多策略调和的布谷鸟搜索算法

布谷鸟搜索（CS）算法是一种新型的群智能算法，结构简单且寻优能力较强，但存在勘探与开采不平衡以及易陷入局部极值的问题。提出一种多策略调和的布谷鸟搜索（MSRCS）算法，基于概率规则选择由自适应步长和改进解更新方法组成的调和策略对布谷鸟个体进行更新，其中自适应步长引导布谷鸟在更好的方向上寻优，3种改进的解更新方法分别从自身邻域、当前最优个体和随机位置3个角度对勘探和开采进行调和，从而提升全局搜索和局部搜索在迭代过程中的适应性。在CEC2013测试集的28个基准函数上的实验结果表明，MSRCS算法至少有12个测试函数优于原始CS及其7种改进算法且排名第一，在求解单峰、多峰和组合函数问题时寻优能力更强，同时相比于3种经典群智能优化算法具有更快的收敛速度和更高的解精度。

Multi-Strategy Reconciled Cuckoo Search Algorithm

The Cuckoo Search(CS) Algorithm is a new swarm intelligence optimization algorithm with a simple structure and good searching ability.Its disadvantages include an imbalance between exploration and exploitation and easily falling into the local optimum.To solve these problems, we propose a Multi-Strategy Reconciled Cuckoo Search(MSRCS) algorithm.The proposed algorithm is based on probability rules in selecting reconciliatory strategies, including a self-adaptive step size and modified solution-update methods to realize individual updates.The self-adaptive step size leads Cuckoos in a better direction.Three modified solution-update methods are searched from their respective neighborhoods, move toward the contemporary optimum, and generate a random solution to balance exploration and exploitation.This algorithm effectively improves the adjusting ability of global and local searches during the iteration process.The experimental results obtained using 28 benchmark functions of the CEC2013 test show that MSRCS has at least 12 functions that are superior to the original CS and its seven improved algorithms and ranks first, indicating better optimization ability in solving unimodal, multimodal, and combinatorial function problems.In addition, MSRCS yields better convergence speed and solution accuracy than three classical swarm intelligence optimization algorithms.