动态调整概率的双重布谷鸟搜索算法

doi:10.3778/j.issn.1673-9418.2004031

摘要/Abstract

摘要：

布谷鸟搜索算法是一种新兴的仿生智能算法，存在着求解精度低、易陷入局部最优及收敛速度慢等缺陷，提出了动态调整概率的双重布谷鸟搜索算法（DECS）。首先，在自适应发现概率P中引入了种群分布熵，通过算法的所处迭代阶数和种群分布情况，动态改变发现概率P的大小，有利于平衡布谷鸟算法局部寻优和全局寻优的能力，加快收敛速度；其次，在布谷鸟寻窝的路径位置更新公式中，采用了一种新型步长因子更新寻优方式，形成Levy飞行双重搜索模式，充分搜索空间；最后，在随机偏好游走的更新公式引入非线性对数递减的惯性权重策略，使得算法有效克服易陷入局部最优的缺陷，提高寻优搜索能力。与4种算法相比和19个测试函数的仿真结果表明：改进布谷鸟算法的寻优性能明显提高，收敛速度更快，求解精度更高，具有更强的全局搜索能力和跳出局部最优能力。

关键词: 种群分布熵, 双重搜索模式, 非线性对数递减的惯性权重, 新型步长因子

Abstract:

Cuckoo search algorithm is an emerging bionic intelligent algorithm, which has the shortages of low search precision, easy to fall into local optimum and slow convergence speed. Double cuckoo search algorithm with dynamically adjusted probability (DECS) is proposed. Firstly, the population distribution entropy is introduced into the adaptive discovery probability P, and the size of the discovery probability P is dynamically changed by the iteration order of the algorithm and the population distribution situation. It is advantageous to balance the ability of cuckoo algorithm local optimization and global optimization and accelerate the convergence speed. Secondly, in the formula for updating the path position of cuckoo??s nest search, a new step-size factor update and optimization method is adopted to form a double search mode of Levy flight, which sufficiently searches the solution space. Finally, the nonlinear logarithmic decreasing inertial weight is introduced into the updated formula of stochastic preference walk, so that the algorithm can effectively overcome the shortcoming of being easily trapped into a local optimum, and improve search ability. Compared with four algorithms, simulation results of 19 test functions show that, the optimization performance of the improved cuckoo algorithm is significantly improved, the convergence speed is faster, the solution accuracy is higher, and it has stronger ability of global search and jumping out of local optimum.

Key words: population distribution entropy, dual search mode, nonlinear logarithmic decreasing inertial weight, new step-size factor

陈程, 贺兴时, 杨新社. 动态调整概率的双重布谷鸟搜索算法[J]. 计算机科学与探索, 2021, 15(5): 859-880.

CHEN Cheng, HE Xingshi, YANG Xinshe. Double Cuckoo Search Algorithm with Dynamically Adjusted Probability[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(5): 859-880.

参考文献

[1] FISTER I, YANG X S, BREST J, et al. A comprehensive review of firefly algorithms[J]. Swarm and Evolutionary Computation, 2013, 13: 34-46.
[2] YANG X S. Bat algorithm for multi-objective optimisation[J]. International Journal of BioInspired Computation, 2011, 3(5): 267-274.
[3] EUSUFF M, LANSEY K E. Shuffled frogleaping algorithm: a mimetic meta-heuristic for discrete optimization[J]. Engineering Optimization, 2006, 38(2): 129-154.
[4] KENNEDY J, EBERHART R C. Particle swarm optimization [C]//Proceedings of the 1995 International Conference on Neural Networks, Perth, Nov 27-Dec 1, 1995. Piscataway: IEEE, 1995: 1942-1948.
[5] ZHANG Q W, WEI Y C. Particle swarm optimization with independent adaptive parameter adjustment[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(4): 637-648.
张其文, 尉雅晨. 独立自适应调整参数的粒子群优化算法[J]. 计算机科学与探索, 2020, 14(4): 637-648.
[6] ZHANG Z C, LIU S Y. Firefly algorithm based on topology improvement and crossover strategy[J]. Computer Engineering and Applications, 2019, 55(7): 1-8.
张哲辰, 刘三阳. 基于拓扑改进与交叉策略的萤火虫算法[J]. 计算机工程与应用, 2019, 55(7): 1-8.
[7] LIAO W L, CHENG S, SHANG D D, et al. Particle?swarm? optimization?algorithm?integrated?with?multiple-strategies[J]. Computer Engineering and Applications, 2021, 57(1): 69-76.
廖玮霖, 程杉, 尚冬冬, 等. 多策略融合的粒子群优化算法[J]. 计算机工程与应用, 2021, 57(1): 69-76.
[8] YANG X S, DEB S. Cuckoo search via Lévy flights[C]//Proceedings of the 2009 World Congress on Nature & Biologically Inspired Computing, Coimbatore, Dec 9-11, 2009. Piscataway: IEEE, 2009: 210-214.
[9] GáLVEZ A, IGLESIAS A. Memetic improved cuckoo search algorithm for automatic B-spline border approximation of cutaneous melanoma from macroscopic medical images[J]. Advanced Engineering Informatics, 2020, 43: 101005.
[10] YANG X S, DEB S. Multiobjective cuckoo search for design optimization[J]. Computers & Operations Research, 2013, 40(6): 1616-1624.
[11] KAMOONA A M, PATRA J C. A novel enhanced cuckoo search algorithm for contrast enhancement of gray scale images[J]. Applied Soft Computing Journal, 2019, 85: 105749.
[12] MELLAL M A, ZIO E. An adaptive cuckoo optimization algorithm for system design optimization under failure dependencies[J]. Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, 2019, 233(6): 1099-1105.
[13] TRAN-NGOC H, KHATIC S, ROECK D, et al. An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm[J]. Engineering Structures, 2019, 199: 109637.
[14] ZHANG H N, YOU X M, LIU S, et al. Interactive learning cuckoo search algorithm[J]. Computer Engineering and Applications, 2020, 56(7): 147-154.
张海南, 游晓明, 刘升, 等. 交互式学习的布谷鸟搜索算法[J]. 计算机工程与应用, 2020, 56(7): 147-154.
[15] WANG F, HE X S, WANG Y. The cuckoo search algorithm based on Gaussian disturbance[J]. Journal of Xi??an Polytechnic University, 2011, 25(4): 566-569.
王凡, 贺兴时, 王燕. 基于高斯扰动的布谷鸟搜索算法[J]. 西安工程大学学报, 2011, 25(4): 566-569.
[16] LUO D, WANG X D, DUAN B W. The improved cuckoo algorithm based on function dynamic decline factor[J]. Journal of Xi??an Polytechnic University, 2018, 32(4): 479-483.
罗东, 王晓东, 段博文. 基于函数动态递减因子的布谷鸟算法[J]. 西安工程大学学报, 2018, 32(4): 479-483.
[17] YE Y R, HE X S, ZHANG C. An adaptive cuckoo algorithm based on random disturbance[J]. Computer Technology and Development, 2019, 29(5): 77-80.
叶亚荣, 贺兴时, 张超. 基于随机扰动的自适应布谷鸟算法[J]. 计算机技术与发展, 2019, 29(5): 77-80.
[18] WANG F, HE X S, WANG Y, et al. Markov model and convergence analysis based on cuckoo search algorithm[J]. Computer Engineering, 2012, 38(11): 180-182.
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.
[19] SONG Y, SHI L B. Dynamic parameter adjustment mechanism based self-adaptive cuckoo search algorithm[J]. Computer Engineering and Applications, 2020, 56(23): 61-67.
宋钰, 石立宝. 参数动态调整的自适应布谷鸟算法[J]. 计算机工程与应用, 2020, 56(23): 61-67.
[20] JIA H, LIAN X F. Improved cuckoo algorithm for adaptive adjustment of discovery probability parameter[J]. Computer Engineering and Applications, 2018, 54(22): 16-22.
贾涵, 连晓峰. 发现概率参数自适应调节的布谷鸟改进算法[J]. 计算机工程与应用, 2018, 54(22): 16-22.
[21] YANG X S, DEB S. Engineering optimisation by cuckoo search[J]. International Journal of Mathematical Modelling and Numerical Optimisation, 2010, 1(4): 330-343.
[22] HE M, RUAN Q, ZHENG X G, et al. Adaptive cuckoo search algorithm[J]. Computers and Applied Chemistry, 2014, 31(8): 961-968.
贺淼, 阮奇, 郑晓桂, 等. 自适应布谷鸟搜索算法[J]. 计算机与应用化学, 2014, 31(8): 961-968.
[23] SHAO Z Z, WANG H G, LIU H. Dimensionality reduction symmetrical PSO algorithm characterized by heuristic detection and self-learning[J]. Computer Science, 2010, 37(5): 219-222.
邵增珍, 王洪国, 刘弘. 具有启发式探测及自学习特征的降维对称微粒群算法[J]. 计算机科学, 2010, 37(5): 219-222.
[24] LI Y, SHANG Z Y, LIU J S. Improved cuckoo search algorithm for function optimization problems[J]. Computer Science, 2020, 47(1): 219-230.
李煜, 尚志勇, 刘景森. 求解函数优化问题的改进布谷鸟搜索算法[J]. 计算机科学, 2020, 47(1): 219-230.
[25] YANG X S, KARAMANOGLU M, HE X. Flower pollination algorithm: a novel approach for multiobjective optimization[J]. Engineering Optimization, 2013, 46(9): 1222-1237.
[26] LI R Y, DAI R W. Adaptive step-size cuckoo search algorithm[J]. Computer Science, 2017, 44(5): 235-240.
李荣雨, 戴睿闻. 自适应步长布谷鸟搜索算法[J]. 计算机科学, 2017, 44(5): 235-240.