Cuckoo search with varied scaling factor

Cuckoo search (CS), inspired by the obligate brood parasitic behavior of some cuckoo species, iteratively uses Lévy flights random walk (LFRW) and biased/selective random walk (BSRW) to search for new solutions. In this study, we seek a simple strategy to set the scaling factor in LFRW, which can vary the scaling factor to achieve better performance. However, choosing the best scaling factor for each problem is intractable. Thus, we propose a varied scaling factor (VSF) strategy that samples a value from the range [0,1] uniformly at random for each iteration. In addition, we integrate the VSF strategy into several advanced CS variants. Extensive experiments are conducted on three groups of benchmark functions including 18 common test functions, 25 functions proposed in CEC 2005, and 28 functions introduced in CEC 2013. Experimental results demonstrate the effectiveness of the VSF strategy.     

Cuckoo search: recent advances and applications

Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and the same has been found to be efficient in solving global optimization problems. In this paper, we review the fundamental ideas of cuckoo search and the latest developments as well as its applications. We analyze the algorithm and gain insight into its search mechanisms and find out why it is efficient. We also discuss the essence of algorithms and its link to self-organizing systems, and finally, we propose some important topics for further research.     

多资源均衡优化的布谷鸟算法

针对标准多目标布谷鸟算法(CSA)后期收敛速度慢、收敛精度不高的缺陷,提出一种求解多资源均衡优化问题的改进多目标布谷鸟算法。首先,引入非均匀变异算子,以均衡算法的全局搜索能力和局部寻优能力;然后,引进差分进化算子,促进群体间的合作和信息交流,提高算法的收敛精度。通过算例测试表明,改进的多目标布谷鸟算法比标准多目标算法和VEPSO-BP算法具有更好的全局收敛性。     

基于差分进化的布谷鸟搜索算法

针对基本布谷鸟搜索算法局部搜索能力弱、寻优精度低等不足,提出了一种具有差分进化策略的改进布谷鸟搜索算法。该算法是在种群进入下一次迭代之前在其个体上增加两个带权的差来实现个体变异,再对其进行交叉、选择操作得到最优个体,使缺乏变异机制的布谷鸟搜索算法具有变异能力,从而提高布谷鸟搜索算法的多样性,避免种群个体陷入局部最优,增强算法全局寻优能力。对几种经典测试函数和1个典型应用实例进行测试,仿真实验结果表明,新算法具有更好的全局搜索能力,在收敛精度、收敛速度以及寻优成功率等性能上显著优于基本布谷鸟搜索算法。     

Cuckoo search based resource optimization of datacenters

With advancements in virtualization technology, datacenters are often faced with the challenge of managing large numbers of virtual machine (VM) requests. Due to this large amount of VM requests, it has become practically impossible to search all possible VM placements in order to find a solution that best optimizes certain design objectives. As a result, managers of datacenters have resorted to the employment of heuristic optimization algorithms for VM placement. In this paper, we employ the cuckoo search optimization (CSO) algorithm to solve the VM placement problem of datacenters. Firstly, we use the CSO to optimize the datacenter for the minimization of the number of physical machines used for placement. Secondly, we implement a multiobjective CSO algorithm to simultaneously optimize the power consumption and resource wastage of the datacenter. Simulation results show that both CSO algorithms outperform the reordered grouping genetic algorithm (RGGA), the grouping genetic algorithm (GGA), improved least-loaded (ILL) and improved FFD (IFFD) methods of VM placement.     

Cuckoo search with search strategies and proper objective function for brightness preserving image enhancement

Image enhancement can be formulated as an optimization problem where one parameterized transformation function is used for enhancement purpose. The proper enhancement significantly depends on two factors- fine tuning of the parameters of the corresponding parameterized transformation function and other one is the selection of a proper objective function. In this study a parameterized variant of histogram equalization (HE) has been used for enhancement purpose and to tune the parameters of that variant a modified cuckoo search (CS) with new global and local search strategies is employed. This paper also concentrates on the selection of a proper objective function to preserve the original brightness of the image. A new objective function has been developed by combining fractal dimension (FD) and quality index based on local variance (QILV). Visual analysis and experimental results prove that modified CS with search strategies outperforms the traditional and some other existing modified CS algorithms. Considering the image’s brightness preserving capability, the proposed objective function significantly outperforms other existing objective functions.     

基于种群特征反馈的布谷鸟搜索算法

布谷鸟搜索(CS) 算法是一种新型的生物启发式算法. 为了提高算法对不同优化问题的适应能力, 根据反馈控制原理提出一种基于种群特征反馈的布谷鸟搜索(SFFCS) 算法, 将年龄结构、变异成功率等种群特征作为反馈信息引入算法框架, 动态调节算法参数, 同时引入双进化策略机制和策略选择概率, 加强算法对局部搜索和全局搜索的平衡能力. 对标准测试函数和电力系统最优潮流问题进行数值实验, 实验结果表明, SFFCS 算法具有较好的收敛性能和适应能力, 验证了所提出算法的有效性和工程应用价值.

     

基于共轭梯度的布谷鸟搜索算法

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。     

基于Tempered Levy Flight随机游走模型的布谷鸟搜索算法

现实生活的众多领域中存在着各式各样的复杂网络,复杂网络搜索问题是复杂网络理论研究中的重要课题之一,对人们的生活具有重要的现实意义。布谷鸟搜索算法是一种启发式算法,利用Levy Flight能够快速寻找到全局最优解。通过研究复杂网络随机游走模型,并根据经典布谷鸟搜索算法,提出了一种新的改进的Tempered L?vy Flight搜索算法。通过几个经典函数测试表明：改进的算法提高了其搜索精度,加快了搜索算法的收敛速度。此外,改进的搜索算法还能够调整搜索范围,增加种群多样性,增强自适应效果,提高算法的整体性能。     

结合基因遗传和贪婪搜索的布谷鸟社区检测算法

为了提高复杂网络社区结构挖掘的精度,结合基因遗传和贪婪搜索提出一种面向模块度优化的布谷鸟社区检测算法(GGCSCA)。布谷鸟种群在有序邻居表上逐维随机游走,并采用优质基因遗传策略,使得种群高效优化,同时应用局部模块度增量最大化的贪婪偏好搜索算法快速提升种群质量,以取得好的社区划分结果。GGCSCA在基准网络和经典网络上进行了实验,并与一些典型算法进行对比,结果说明了本社区发现算法的有效性、准确性和快速收敛性,具有较强的社区识别能力,能够精细地检测出网络社区结构。     

基于Powell局部搜索策略的全局优化布谷鸟算法

为了解决布谷鸟搜索算法后期收敛速度慢、求解精度不高、易陷入局部最优等缺陷,提出了一种基于Powell局部搜索策略的全局优化布谷鸟搜索算法.算法将布谷鸟全局搜索能力与Powell方法的局部寻优性能有机地结合,并根据适应度值逐步构建精英种群候选解池在迭代后期牵引Powell搜索的局部优化,在保证求解速度、尽可能找到全局极值点的同时提高算法的求解精度.对52个典型测试函数实验结果表明,该算法相比于传统的布谷鸟搜索算法不仅寻优精度和寻优率有所提高,并且适应能力强、鲁棒性好,与最新提出的其他改进算法相比也具有一定的竞争优势.     

Edge magnitude based multilevel thresholding using Cuckoo search technique

Multilevel thresholding technique is popular and extensively used in the field of image processing. In this paper, a multilevel threshold selection is proposed based on edge magnitude of an image. The gray level co-occurrence matrix (second order statistics) of the image is used for obtaining multilevel thresholds by optimizing the edge magnitude using Cuckoo search technique. New theoretical formulation for objective functions is introduced. Key to our success is to exploit the correlation among gray levels in an image for improved thresholding performance. Apart from qualitative improvements the method also provides us optimal threshold values. Results are compared with histogram (first order statistics) based between-class variance method for multilevel thresholding. It is observed that the results of our proposed method are encouraging both qualitatively and quantitatively.     

Cuckoo search with differential evolution mutation and Masi entropy for multi-level image segmentation

Multimedia Tools and Applications - Since the beginning of the twenty-first century, the Cuckoo Search (CS) algorithm has emerged as one of the robust, flexible, fast, and easily implementable...     

混沌云模型多目标布谷鸟搜索算法

针对多目标布谷鸟搜索算法（MOCS）迭代后期寻优速度慢,并且容易造成局部最优等缺点,提出一种混沌云模型多目标布谷鸟搜索算法（CCMMOCS）。首先在进化过程中通过混沌理论对一般的布谷鸟巢位置在全局中寻求优化,以防落入局部最优;然后利用云模型对较好的布谷鸟巢位置局部优化来提高精度;最后将两种方法对比得到相对更好的解作为最优值以完成优化。对比误差估计值及多样性指标,由5个常用多目标测试函数仿真结果可知,CCMMOCS比传统多目标布谷鸟搜索算法、多目标粒子群算法（MOPSO）及多目标遗传（NSGA-Ⅱ）算法性能更好,Pareto前沿更接近理想曲线,分布也更均匀。     

Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems

In this study, a new metaheuristic optimization algorithm, called cuckoo search (CS), is introduced for solving structural optimization tasks. The new CS algorithm in combination with Lévy flights is first verified using a benchmark nonlinear constrained optimization problem. For the validation against structural engineering optimization problems, CS is subsequently applied to 13 design problems reported in the specialized literature. The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area. The optimal solutions obtained by CS are mostly far better than the best solutions obtained by the existing methods. The unique search features used in CS and the implications for future research are finally discussed in detail.     

Cuckoo search algorithm with membrane communication mechanism for modeling overhead crane systems using RBF neural networks

Developing a precise dynamic model is a critical step in the design and analysis of the overhead crane system. To achieve this objective, we present a novel radial basis function neural network (RBF-NN) modeling method. One challenge for the RBF-NN modeling method is how to determine the RBF-NN parameters reasonably. Although gradient method is widely used to optimize the parameters, it may converge slowly and may not achieve the optimal purpose. Therefore, we propose the cuckoo search algorithm with membrane communication mechanism (mCS) to optimize RBF-NN parameters. In mCS, the membrane communication mechanism is employed to maintain the population diversity and a chaotic local search strategy is adopted to improve the search accuracy. The performance of mCS is confirmed with some benchmark functions. And the analyses on the effect of the communication set size are carried out. Then the mCS is applied to optimize the RBF-NN models for modeling the overhead crane system. The experimental results demonstrate the efficiency and effectiveness of mCS through comparing with that of the standard cuckoo search algorithm (CS) and the gradient method.     

单纯形法的布谷鸟搜索算法及其在约束优化问题中的应用

为求解约束优化问题,针对布谷鸟搜索算法(CS)后期收敛速度慢,求解精度不高等不足,利用单纯形法局部搜索能力强的特点,提出了基于单纯形法的布谷鸟搜索算法(SMCS)。算法首先用CS算法进行全局搜索,再用单纯形法进行局部搜索。10个标准测试函数的实验结果表明,SMCS算法相对于CS算法有更强的寻优能力,再将算法用于求解减速器设计、伸缩绳设计、焊接条设计等约束优化问题。实验结果表明,CS算法和SMCS算法均能求出比其他文献更优的解,且SMCS算法求出的解更优、稳定性更强。     

Cuckoo search based hybrid models for improving the accuracy of software effort estimation

Microsystem Technologies - This research proposes a new approach which is based on Cuckoo Search algorithm for the prediction of software development effort. It uses Cuckoo Search for discovering...     

Cuckoo search (CS)-NFC-based UPQC for compensating voltage sag of nonlinear load

In this paper, a cuckoo search (CS) algorithm-based neuro-fuzzy controller (NFC) is developed to improve the performance of unified power quality conditioner (UPQC). The CS algorithm is used for optimising the output of neural network (NN) so that the classification output of the NN is enhanced. The inputs of the networks are error and change of error voltage of the PQ issue signal of nonlinear load which are calculated by comparison with the reference signal. Next, the output of network, i.e. regulated (compensated) voltage, is optimised by the CS algorithm. From the output of CS, an optimum rule-based fuzzy interference system is developed and the PQ problem is compensated. The CS-NFC-based UPQC is implemented in MATLAB/Simulink and the PQ issue clearing performance is analysed. The PQ issue clearing performance of proposed UPQC is compared with traditional UPQC, NFC-UPQC, GA-NFC-UPQC and adaptive GA-NFC-UPQC. The CS-NFC-based UPQC controller has lesser error deviation of 2.8% with traditional UPQC, 2.12% with NFC, 1.7% with GA-NFC and 0.6% with adaptive GA-NFC.     

Substring search and repeat search using factor oracles

We present some simple but useful properties of factor oracles, and propose fast algorithms for indexed full-text search and finding repeated substrings. Some experiments are given to demonstrate the performance of our algorithms.