计算机科学 ›› 2013, Vol. 40 ›› Issue (12): 98-103.

• 综述 • 上一篇    下一篇

基于排名映射概率的混沌人工蜂群算法

张新明,李晓安,何文涛,王鲜芳   

  1. 河南师范大学计算机与信息工程学院 新乡453007;河南师范大学计算机与信息工程学院 新乡453007;河南师范大学计算机与信息工程学院 新乡453007;河南师范大学计算机与信息工程学院 新乡453007
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受国家自然科学基金项目(61173071),河南省高校创新人才支持计划项目(2012HASTIT011)资助

Chaotic Artificial Bee Colony Algorithm Based on Rank Mapping Probability

ZHANG Xin-ming,LI Xiao-an,HE Wen-tao and WANG Xian-fang   

  • Online:2018-11-16 Published:2018-11-16

摘要: 针对人工蜂群算法(Artificial Bee Colony algorithm,ABC)因直接采用函数值映射的概率选择食物源而引起过早收敛和陷入局部最优以及优化精度不高的问题,提出一种基于排名映射概率的混沌人工蜂群算法(Chaotic Artificial Bee Colony algorithm based on Rank mapping probability,CABC-R)。首先利用目标函数值的排名映射获取选择食物源的概率,然后构建基于排名映射概率的人工蜂群算法以便能够维持种群的多样性,获得较好的全局最优解,最后创建较高寻优精度的新型局部混沌优化算法精确寻找最优解。对10个标准测试函数进行了仿真,结果表明,CABC-R算法不仅优化效果更准确而且更能跳出局部最优,有效地找到全局最优解,优于标准的ABC、JADE、 MSEP 和 RABC算法。

关键词: 优化方法,人工蜂群算法,混沌优化算法,排名映射概率,直接映射概率

Abstract: In view of the shortcomings of artificial bee colony algorithms,such as the low convergence rate and being trapped into local optimums owing to choosing the food source based on direct mapping probability,and low optimization precision,a chaotic artificial bee colony optimization algorithm based on rank mapping probability (CABC-R) was proposed in this paper.The proposed search process was divided into two different phases:in the first one an ABC global optimizer based on rank mapping probability was created to get a global solution,in the second one the local chaotic optimization algorithm was gotten to obtain more precise an optimum.The simulation results on 10standard test complicated functions indicate that the proposed optimization algorithm is rapid and effective,and that it outperforms the current global optimization algorithms such as ABC,JADE,MSEP and RABC.

Key words: Optimization method,Artificial bee colony algorithm (ABC),Chaotic optimization algorithm (COA),Rank mapping probability,Direct mapping probability

[1] Karaboga D.An idea based on honey bee swarm for numerical optimization [R]. Technical Report-TR06.Erciyes University,Kayseri,Turkey,2005
[2] Karaboga D,Basturk B.A powerful and efficient algorithm for numerical function optimization:Artificial bee colony (ABC) algorithm [J].Journal of Global Optimization,2007,39(3):459-171
[3] Banharnsakun A,Achalakul T,Sirinaovakul B.The best-so-farselection in artificial bee colony algorithm [J].Applied Soft Computing,2011,11:2888-2901
[4] Li G Q,Niu P F,Xiao X J.Development and investigation of efficient artificial bee colony algorithm for numerical function optimization [J].Applied Soft Computing,2012,12:320-332
[5] Kang F,Li J J,Ma Z Y.Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions [J].Information Sciences,2011,181(1):3508-3531
[6] Gao W F,Liu S Y.Improved artificial bee colony algorithm for global optimization [J].Information Processing Letters,2011,111(17):871-882
[7] 毕晓君,王艳娇.加速收敛的人工蜂群算法[J].系统工程与电子技术,2011,33(12):2755-2761
[8] 张新明,孙印杰.基于混沌优化的自适应中值滤波[J].电子技术应用,2007,33(9):63-65
[9] 张新明,徐久成.基于混沌理论和支持向量机的人脸识别方法[J].高技术通讯,2009,19(5):494-500
[10] Alatas B.Chaotic bee colony algorithms for global numerical optimization [J].Expert Systems with Applications,2010,37:5682-5687
[11] 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,2010,25(12):1913-1916
[12] 李志勇,李玲玲,王翔,等.基于Memetic 框架的混沌人工蜂群算法[J].计算机应用研究,2012,29(11):4045-4049
[13] 张新明,雷冠军,闫林,等.一种新型快速的直接随机优化算法[J].吉林大学学报:理学版,2012,50(4):750-756
[14] Dong H B,He J,Huang H K,et al.Evolutionary programming using a mixed mutation strategy [J].Information Sciences,2007,177(1):312-327
[15] Zhang J,Sanderson A C.JADE:adaptive differential evolution with optional external archive [J].IEEE Transactions on Evolutionary Computation,2009,13(5):945-958

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