DCS算法求解带软时间窗的送取货一体化VRP

为求解带软时间窗的送取货一体化车辆路径问题,提出一种离散布谷鸟(DCS)搜索算法。该算法在基本布谷鸟搜索算法基础之上,对莱维飞行获取新鸟巢和以一定概率抛弃鸟巢的迭代公式进行重新定义;同时设计客户序列和车辆序列双倍体鸟巢,并对客户序列采用交换、逆序、插入操作和对每辆车的访问路线采用2-Opt操作。通过5个算例的仿真实验和相关文献比较,结果表明所提DCS算法行之有效。

基于ICS算法的列车运动模型参数辨识

列车运动模型参数辨识对其运行控制至关重要,文章采用单质点列车模型,对运行列车进行受力分析,建立列车运动模型,并提出利用改进的布谷鸟搜索(ICS)算法对列车基本阻力参数进行辨识;基于真实的列车运行数据,对基本阻力参数辨识进行仿真分析,并基于TD3算法对参数辨识的有效性进行仿真验证。仿真结果表明,基本阻力参数辨识对列车运行控制十分必要,相对于经验参数,辨识参数下的列车基本阻力更为准确,且辨识参数下的牵引能耗有一定的降低。

Adaptive allocation strategy for cooperatively jamming netted radar system based on improved cuckoo search algorithm

The jamming resource allocation problem of the aircraft formation cooperatively jamming netted radar system is investigated.An adaptive allocation strategy based on dynamic adaptive discrete cuckoo search algorithm(DADCS)is proposed,whose core is to adjust allocation scheme of limited jamming resource of aircraft formation in real time to maintain the best jamming effectiveness against netted radar system.Firstly,considering the information fusion rules and different working modes of the netted radar system,a two-factor jamming effectiveness evaluation function is constructed,detection probability and aiming probability are adopted to characterize jamming effectiveness against netted radar system in searching and tracking mode,respectively.Then a nonconvex optimization model for cooperatively jamming netted radar system is established.Finally,a dynamic adaptive discrete cuckoo search algorithm(DADCS)is constructed by improving path update strategies and introducing a global learning mechanism,and a three-step solution method is proposed subsequently.Simulation results are provided to demonstrate the advantages of the proposed optimization strategy and the effectiveness of the improved algorithm.

Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

The existing Maximum Power Point Tracking(MPPT)method has low tracking efficiency and poor stability.It is easy to fall into the Local Maximum Power Point(LMPP)in Partial Shading Condition(PSC),resulting in the degradation of output power quality and efficiency.It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms,and their performance in tracking the Global Maximum Power Point(GMPP)varies.Thus,a Cuckoo search algorithm(CSA)combined with the Incremental conductance Algorithm(INC)is proposed(CSA-INC)is put forward for the MPPT method of photovoltaic power generation.The method can improve the tracking speed by more than 52%compared with the traditional Cuckoo Search Algorithm(CSA),and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization(PSO)and the Gravitational Search Algorithm(GSA).CSA-INC has an average tracking efficiency of 99.99%and an average tracking time of 0.19 s when tracking the GMPP,which improves PV power generation’s efficiency and power quality.

基于IMOCS-BP神经网络的锂离子电池SOH估计

锂离子电池随着循环充放电次数的增长,其健康状态SOH(state-of-health)会随之发生一定程度的衰减。针对以上问题,设计了一种基于改进的多目标布谷鸟搜索IMOCS(improved multi-objective Cuckoo search)-BP神经网络的锂离子电池健康状态估计方法,在避免算法陷入局部最优的同时自适应改变布谷鸟搜索CS(Cuckoo search)算法更新概率和搜索步长,解决CS算法收敛速度慢和求解精度低的问题。以IMOCS算法和BP神经网络结合,对节点空间范围进行全局搜索,降低权值和阈值的初值对BP神经网络的影响,实现参数优化。通过Matlab仿真,验证了基于IMOCS-BP神经网络的SOH估计算法误差低、性能强,实现了锂电池SOH的精准预测。

基于CNN-LSTM-CS工业管道腐蚀率预测模型

针对传统工业管道腐蚀率预测模型存在特征提取依赖人工经验和泛化能力不足的问题,本文将卷积神经网络(convolutional neural network,CNN)和长短期记忆网络(long short-term memory,LSTM)相结合,提出了基于布谷鸟优化算法(cuckoo search,CS)的CNN-LSTM-CS网络模型,实现对工业管道腐蚀率预测.首先,对采集的管道腐蚀数据集进行归一化预处理;然后,利用CNN网络提取影响管道腐蚀率因素的深层次特征信息,并通过训练LSTM网络构建CNN-LSTM预测模型;最后,采用CS算法对预测模型进行参数优化,减少预测误差,实现腐蚀率的精准预测.实验结果表明,对比几种典型的腐蚀率预测方法,本文提出的方法具有更高的预测精度,为工业管道腐蚀率检测提供新的思路.

基于ICS-IP&O的光伏最大功率跟踪

针对光伏组件存在局部阴影条件(PSC)时,功率输出曲线呈现出的多峰现象,传统最大功率点追踪方法容易陷入局部最优情况。为了解决上述问题,提出一种将自适应布谷鸟搜索(ICS)算法和变步长扰动观察法(IP&O)相结合的复合算法(ICS-IP&O)。对布谷鸟搜索方法(CS)的切换概率、Lévy飞行步长系数进行非线性自适应优化,使其满足迭代前、后期不同的需求,加快收敛进程。在偏好随机游走部分,引入粒子群算法思想,对位置更新公式进行优化,提高其多样性,使算法具有较强的全局搜索随机性,降低陷入局部最优的可能性。算法后期切换成IP&O搜索,减小振荡。通过Simulink进行仿真测试,并与粒子群算法(PSO)、布谷鸟算法(CS)进行对比。结果表明,该算法在静态、局部遮阴、动态遮阴条件下均具有更好的收敛速度和精度。

CSFW-SC: Cuckoo Search Fuzzy-Weighting Algorithm for Subspace Clustering Applying to High-Dimensional Clustering

Aimed at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is presented on the basis of the exited soft subspace clustering algorithm. In the proposed algorithm, a novel objective function is firstly designed by considering the fuzzy weighting within-cluster compactness and the between-cluster separation, and loosening the constraints of dimension weight matrix. Then gradual membership and improved Cuckoo search, a global search strategy, are introduced to optimize the objective function and search subspace clusters, giving novel learning rules for clustering. At last, the performance of the proposed algorithm on the clustering analysis of various low and high dimensional datasets is experimentally compared with that of several competitive subspace clustering algorithms. Experimental studies demonstrate that the proposed algorithm can obtain better performance than most of the existing soft subspace clustering algorithms.

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.

Cuckoo search algorithm-based optimal deployment method of heterogeneous multistatic radar for barrier coverage

This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence.Based on these properties and considering location restrictions,it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars.To overcome the non-convexity of the model and the non-analytical nature of the objective function,an algorithm combining integer line programming and the cuckoo search algorithm(CSA)is proposed.The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost.Simulations are conducted in different conditions to verify the effectiveness of the proposed method.

Ensuring Information Security in Electronic Health Record System Using Cryptography and Cuckoo Search Algorithm

In the contemporary era,the abundant availability of health information through internet and mobile technology raises concerns.Safeguarding and maintaining the confidentiality of patients’medical data becomes paramount when sharing such information with authorized healthcare providers.Although electronic patient records and the internet have facilitated the exchange of medical information among healthcare providers,concerns persist regarding the security of the data.The security of Electronic Health Record Systems(EHRS)can be improved by employing the Cuckoo Search Algorithm(CS),the SHA-256 algorithm,and the Elliptic Curve Cryptography(ECC),as proposed in this study.The suggested approach involves usingCS to generate the ECCprivate key,thereby enhancing the security of data storage in EHR.The study evaluates the proposed design by comparing encoding and decoding times with alternative techniques like ECC-GA-SHA-256.The research findings indicate that the proposed design achieves faster encoding and decoding times,completing 125 and 175 iterations,respectively.Furthermore,the proposed design surpasses other encoding techniques by exhibiting encoding and decoding times that are more than 15.17%faster.These results imply that the proposed design can significantly enhance the security and performance of EHRs.Through the utilization of CS,SHA-256,and ECC,this study presents promising methods for addressing the security challenges associated with EHRs.

基于ICS优化RBF的水库水质三维预测方法

针对已有水质预测模型在数据降噪、网络参数初始值设置和优化、精度提高等方面能力的不足,构建了一种优化的水质三维预测模型。利用主成分分析算法筛选出水质关键参数,并基于自适应噪声的完全集合经验模态分解算法结合小波阈值模型对三维水质参数和气象数据降噪处理,使用3维卷积神经网络(Three-dimensional convolutional neural networks,3-D CNN)提取出特征数据集,自编码器(Autoencoder,AE)获得径向基函数(Radial basis function,RBF)网络参数初始化值,改进布谷鸟搜索算法(Improved cuckoo search,ICS)优化更新网络中超参数动态初始化值。广东省湛江市徐闻县大水桥水库区域22个典型在线监测站点以及6个手持监测点的实测数据对比验证结果表明,浊度和藻密度分别与总氮含量强正相关,叶绿素含量与气温强正相关,所提出的水质预测模型在5个典型精准性评价指标方面优于已有文献方法。研究成果可为管理部门和研究者对水质监测提供参考。

基于CS算法优化的SVM短时交通流预测模型

为了提高短时交通流预测模型的准确度,提出一种基于布谷鸟搜索算法(Cuckoo Search,CS)优化的支持向量机(Support Vector Machine,SVM)短时交通流预测模型(CS-SVM)。选取青岛市内的多组典型城市路段作为研究对象,将观测收集的车流量数据作为学习样本。利用CS算法对SVM模型的主要参数进行优化,建立以SVM为基础的短时交通流预测模型。最后将CS-SVM模型与多种现有模型进行仿真分析。结果表明,CS-SVM模型相比其他传统模型具有更低的预测误差和更好的稳定性,CS-SVM模型相比SVM模型的MAE值下降了6.56%,RMSE值下降了7.36%。因此该模型能够为城市交通出行和交通流理论研究提供有效帮助。

基于MPL-CS自适应反步法的过失速机动控制

超机动能力仍是未来飞机的重要性能指标,而过失速机动控制的效果将成为决定近距空战胜负的关键。本文提出了一种基于最小参数学习和布谷鸟搜索(MPL-CS)的自适应反步法的过失速机动控制方法,以解决先进布局飞机过失速机动中严重的非线性、耦合性和迟滞性等导致的鲁棒性差和控制精度低等问题。首先,基于一套完整的先进布局飞机缩比模型大振幅振荡风洞试验数据,在给定的建模精度目标下,通过改进极限学习机(ELM)方法,建立了大迎角下先进布局飞行器精确的非定常气动模型。其次,设计了一种基于MPL的自适应反步法,以减少需要优化的参数数量。在不确定性和模型扰动的影响下,结合串接链分配方法完成了分配设计。基于CS方法对MPL下自适应反步控制律的关键参数进行了优化。最后,经典的眼镜蛇机动仿真结果表明,该方法的控制精度高于传统的基于MPL的自适应反步方法,且充分考虑了工程的实际需求,控制精度高、鲁棒性强。该方法为未来先进布局飞机的过失速机动控制提供了理论支撑和技术路径。

数字孪生下基于DACS-MFAC的数控机床热误差自适应预测方法

基于现代控制理论的经验建模法,在针对数控机床不同生产工况时,难以建立一种共性的热误差解决方案。探索了在数字孪生框架下以无模型驱动方式实现数控机床热误差自适应预测的研究。首先,建立了机床“热传感-映射-融合与优化-驱动”数字孪生框架,实现热特征信息在数字孪生体中的存储与融合。然后,基于MISO系统的假设条件和动态线性化几何释义,提出了一种不受被控系统任何结构数据影响的热误差无模型自适应控制(MFAC)方法。进一步,基于动态发现概率和自适应步长的DACS-MFAC算法按照一定的周期更新系统参数,实现数字孪生系统下热误差预测值的动态优化。实验结果表明,DACS-MFAC方法具有适应强、精度高、收敛性好等优点。

基于IMOCS算法的跨流域水资源多目标优化调配

为有效求解跨流域水资源多目标优化调配问题,提出一种改进多目标布谷鸟算法(improved multi-objective cuckoo search algorithm,IMOCS)。针对多目标布谷鸟优化算法(multi-objective cuckoo search algorithm,MOCS)收敛速度慢、容易陷入局部最优解的缺点,引入混沌理论和变异机制,采用自适应发现概率和步长改进MOCS,形成IMOCS算法。以南水北调东线工程江苏段为例,构建跨流域水资源多目标调配模型,分别采用IMOCS和MOCS求解模型,并运用基于组合赋权的非负矩阵分解法对2种算法所得的Pareto解集进行评价。结果表明:IMOCS在收敛性、多样性和综合性能方面优于MOCS,能够得到更高质量的Pareto解集;相较于50%、75%和95%来水频率下的MOCS所求解的最优配置方案,IMOCS所求解的最优配置方案缺水总量减少0.21亿、0.51亿和0.07亿m~3,损失水量分别减少了0.13亿、1.53亿和1.11亿m~3。因此,IMOCS可为跨流域水资源多目标优化配置计算提供有效的算法参考。

基于CSA优化RBF网络的液压泵典型故障诊断研究

液压泵具有高负载的特性,在液压机上广泛应用。为了提高液压泵典型故障诊断精度,设计了一种基于布谷鸟搜索算法(CSA)优化径向基函数(RBF)网络的液压泵典型故障诊断方法,然后对对液压泵典型故障进行实验测试。研究结果表明:对比PSO-RBF与UKF-RBF发现,CSA算法在寻优能力方面获得了明显提升,表现出来很高的收敛效果。CSA-RBF故障诊断水平最高,故障诊断缩减了总体耗时,检测平均精度能够达到97%以上,具有很高的精度保障。该方法有助于提高液压泵的使用寿命,为后续的参数优化奠定一定的理论基础。

基于Attention-CS-LSTM乙烯裂解炉管温度预测

在乙烯生产过程中,针对乙烯裂解炉管温度难监测的情况,需要对传统的温度测量方法进行改进,通过数据模型下的优化操作可以有效预测乙烯裂解炉出口温度,当出现温度波动时进行干预,提高产品效率和生产安全。文章将改进的布谷鸟算法优化LSTM(CS-LSTM)应用于真实工业数据,并与四种模型进行比较。仿真结果表明,采用Attention-CS-LSTM预测准确率明显提高,且具有良好的稳态准确度,该方法的温度预测准确率为95%。

一种自适应CS算法及其在风电齿轮箱故障诊断中的应用

针对布谷鸟搜索(CS)算法易出现早熟收敛以及风电机组齿轮箱的故障模式难以有效识别等问题,提出一种基于自适应CS算法的BP神经网络(SaCS-BP)智能诊断技术。通过构建SaCS算法,实现了步长和发现概率的自适应调整,并采用一组基准函数测试了该算法的有效性;将SaCS与BP神经网络进行融合,构建了风电齿轮箱的故障诊断模型。结果表明,SaCS算法具有较佳的寻优精度和普适性。此外,与BP神经网络以及布谷鸟搜索算法优化BP网络(CS-BP)相比,SaCS-BP算法获得了最高的诊断准确度,从而实现了风电齿轮箱故障模式的有效识别。