The Cuckoo search Algorithm Based on Fuzzy C-mean Clustering
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- 英文论文题名：The Cuckoo search Algorithm Based on Fuzzy C-mean Clustering
- 论文作者：Lijun Yu ; Zequan Dong ; Hui Wang ; Ying Ding
- 英文论文作者：Lijun Yu ; Zequan Dong ; Hui Wang ; Ying Ding ; College of Automation ; Harbin Engineering University
- 年：2017
- 作者机构：College of Automation,Harbin Engineering University;
- 英文论文关键词：Cuckoo Search ; Fuzzy C-means clustering ; Greedy strategy ; High-dimensional functions optimization
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：TP18
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：227k
- 原文格式：D
- 会议级别：全国
摘要
Cuckoo Search is a meta-heuristic algorithm proposed in recent years.General evaluation strategy is used in pair solution,while the quality and convergence rate of solutions decrease for the interdimensional interference when solving high-dimensional functions optimization.In order to supplement this defect,this work proposed to cluster similar components among individuals with fuzzy C-means clustering and then update the evaluation by clusters.Moreover,the new solution to the current solution can be improved by accepting with greedy strategy.The simulation results show that the improved CS can effectively increase the quality and convergence rate of solutions.Moreover,with the increase of dimensions,this advantage becomes more and more obvious.The comparison between other improved CS and meta-heuristic algorithm shows that the improved algorithm has large advantage in solving high-dimensional functions.
Cuckoo Search is a meta-heuristic algorithm proposed in recent years.General evaluation strategy is used in pair solution,while the quality and convergence rate of solutions decrease for the interdimensional interference when solving high-dimensional functions optimization.In order to supplement this defect,this work proposed to cluster similar components among individuals with fuzzy C-means clustering and then update the evaluation by clusters.Moreover,the new solution to the current solution can be improved by accepting with greedy strategy.The simulation results show that the improved CS can effectively increase the quality and convergence rate of solutions.Moreover,with the increase of dimensions,this advantage becomes more and more obvious.The comparison between other improved CS and meta-heuristic algorithm shows that the improved algorithm has large advantage in solving high-dimensional functions.
Cuckoo Search is a meta-heuristic algorithm proposed in recent years.General evaluation strategy is used in pair solution,while the quality and convergence rate of solutions decrease for the interdimensional interference when solving high-dimensional functions optimization.In order to supplement this defect,this work proposed to cluster similar components among individuals with fuzzy C-means clustering and then update the evaluation by clusters.Moreover,the new solution to the current solution can be improved by accepting with greedy strategy.The simulation results show that the improved CS can effectively increase the quality and convergence rate of solutions.Moreover,with the increase of dimensions,this advantage becomes more and more obvious.The comparison between other improved CS and meta-heuristic algorithm shows that the improved algorithm has large advantage in solving high-dimensional functions.
引文
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[2]Wang Lijin,Zhong Yiwen,Hu Xinxin.An improved group search optimizer for multi-dimensional function optimization problems[J].Journal of Chinese computer systems,2013,3(3):613-615.
[3]Shi Junpeng,Wu Yiquan.A fingerprint minutiae matching algorithm based on chaotic bee colony optimization[J].CAAI Transactions on Intelligent Systems,2016,11(5):613-618.
[4]J.Eaton,S.Yang,M.Mavrovouniotis.Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays[J].Soft Computing,2016,20(8):2951-2966.
[5]Rana S,Jasola S,Kumar R.A review on particle swarm optimization algorithms and their applications to data clustering[J].Artificial Intelligence Review,2011,35(3):211-222.
[6]Yang XS,Deb S.Cuckoo search via Le'vy flights.Proceedings of world congress on nature and biologically inspired computing(Na BIC 2009).IEEE Publications,USA,2009,pp 210-214.
[7]Yang XS,Deb S.Engineering optimization by cuckoo search.Int J Math Modell Num Opt,2010,1(4):330-343.
[8]Valian E,Mohanna S,Tavakoli S.Improved cuckoo search algorithm for global optimization.Int'l Journal of Communications and Information Technology,2011,1(1):31-44.
[9]Wang Fang,He Xingshi,Luo Ligui,Wang Yan.Hybrid optimization algorithm of PSO and cuckoo search.In:Proc.of the 2nd Int'l Conf.on Artificial Intelligence,Management Science and Electronic Commerce(AIMSEC).Piscataway:IEEE Inc,2011.1172-1175.
[10]Wang Lijin,Yin Yilong,Zhong Yiwen.Cuckoo search algorithm with dimension by dimension[J].Journal of Software,2013(11):2687-2698.
[11]Hansen N,Ostermeier A.Completely derandomized self-adaptation in evolution strategies.Evolutionary Computation,2001,9(2):159-195.
[12]Deb K,Anand A,Joshi D.A computationally efficient evolutionary algorithm for real-parameter optimization.Evolutionary Computation,2002,10(4):371-395.
[13]Noman N,Iba H.Accelerating differential evolution using an adaptive local search.IEEE Trans,on Evolutionary Computation,2008,12(1):107-125.