Structural reliability analysis using enhanced cuckoo search algorithm and artificial neural network

The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.

基于CSA-INC算法的光伏发电MPPT仿真研究

为提高最大功率点追踪的速度与准确性,并减小光伏阵列在局部阴影时光伏发电系统输出功率的损失与谐波含量,提出基于布谷鸟算法(CSA:Cuckoo Search Algorithm)和电导增量法(INC:Incremental Conductivity method)相结合的控制方法。在算法前期利用布谷鸟算法进行全局搜索,避免算法陷入局部最优解。后期利用电导增量法进行局部范围内细致搜索,锁定最大功率点。并且将此算法应用于并网控制,验证是否满足并网谐波含量要求。在Matlab/Simuink中建立了仿真模型,结果表明基于布谷鸟与电导增量法相结合的复合算法追踪速度更快,误差更小,满足并网谐波含量要求。

Modified Cuckoo Search Algorithm to Solve Economic Power Dispatch Optimization Problems

A modified cuckoo search(CS) algorithm is proposed to solve economic dispatch(ED) problems that have nonconvex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones, transmission losses and ramp rate limits. Comparing with the traditional cuckoo search algorithm, we propose a self-adaptive step size and some neighbor-study strategies to enhance search performance.Moreover, an improved lambda iteration strategy is used to generate new solutions. To show the superiority of the proposed algorithm over several classic algorithms, four systems with different benchmarks are tested. The results show its efficiency to solve economic dispatch problems, especially for large-scale systems.

Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method

We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems.This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy.Experiments are conducted on the Lorenz system and the Chen system.The proposed algorithm is used to estimate the parameters for these two systems.Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained.

用于碳酸盐岩储层裂缝检测的GWO-CS-BP算法及应用研究

碳酸盐岩储层中的裂隙是油气的运移通道和储集空间,对于油气勘探、开发和评价都具有重要的指导意义。针对研究区碳酸盐岩储层裂缝检测的难题,提出灰狼布谷鸟优化BP算法(GWO-CS-BP),该算法是将GWO-CS(grey wolf-cuckoo search algorithm)与BP(back propagation)相结合形成的裂隙检测方法。将含裂缝信息的相干、曲率、倾角、方位角和构型张量等属性作为GWO-CS-BP神经网络的输入数据,在工区地质资料约束下根据测井数据获得裂缝发育水平评价指标,进而对研究区裂缝发育水平进行评价并划分等级。研究区碳酸盐岩储层裂缝发育水平检测结果表明,GWO-CS-BP算法能够综合各属性特点对研究区的裂缝发育水平特征进行二次误差控制,获得裂缝发育水平评价指标f s并将研究区裂缝发育水平划分为3个等级及4个裂缝存在区域。其中,当研究区裂缝发育水平参数的值适中时,即f s的值大于4.0且小于5.8时,C区域最有利于油气的聚集,高产井的分布数量较多。利用GWO-CS-BP算法对研究区的裂缝发育水平进行了精细评价,并得出裂隙发育水平参数f s,实现了GWO-CS算法改进的BP神经网络在裂缝检测中的有效应用。

基于CSA-AFSA算法的集装箱港口连续型泊位分配优化

为提升集装箱港口运营效率,笔者研究了考虑潮汐因素与泊位偏好的连续型泊位分配问题。引入了船舶时空矩形不可重叠约束和潮汐时间窗约束,构建以最小化船舶等待、延迟离港、泊位偏离以及在港期间油耗费用和最小为目标的混合整数线性规划模型;根据模型特征,采用CPLEX求解软件、鱼群算法、布谷鸟搜索算法和布谷鸟鱼群混合算法进行求解,以计划周期为36 h的20个不同规模的船舶到港数据为研究算例,通过算例求解得到符和潮汐时间窗、泊位偏好等要求的泊位分配方案。算例求解表明:算例规模较小时,CPLEX可以在较短时间内求出最优泊位分配方案;算例规模较大时,CPLEX求解时间较长,布谷鸟鱼群混合算法可以在平均3 min内求出与CPLEX差距为0.39%~4.20%的次优解;不同算法间的对比表明,布谷鸟鱼群混合算法求解能力更优,混合算法所得泊位分配方案中,乘潮船舶的进出港时刻均在潮汐高水位时段,且85%以上的船舶在偏好泊靠点200 m内接受装卸服务。

Application of SVM and PCA-CS algorithms for prediction of strip crown in hot strip rolling

To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip rolling.Meanwhile,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple indicators.Furthermore,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a comparison.Besides,the prediction performance comparisons of the three models are discussed.Finally,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed.Furthermore,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μm.Especially,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.

Wind speed forecasting based on wavelet decomposition and wavelet neural networks optimized by the Cuckoo search algorithm

Wind speed forecasting is of great importance for wind farm management and plays an important role in grid integration. Wind speed is volatile in nature and therefore it is difficult to predict with a single model. In this study, three hybrid multi-step wind speed forecasting models are developed and compared — with each other and with earlier proposed wind speed forecasting models. The three models are based on wavelet decomposition(WD), the Cuckoo search(CS) optimization algorithm, and a wavelet neural network(WNN). They are referred to as CS-WD-ANN(artificial neural network), CS-WNN, and CS-WD-WNN, respectively. Wind speed data from two wind farms located in Shandong, eastern China, are used in this study. The simulation result indicates that CS-WD-WNN outperforms the other two models, with minimum statistical errors. Comparison with earlier models shows that CS-WD-WNN still performs best, with the smallest statistical errors. The employment of the CS optimization algorithm in the models shows improvement compared with the earlier models.

A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization

Constrained optimization problems are very important as they are encountered in many science and engineering applications.As a novel evolutionary computation technique,cuckoo search(CS) algorithm has attracted much attention and wide applications,owing to its easy implementation and quick convergence.A hybrid cuckoo pattern search algorithm(HCPS) with feasibility-based rule is proposed for solving constrained numerical and engineering design optimization problems.This algorithm can combine the stochastic exploration of the cuckoo search algorithm and the exploitation capability of the pattern search method.Simulation and comparisons based on several well-known benchmark test functions and structural design optimization problems demonstrate the effectiveness,efficiency and robustness of the proposed HCPS algorithm.

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。

Control allocation for aircraft with input constraints based on improved cuckoo search algorithm

The control allocation problem of aircraft whose control inputs contain integer constraints is investigated. The control allocation problem is described as an integer programming problem and solved by the cuckoo search algorithm. In order to enhance the search capability of the cuckoo search algorithm, the adaptive detection probability and amplification factor are designed. Finally, the control allocation method based on the proposed improved cuckoo search algorithm is applied to the tracking control problem of the innovative control effector aircraft. The comparative simulation results demonstrate the superiority and effectiveness of the proposed improved cuckoo search algorithm in control allocation of aircraft.

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.

Optimization of the reflux ratio of benzene-toluene stage distillation columns by the Cuckoo algorithm

In this study, an enthalpy-concentration method was applied in order to model a steady state continuous benzene-toluene mixture distillation column. For a distillation tower such as the benzene- toluene splitter, there are relatively few degrees of freedom that can be manipulated in order to minimize the total annualized cost. The reflux ratio can influence the steady-state operating point and therefore influence the total annualized cost. The trade-offs between reflux ratios and total annualized cost were discussed. The Cuckoo optimization algorithm was applied to obtain a correlation for the optimum value of the reflux ratio as a power function of the economic parameters of energy price and capital cost. The results show that, at low energy price or high capital cost, the optimum reflux factor is high.

A Novel Method for Identifying Recursive Systematic Convolutional Encoders Based on the Cuckoo Search Algorithm

The existing methods for identifying recursive systematic convolutional encoders with high robustness require to test all the candidate generator matrixes in the search space exhaustively.With the increase of the codeword length and constraint length,the search space expands exponentially,and thus it limits the application of these methods in practice.To overcome the limitation,a novel identification method,which gets rid of exhaustive test,is proposed based on the cuckoo search algorithm by using soft-decision data.Firstly,by using soft-decision data,the probability that a parity check equation holds is derived.Thus,solving the parity check equations is converted to maximize the joint probability that parity check equations hold.Secondly,based on the standard cuckoo search algorithm,the established cost function is optimized.According to the final solution of the optimization problem,the generator matrix of recursive systematic convolutional code is estimated.Compared with the existing methods,our proposed method does not need to search for the generator matrix exhaustively and has high robustness.Additionally,it does not require the prior knowledge of the constraint length and is applicable in any modulation type.

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.

Enhanced Energy Efficient Multipath Routing Protocol for Wireless Sensor Communication Networks Using Cuckoo Search Algorithm

Energy efficient routing is one of the major thrust areas in Wireless Sensor Communication Networks (WSCNs) and it attracts most of the researchers by its valuable applications and various challenges. Wireless sensor networks contain several nodes in its terrain region. Reducing the energy consumption over the WSCN has its significance since the nodes are battery powered. Various research methodologies were proposed by researchers in this area. One of the bio-inspired computing paradigms named Cuckoo search algorithm is used in this research work for finding the energy efficient path and routing is performed. Several performance metrics are taken into account for determining the performance of the proposed routing protocol such as throughput, packet delivery ratio, energy consumption and delay. Simulation is performed using NS2 and the results shows that the proposed routing protocol is better in terms of average throughput, and average energy consumption.

A Method for Crude Oil Selection and Blending Optimization Based on Improved Cuckoo Search Algorithm

Refineries often need to find similar crude oil to replace the scarce crude oil for stabilizing the feedstock property. We introduced the method for calculation of crude blended properties firstly, and then created a crude oil selection and blending optimization model based on the data of crude oil property. The model is a mixed-integer nonlinear programming(MINLP) with constraints, and the target is to maximize the similarity between the blended crude oil and the objective crude oil. Furthermore, the model takes into account the selection of crude oils and their blending ratios simultaneously, and transforms the problem of looking for similar crude oil into the crude oil selection and blending optimization problem. We applied the Improved Cuckoo Search(ICS) algorithm to solving the model. Through the simulations, ICS was compared with the genetic algorithm, the particle swarm optimization algorithm and the CPLEX solver. The results show that ICS has very good optimization efficiency. The blending solution can provide a reference for refineries to find the similar crude oil. And the method proposed can also give some references to selection and blending optimization of other materials.

基于NACS-PSO算法的光伏系统MPPT控制研究

对于局部遮阴下的光伏阵列,传统的最大功率点跟踪算法收敛速度慢、精度差、功率波动大且容易陷入局部最优。为此,提出一种基于新型自适应布谷鸟算法与粒子群算法相结合的复合算法。该方法在布谷鸟算法中引入自适应发现概率和自适应莱维飞行步长控制因子,同时加入对立种群策略,以提高算法收敛速度和全局寻优能力。在算法前期,用粒子群算法全局搜索快速找到全局最大功率点附近,后期用新型自适应布谷鸟算法在局部范围内精准寻优,以快速、准确和稳定地跟踪到全局最大功率点。仿真结果表明,本文提出的算法在四种光照模式下的收敛时间和跟踪误差分别为0.106s和0.012%、0.108s和0.034%、0.110s和0.059%、0.106s和0.031%,均优于其他算法,验证了本文算法在六种对比算法中,收敛速度最快、跟踪精度最高、功率波动最小、陷入局部最优的可能性最小。

基于CS-DBN的锂电池剩余寿命预测

为了更准确地对锂电池剩余使用寿命进行预测,提出一种基于布谷鸟算法(CS)和深度信念网络(DBN)的预测模型。首先,引进16个影响锂电池RUL的健康因子(HI),通过随机森林(RF)选择出对于剩余寿命预测较为重要的9个HI。随后用CS去寻优深度信念网络模型中隐藏层的参数,通过寻优,建立最优的深度信念网络预测模型。最后,使用马里兰大学所收集的电池数据(CALCE)进行实验,结果表明:所提出的CS-DBN模型的拟合优度高达98%,且与其他模型的预测结果进行对比,具有更小的误差,验证了所提方法的有效性。