Wang Jie-sheng, Li Shu-xia, Song Jiang-di
School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114044, China ; National Financial Security and System Equipment Engineering Research Center, University of Science and Technology Liaoning, Anshan 114044, China.
School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114044, China.
Comput Intell Neurosci. 2015;2015:374873. doi: 10.1155/2015/374873. Epub 2015 Aug 3.
In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.
为了提高布谷鸟搜索（CS）算法求解函数优化问题的收敛速度和优化精度，提出了一种基于重复循环渐近自学习和自进化扰动（RC-SSCS）的改进布谷鸟搜索算法。通过构造扰动因子在算法中加入扰动操作，以便在鸟巢位置附近进行更细致、全面的搜索。为了选择合理的重复循环扰动次数，进一步研究了扰动次数的选择。最后，采用六个典型测试函数进行仿真实验，并将本文算法与两种典型的群体智能算法——粒子群优化（PSO）算法和人工蜂群（ABC）算法进行比较。结果表明，改进后的布谷鸟搜索算法具有更好的收敛速度和优化精度。
