12] Chen S H,Chen M C,Chang P C,et al.Guidelines for developing effective estimation of distribution algorithms in solving single machine scheduling problems[J].Expert Systems with Applications,2010,37(9):6441-6451 |
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