采用搜索趋化策略的布谷鸟全局优化算法

布谷鸟搜索算法是一种基于莱维飞行搜索策略的新型智能优化算法.单一的莱维飞行随机搜索更新策略存在全局搜索性能不足和寻优精度不高等缺陷.为了解决这一问题,本文提出了一种改进的布谷鸟全局优化算法.该算法的主要特点在于以下三个方面:首先,采用全局探测和模式移动交替进行的模式搜索趋化策略,实现了布谷鸟莱维飞行的全局探测与模式搜索的局部优化的有机结合,从而避免盲目搜索,加强算法的局部开采能力;其次,采取自适应竞争机制动态选择最优解数量,实现了迭代过程搜索速度和解的多样性间的有效平衡;最后,采用优势集搜索机制,实现了最优解的有效合作分享,强化了优势经验的学习.对52个典型测试函数实验结果表明,本文算法不仅寻优精度和寻优率显著提高,鲁棒性强,且适合于多峰及复杂高维空间全局优化问题.本文算法与最新提出的改进的布谷鸟优化算法以及其它智能优化策略相比,其全局搜索性能与寻优精度更具优势,效果更好.

A Global Cuckoo Optimization Algorithm Using Coarse-to-Fine Search

Cuckoo search (CS) algorithm is a meta-heuristic optimization algorithm based on Lévy flights.We propose an improved cuckoo search algorithm to enhance the accuracy,avoid the local optima and accelerate the convergence speed.The proposed algorithm has three main characteristics.Firstly,pattern search method enhances the exploitation ability of the basic CS algorithm.The proposed algorithm combines the random exploration of the CS algorithm and the exploitation capacity of pattern search method.Secondly,the optimal solution is obtained by self-adaptive competition mechanism.Hence,the proposed algorithm has a trade-off between searching speed and the diversity of solution.Finally,we realize the cooperation of the optimal solution to share,and strengthen the advantage of experience learning in the use of the optimal solution set search mechanism.The experimental results conducted on 52 benchmark functions show that the proposed algorithm is promising in terms of accuracy,success rate and robustness.And it is also suitable for multimodal and high-dimensional numerical optimization problems.Therefore,in terms of the global search ability and solution accuracy,our algorithm performs better than other modified CS algorithms,such as ICS (Improved Cuckoo Search algorithm),CSPSO (Cuckoo Search algorithm and Particle Swarm Optimization),OLCS (Orthogonal Learning Cuckoo Search algorithm),etc.