基于二次插值法的布谷鸟搜索算法研究
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摘要
对基本的布谷鸟搜索算法(Cuckoo Search,CS)进行研究,为改进CS算法局部搜索能力差、进化后期收敛速度慢、求解精度低等缺陷,考虑到二次插值法是一种局部搜索能力较强的搜索方法,提出一种基于二次插值法的布谷鸟搜索算法(QI_CS)。新算法充分利用鸟窝个体局部的优化信息,增强算法的局部搜索能力,加快算法搜索全局最优解的收敛速度。仿真实验结果表明,QI_CS算法在保持原算法的强大全局寻优能力的基础上大幅提高算法的收敛能力和求解精度,是求解多峰函数优化问题的一种可行和有效的方法。
The Cuckoo Search algorithm(CS) was studied, and in order to improve the shortcomings of the basic CS algorithm, such as low optimization precision and convergence slowly and poor local search ability in late evolution, an improved CS algorithm(QI_GSO) based on quadratic interpolation method was proposed in this paper. New algorithm makes full use of the bird's nest local information, enhances the local search ability of the algorithm, and speeds up the convergence of the global optimal solution. The feasibility and effectiveness of the new approach was verified through testing by functions. The experimental results show that the QI_CS algorithm is significantly superior to original CS and can greatly improve the ability of seeking the global excellent result and convergence property and accuracy, which is an effective method to solve multimodal function optimization problem.
The Cuckoo Search algorithm(CS) was studied, and in order to improve the shortcomings of the basic CS algorithm, such as low optimization precision and convergence slowly and poor local search ability in late evolution, an improved CS algorithm(QI_GSO) based on quadratic interpolation method was proposed in this paper. New algorithm makes full use of the bird's nest local information, enhances the local search ability of the algorithm, and speeds up the convergence of the global optimal solution. The feasibility and effectiveness of the new approach was verified through testing by functions. The experimental results show that the QI_CS algorithm is significantly superior to original CS and can greatly improve the ability of seeking the global excellent result and convergence property and accuracy, which is an effective method to solve multimodal function optimization problem.
引文
[1]Yang X S,Deb S.Cuckoo search via Levy Flights[C].//Pro c.of World Congress on Nature&Biologically Inspired Computing(Na Bic 2009),2009:210~214.
[2]Yang X S,Deb S.Engineering optimization by cuckoo search[J].Int.J.Mathematical Modeling and Numerical Optimization,2010,1(4):330-343.
[3]Valian E,Mohanna S,Saeed Tavakoli.Improved cuckoo search algorithm for feedforward neural network training[J].International Journal of Artificial Intelligence&Applications,2011,2(3):36-43.
[4]Walton S,Hassan O,Morgan K.Modified cuckoo search:a new gradient free optimisation algorithm[J].Chaos,Solitons&Fractals,2011,44(9):710-718.
[5]赵鹏军.求解非线性方程组的智能新方法[J].商洛学院学报,2012,26(4):18-20.
[6]王凡,贺兴时,王燕,等.基于CS算法的Markov模型及收敛性分析[J].计算机工程,2012,38(11):180-182.
[7]郑洪清,周永权.一种自适应步长布谷鸟搜索算法[J].计算机工程与应用,2013,49(10):68-71.
[8]李煜,马良.新型元启发式布谷鸟搜索算法[J].系统工程,2012,30(8):64-68.
[9]杜利敏,阮奇,冯登科.基于共轭梯度的布谷鸟搜索算法[J].计算机与应用化学,2013,30(4):406-410.
[10]武建娜,崔志华,刘静.基于二次插值法的社会情感优化算法[J].计算机应用,2011,31(9):2522-2525.
[2]Yang X S,Deb S.Engineering optimization by cuckoo search[J].Int.J.Mathematical Modeling and Numerical Optimization,2010,1(4):330-343.
[3]Valian E,Mohanna S,Saeed Tavakoli.Improved cuckoo search algorithm for feedforward neural network training[J].International Journal of Artificial Intelligence&Applications,2011,2(3):36-43.
[4]Walton S,Hassan O,Morgan K.Modified cuckoo search:a new gradient free optimisation algorithm[J].Chaos,Solitons&Fractals,2011,44(9):710-718.
[5]赵鹏军.求解非线性方程组的智能新方法[J].商洛学院学报,2012,26(4):18-20.
[6]王凡,贺兴时,王燕,等.基于CS算法的Markov模型及收敛性分析[J].计算机工程,2012,38(11):180-182.
[7]郑洪清,周永权.一种自适应步长布谷鸟搜索算法[J].计算机工程与应用,2013,49(10):68-71.
[8]李煜,马良.新型元启发式布谷鸟搜索算法[J].系统工程,2012,30(8):64-68.
[9]杜利敏,阮奇,冯登科.基于共轭梯度的布谷鸟搜索算法[J].计算机与应用化学,2013,30(4):406-410.
[10]武建娜,崔志华,刘静.基于二次插值法的社会情感优化算法[J].计算机应用,2011,31(9):2522-2525.