The application of one rank cuckoo search algorithm for solving economic load dispatch problems

In this paper, a one rank cuckoo search algorithm (ORCSA) is proposed for solving economic load dispatch (ELD) problems. The main objective of the ELD problem is to minimize total cost of thermal generators while satisfying power balance constraint, prohibited operating zones, ramp rate constraints and operating limits of generators. Moreover, the generating units considered in this paper have different characteristics such as quadratic fuel cost function, nonconvex fuel cost function and multiple fuel options. The proposed ORCSA method has been developed by performing two modifications on the original cuckoo search algorithm (CSA) to improve optimal solution quality and computational time. The first modification is to merge new solution generated from both Lévy flights and replacement a fraction of egg together and to evaluate and rank the solutions at once only. A bound by best solution mechanism has been used in the second modification for properly handling the inequality constraints. The proposed ORCSA method has been tested on different systems with different characteristics of thermal units and constraints. The results obtained by ORCSA have been compared to those from other methods available in the literature and the result comparison has indicated that the ORCSA method can obtain better solution quality than many other methods. Therefore, the proposed ORCSA can be a very effective and efficient method for solving ELD problems.     

城市灾区无人机网络自适应覆盖优化算法

城市灾区中,地面用户节点的移动特性使得应急网络覆盖成为难题。针对城市灾区移动用户节点的应急网络覆盖优化问题,提出一种无人机网络自适应覆盖优化算法。对布谷鸟搜索算法进行改进,并对目标函数进行优化调整,将城市灾区地面用户节点的移动模型应用于改进的布谷鸟算法模拟中,最终实现对城市灾区重点区域移动用户的自适应覆盖优化。仿真结果表明,所提算法与相同实验环境下的标准布谷鸟算法（CSA）和模拟退火算法（SAA）相比,对重点区域的覆盖率分别提升了2.98个百分点和1.87个百分点。多次实验表明无人机网络的覆盖率、连通性及路径损耗稳定,且随着仿真时间变化,应急网络的性能稳定。证明了该算法不仅能够对城市灾区移动节点提供稳定的动态网络覆盖,有较强的全局以及局部寻优能力且能够更加有效地提高对重点区域的覆盖率。     

移动感知环境下基于CSA-SSVR的交通状态预测方法

相较于传统感知网络,移动群智感知网络在部署和维护成本上有着较大优势,在智能交通系统中得到了越来越多的应用。交通状态的预测对交通管理系统具有重要意义,从移动群智感知环境下获取的车速数据出发,以支持向量回归算法(SVR)为基础,引入周期性算子,并采用布谷鸟算法(CSA)确定周期性SVR(SSVR)中的主要参数,提出了CSA-SSVR,对道路未来车速进行预测,据此判断道路的未来交通状态。实验表明,CSA-SSVR在移动群智感知环境下对于交通状态预测问题的准确性较高。     

Study impact of various load models on DG placement and sizing using backtracking search algorithm

In this article, a meta-heuristic technique based on a backtracking search algorithm (BSA) is employed to produce solutions to ascertain distributed generators (DGs). The objective is established to reduce power loss and improve network voltage profile in radial distribution networks by determining optimal locations and sizes of the DGs. Power loss indices and bus voltages are engaged to explore the initial placement of DG installations. The study cares with the DG type injects active and reactive power. The proposed methodology takes into consideration four load models, and their impacts are addressed. The proposed BSA-based methodology is verified on two different test networks with different load models and the simulation results are compared to those reported in the recent literature. The study finds that the constant power load model among various load models is sufficed and viable to allocate DGs for network loss and voltage studies. The simulation results reveal the efficacy and robustness of the BSA in finding the optimal solution of DGs allocation.     

自适应动态邻域布谷鸟混合算法求解TSP问题

针对离散布谷鸟算法求解旅行商问题时邻域搜索效率低和易陷入局部最优解等问题,提出了一种自适应动态邻域布谷鸟混合算法（Adaptive Dynamic Neighborhood Hybrid Cuckoo Search algorithm,ADNHCS）。为了提升邻域搜索效率,设计了一种圆限定突变的动态邻域结构来降低经典算法的随机性;此外,提出了可根据迭代过程进行自适应参数调整的策略,并结合禁忌搜索算法来提升全局寻优的能力。使用MATLAB和标准TSPLIB数据库中的若干经典算例对算法性能进行了实验仿真,结果表明与其他基于布谷鸟算法、经典和新型群智能优化算法相比,ADNHCS算法在全局寻优能力以及稳定性方面表现更优。     

基于布谷鸟搜索算法的无线传感器网络节点定位

无线传感器网络的节点定位实际上是解决测量距离和测距误差的多维约束优化问题。针对最小二乘方法对测距误差敏感的不足,提出一种基于布谷鸟搜索算法的无线传感器网络节点定位算法。该算法利用全局和局部寻优能力强的布谷鸟算法求解定位过程中的多维约束优化问题;通过设定相应的约束适应度函数来减小定位过程的搜索范围,加快了收敛速度,能够快速地确定未知节点的位置。仿真结果表明：相较于粒子群算法和最小二乘算法,该算法能有效地抑制测距误差对定位的影响,提高节点的定位精度,具备很好的实用性。     

云模型的布谷鸟搜索算法*

针对布谷鸟搜索算法(CS)存在的不足,优化布谷鸟搜索算法求解连续函数问题的性能,结合云模型在定性与定量之间相互转换的优良特性,设计出云模型的布谷鸟搜索算法(CCS)。其核心思想是通过云模型实现布谷鸟的进化学习过程,类似差分进化进行群体间的信息交流。经过10个测试函数的实验仿真,测试结果表明该文算法能有效改善求解连续函数优化问题的性能。同时,针对连续函数优化问题,该算法与其它算法相比是有更好性能的优化算法。     

参数动态调整的自适应布谷鸟算法

为提高布谷鸟算法的收敛速度和求解精度,提出了一种基于自适应机制的改进布谷鸟算法。该算法在迭代初期和末期分别使用两种自适应策略来动态调整步长和发现概率,提高了算法的局部和全局寻优能力。利用10个标准测试函数对基本布谷鸟算法、所提出的改进算法以及其他智能优化方法进行了仿真对比验证,结果表明所提出的改进布谷鸟算法在求解精度、稳定性以及收敛速度上都具有一定优势。     

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。     

A new hybrid correction method for short-term load forecasting based on ARIMA,SVR and CSA

Accurate load-forecasting problem is a significant and vital issue, especially in the new competitive electricity market. The models that are employed for forecasting purposes would determine how reliable the last forecasted results are. Therefore, this paper proposes a new hybrid correction method based on autoregressive integrated moving average (ARIMA) model, support vector regression (SVR) and cuckoo search algorithm (CSA) to achieve a more reliable forecasting model. The proposed method gets use of the autocorrelation function (ACF) and the partial ACF to search the stationary or non-stationary behaviour of the investigated time series. In the case of non-stationary data, it will be differenced one or more times to become stationary. After that, Akaike information criterion is utilised to find the appropriate ARIMA model such that the linear component of the data would be captured. Therefore, the ARIMA residuals would contain the non-linear components that should be modelled by use of the SVR model. The role of CSA as a successful optimisation algorithm is to find the optimal SVR parameters for more accurate forecasting. Meanwhile, a novel self-adaptive modification method based on CSA is proposed to empower the total search ability of the algorithm effectively. The proposed method is applied to the empirical peak load data of Fars Electrical Power Company in Iran.     

求解混合流水车间调度问题的离散布谷鸟算法

为求解混合流水车间调度问题,提出一种离散布谷鸟算法。针对常规解码方法难以获得最优解的缺点,提出一种改进的解码方法,基于工件数与并行机数,按概率随机分配机器;根据标准布谷鸟算法中莱维飞行和巢寄生行为两种位置更新策略的核心思想,提出基于位置交叉和个体距离的离散莱维飞行,设计基于最优插入和最优交换的巢寄生策略。最后算例对比实验结果显示,采用基于改进解码方法的离散布谷鸟算法求解所得结果的平均值最小,验证了改进解码方法能提高解的质量;实例测试所得结果均获得了当前最优解,验证了离散布谷鸟算法求解该类问题的优越性。     

多智能体布谷鸟算法的网络计划资源均衡优化

网络计划资源均衡属于组合优化问题,为了能快速有效地求解此类问题,提出了一种多智能体布谷鸟算法。针对标准布谷鸟算法缺乏信息共享的缺陷,将多智能体系统引入布谷鸟算法中。多智能体的邻域竞争合作算子实现智能体间信息的交流,加快算法收敛速度;变异算子扩大搜索范围增加种群多样性;自学习算子提高局部寻优的能力;布谷鸟算法的Levy飞行进化机制能有效地跳出局部最优实现全局收敛。实例仿真结果证实了,与其他算法相比多智能体布谷鸟算法能更有效地求解网络计划资源均衡优化问题。     

DG placement in radial distribution network by symbiotic organisms search algorithm for real power loss minimization

Incorporation of distributed generation (DG) in distribution network may reduce the network loss if DG of appropriate size is placed at proper strategic location. The current article presents determination of optimal size and location of DG in radial distribution network (RDN) for the reduction of network loss considering deterministic load demand and DG generation using symbiotic organisms search (SOS) algorithm. SOS algorithm is a meta-heuristic technique, inspired by the symbiotic relationship between different biological species. In this paper, optimal size and location of DG are obtained for two different RDNs (such as, 33-bus and 69-bus distribution networks). The obtained results, using the proposed SOS, are compared to the results offered by some other optimization algorithms like particle swarm optimization, teaching-learning based optimization, cuckoo search, artificial bee colony, gravitational search algorithm and stochastic fractal search. The comparison is done based on minimum loss of the distribution network as well as based on the convergence mobility of the fitness function offered by each of the comparative algorithms for both the networks under consideration. It is established that the proposed SOS algorithm offers better result as compared to other optimization algorithms under consideration. The results are also compared to the existing solution available in the literature.     

求解TSP问题的自适应离散型布谷鸟算法

对于求解的TSP问题,提出了一种自适应离散型布谷鸟算法（Adaptive Discrete Cuckoo Search,ADCS）。在基于布谷鸟搜索算法（Cuckoo Search,CS）的搜索原理下构造TSP问题的路径求解策略。针对离散型算法整体调整容易破坏已形成的较优路径和随着算法迭代数目增加导致种群多样性下降这两个缺陷,设计了一种针对路径的自适应型局部调整算子和全局随机扰动策略,采用了简单的2-opt优化算子作为局部优化算子以加快算法的收敛速度。最后采用多组不同规模的标准TSPLIB数据与其他的优化算法进行对比实验,结果表明ADCS算法在求解精度和稳定性方面具有优势。     

Minimization of Energy Losses in Electric Power Distribution Systems by Intelligent Search Strategies

Energy is continuously dissipated in electric power systems due to electrical resistance in transmission and distribution lines. This paper addresses the problem of obtaining a network topology with minimum energy losses for electric power distribution systems. As distribution networks must operate radially, the problem can be formulated as a generalization of the minimum spanning tree problem. The generalization is due to variation in costs as network configuration changes. Nonlinear network flow techniques are teamed with search strategies borrowed from the field of artificial intelligence to overcome computation intractability.     

基于布谷鸟算法优化BP神经网络的锂电池健康状态预测

锂电池健康状态(SOH)的预测是电动汽车锂电池管理系统的最重要的关键技术之一;传统的误差逆向传播(BP)神经网络容易使权值和阈值陷入局部最优,从而导致预测结果不精确;结合布谷鸟搜索算法(CS)的全局优化能力,提出一种基于CS算法优化BP神经网络的锂电池SOH预测方法,该方法的核心在于优化BP神经网络的初始权值和阈值,从而减小算法对初始值的依赖;为了验证算法的泛化性,利用美国国家航空航天局开源锂电池数据集6号电池和7号电池进行仿真实验,仿真得到该算法预测SOH的均方根误差(RMSE)分别为0.2658和0.2620,平均绝对百分比误差(MAPE)分别为0.3319%和0.2605%;通过与BP神经网络、粒子群优化的BP神经网络(PSO-BP)、遗传算法优化的BP神经网络(GA-BP)对比,布谷鸟算法优化的BP神经网络(CS-BP)具有更小的预测误差。     

汽车混流装配线的混合布谷鸟算法排序研究

为解决汽车混流装配线作业者工作负荷不均衡的问题,构建了最小化违背装配频率上限次数的优化模型,提出了布谷鸟算法与遗传算法相结合的混合算法。该方法将遗传算法的选择与交叉思想引入布谷鸟算法的迭代过程,以克服布谷鸟算法寻优过程中收敛速度慢和容易陷入局部最优的问题。测试函数的对比求解和合作汽车企业的优化实例表明该改进算法具有更高的求解精度和更快的收敛速度,能有效地解决大规模的汽车混流装配线排序优化问题。     

存在整数约束的分布式驱动变体飞行器控制分配

针对一类含有整数控制约束的分布式驱动变体飞行器在线控制分配问题,提出了一种基于布谷鸟搜索算法的求解策略.首先,考虑到指令分配误差以及作动器的损耗,将飞行器在线作动器控制分配问题表示为一类整数规划问题.然后,设计改进的布谷鸟搜索算法求解作动器的控制指令,采用Tent混沌映射初始化种群、自适应的步长控制量等措施提高算法的搜索效率.最后,将提出的控制分配方法应用于一类新型操纵面飞行器,仿真结果验证了该方法的有效性.     

ACOCS:一种云环境下资源调度混合算法

蚁群算法在优化组合问题中有着重要的意义,传统的蚁群调度算法搜索速度慢、容易陷入局部最优。针对这种情况,结合布谷鸟搜索算法,提出一种基于蚁群算法与布谷鸟搜索算法的混合算法(ACOCS),用于云环境下的资源调度。该方法有效保留了蚁群算法求解精度高和鲁棒性的特性,并融入了布谷鸟搜索具有快速全局搜索能力的优势。仿真实验结果表明,提出的ACOCS调度算法有效减少了调度所需的响应时间,也在一定程度上提高了系统资源利用率。     

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.