摘要
布谷鸟搜索算法(Cuckoo Search,CS)是基于群体智能的新型随机全局优化算法,具有控制参数少、搜索路径优和全局寻优能力强等优点,但也存在局部搜索能力较弱、收敛速度偏慢和收敛精度不够高等缺点。为了克服CS算法的缺点,提出一种基于共轭梯度的布谷鸟搜索算法(CGCS),使经过Levy飞行机制和淘汰机制进化后的布谷鸟种群沿着相互共轭的方向迅速下降.从而在保持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。用4个典型测试函数分别对CGCS算法和CS算法进行性能测试,结果表明,CGCS算法比CS算法具有更快的收敛速度、更高的收敛精度和更稳定的优化结果。CGCS算法同时具有很强的全局寻优能力、收敛能力和鲁棒性,特别适合多峰及高维函数的优化。
Cuckoo search algorithm is a novel stochastic global optimization algorithm based on swarm intelligence, with advantages of few control parameters, optimal search path and good global search capability, but it also has shortcomings of weak local search ability, slow convergence velocity and low convergence accuracy. In order to overcome these disadvantages of CS algorithm, an improved cuckoo search algorithm based on conjugate gradient is introduced. After evolved from Lrvy flights and elimination mechanism, the cuckoo populations decline rapidly in the mutually conjugate directions so that the convergence ability of algorithm is strengthened significantly under the condition of maintaining the strong global search capability of CS algorithm. The CGCS algorithm and CS algorithm are tested by four typical test functions. The conclusions indicate that CGCS algorithm has faster convergence velocity, higher convergence accuracy and more stable optimization results. Meanwhile CGCS algorithm has good global search capability, convergence ability and robustness, which is particularly suitable for the optimization of multimodal function and high dimension fimction.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2013年第4期406-410,共5页
Computers and Applied Chemistry
基金
福建省教育厅科技基金资助项目(JB08001)
关键词
布谷鸟搜索
Levy飞行
共轭梯度
全局寻优
收敛能力
cuckoo search, Levy flights, conjugate gradient, global searching, convergence ability
作者简介
杜利敏（1988—），男，福建人，硕士研究生，Emajl：mengyuhaDiao@163.com
联系人：阮奇（1956-），男，福建人，教授，硕士生导师，E—majl：hys@fzu．edu．cn
