基于COOT优化算法和改进型重复PI控制的三相LCL型光伏并网系统

为提高光伏并网发电的发电效率,提出了基于COOT优化算法和改进型重复PI控制的三相LCL型光伏并网系统。COOT优化算法用于前级MPPT算法,可以准确快速追踪到光伏发电系统全局最大输出功率;改进型重复PI控制用于后级逆变,通过阻尼因子设计LCL有源阻尼控制,在数学建模过程中把采样计算延时以及PI控制考虑进去,最后加入重复控制;为了减少奇次谐波,采用级联式重复PI控制结构;同时针对电网频率波动时重复控制策略控制效果降低的问题,采用一种基于拉格朗日插值原理的频率自适应级联式重复PI控制。基于所提出的控制策略,利用Simulink仿真平台对系统进行了建模仿真,仿真结果表明了所提控制策略的正确性和可行性。

基于COOT算法的城轨车辆车体称重调簧算法优化

现有城轨车辆车体称重调簧模型忽略了车体的非均匀弹性变形、弹簧刚度的非线性等问题。针对该问题,文章首先建立了基于最小二乘法拟合二系支撑位刚度的改进称重调簧模型,该模型相较于传统称重调簧模型更贴合车体的实际负载情况;利用改进白骨顶鸡(COOT)算法对称重调簧算法进行多目标改造和搜索机制优化,相较于模拟退火(SA)算法、粒子群优化(PSO)算法、遗传算法(GA),改进COOT算法使稳定收敛载荷偏差分别降低了3.2 N、2.1 N和7.4 N,收敛速度分别提高了3.5%、6.8%和18.0%,具有更好的精度、效率和鲁棒性;基于C#和MATLAB搭建了整套称重调簧试验系统,并进行模拟试验及实车验证,证明了改进COOT算法及称重调簧系统的准确性和稳定性。

基于SS-COOT和路径空间缩减的不规则危险区路径规划

城市作战的重要性日益凸显,城市作战路径规划也受到了更多的关注。如何在城市复杂的环境和众多危险区中寻找安全迅速的路径是非常重要的。为保障作战安全,提出了一种基于选拔科特鸟和路径缩减的不规则危险区路径规划算法。首先,结合城市危险区特征和受限情况以构建更符合真实战场的不规则危险区数学模型。其次,建立路径空间缩减模型对路径威胁度进行评估和量化,以剔除掉高威胁路径来降低作战风险。最后,基于选拔策略的科特鸟优化算法(COOT Bird Optimization Algorithm based on Selection Strategy,SS-COOT)结合优质个体以提高算法的寻优效率。经实验验证,该算法在结合不规则危险区的城市路径规划问题上具有搜索速度快、寻优效果好的特点。

基于COOT算法的局部阴影下光伏阵列MPPT控制研究

针对光伏阵列在局部阴影下会产生多个功率峰值,可能出现跟踪到错误的功率峰值的情况,将白冠鸡优化算法(COOT)应用于局部阴影MPPT控制,通过动态调整太阳能电池板输出电压和电流,使得太阳能电池板的输出功率最大,从而提高太阳能电池板的转换效率和输出功率。结果表明,不论是在静态还是动态阴影条件下,采用白冠鸡优化算法都能准确定位到最大功率峰值点。

Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization for Solving Continuous Numerical Optimization Problems

Harris Hawks Optimization(HHO)is a novel meta-heuristic algorithm that imitates the predation characteristics of Harris Hawk and combines Lévy flight to solve complex multidimensional problems.Nevertheless,the basic HHO algorithm still has certain limitations,including the tendency to fall into the local optima and poor convergence accuracy.Coot Bird Optimization(CBO)is another new swarm-based optimization algorithm.CBO originates from the regular and irregular motion of a bird called Coot on the water’s surface.Although the framework of CBO is slightly complicated,it has outstanding exploration potential and excellent capability to avoid falling into local optimal solutions.This paper proposes a novel enhanced hybrid algorithm based on the basic HHO and CBO named Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization(EHHOCBO).EHHOCBO can provide higher-quality solutions for numerical optimization problems.It first embeds the leadership mechanism of CBO into the population initialization process of HHO.This way can take full advantage of the valuable solution information to provide a good foundation for the global search of the hybrid algorithm.Secondly,the Ensemble Mutation Strategy(EMS)is introduced to generate the mutant candidate positions for consideration,further improving the hybrid algorithm’s exploration trend and population diversity.To further reduce the likelihood of falling into the local optima and speed up the convergence,Refracted Opposition-Based Learning(ROBL)is adopted to update the current optimal solution in the swarm.Using 23 classical benchmark functions and the IEEE CEC2017 test suite,the performance of the proposed EHHOCBO is comprehensively evaluated and compared with eight other basic meta-heuristic algorithms and six improved variants.Experimental results show that EHHOCBO can achieve better solution accuracy,faster convergence speed,and a more robust ability to jump out of local optima than other advanced optimizers in most test cases.Finally,EHHOCBOis applied to address four engineering design problems.Our findings indicate that the proposed method also provides satisfactory performance regarding the convergence accuracy of the optimal global solution.

Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers.Cyber-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)attacks.In this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their damage.The recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying cyber-attacks.The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT environment.The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment.To accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square test.To detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this study.Finally,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency.The proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct aspects.The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

基于COOT-SVM的短期光伏发电功率预测

为了提高短期光伏发电功率预测的精度,提出了一种基于白冠鸡优化算法(COOT)优化支持向量机(SVM)的短期光伏发电功率预测模型。首先,分别选取某光伏电站在2017年4月和7月的前21天数据进行仿真分析,计算光伏输出功率和每一个气象因素之间的皮尔逊相关系数;然后,依据皮尔逊相关系数选择太阳总辐射强度、太阳散射辐射强度、太阳直射辐射强度、组件温度和环境温度5个气象因素作为预测模型的输入数据,光伏电站的发电功率作为输出数据。通过与BP和SVM预测模型进行仿真对比可知,对于4月和7月的数据来说,COOT-SVM预测模型的均方根误差、均方误差和平均绝对误差均比BP和SVM预测模型小。因此,所提COOT-SVM预测模型可有效提高短期光伏发电功率的预测精度,具有较高的工程应用价值。

Territory and territorial behavior of migrating Common Coot (Fulica atra)

Territory and territorial behavior of the Common Coot（Fulica atra） were studied in two breeding sites,Anbanghe Nature Reserve and Daqing Longfeng wetland,in Heilongjiang Province,China from April to October in 2008 and 2009.In the breeding season,the breeding pairs occupied an area and protected it throughout the reproduction,and both interspecific and intraspecific conflicts were observed.Territory activities became severe since early May,the peak of territory behaviors appeared at late May,and then declined gradually.The territorial activities level was higher than that in the nest building period than in the laying and incubation periods.The most adopted behavioral model was expelling,which was the least energy cost.The degree of territorial behavior tended to be descended since the development of breeding phase.The territory size differed from 1 333 m2 to above 5 000 m2.Wintering population was observed in Poyang Lake of Jiangxi Province.The coots gathered in the open water;however,there was no territory behavior both in the interspecies and intraspecies in wintering sites.The hypotheses why there was territory behaviors for coots both in the interspecies and intraspecies were also discussed.

Effective participation of wind turbines in frequency control of a two-area power system using coot optimization

In this paper,load frequency control is performed for a two-area power system incorporating a high penetration of renewable energy sources.A droop controller for a type 3 wind turbine is used to extract the stored kinetic energy from the rotating masses during sudden load disturbances.An auxiliary storage controller is applied to achieve effec-tive frequency response.The coot optimization algorithm(COA)is applied to allocate the optimum parameters of the fractional-order proportional integral derivative(FOPID),droop and auxiliary storage controllers.The fitness function is represented by the summation of integral square deviations in tie line power,and Areas 1 and 2 frequency errors.The robustness of the COA is proven by comparing the results with benchmarked optimizers including:atomic orbital search,honey badger algorithm,water cycle algorithm and particle swarm optimization.Performance assessment is confirmed in the following four scenarios:(i)optimization while including PID controllers;(ii)optimization while including FOPID controllers;(iii)validation of COA results under various load disturbances;and(iv)validation of the proposed controllers under varying weather conditions.

Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

基于拉丁超立方体的改进白骨顶鸡算法

针对白骨顶鸡算法求解工程问题时收敛速度慢,易陷入局部最优等不足,提出一种基于拉丁超立方体的改进白骨顶鸡算法。使用拉丁超立方体抽样增强初始种群的均匀性和多样性;引入非线性决策因子和自适应动态边界机制,提高算法全局搜索和局部开发能力;利用柯西变异对最优解进行扰动,帮助算法跳出局部最优。在16个基准函数、高维函数和工程问题进行仿真,其结果验证,该算法收敛速度和寻优精度良好,在工程问题上具有可行性和有效性。

白骨顶的种内巢寄生行为及对不同类型寄生卵的反应

种内巢寄生是鸟类的条件性繁殖策略,具有特定的进化历史和地域特征。白骨顶(Fulica atra)作为古北界广泛分布的湿地水禽,其种内巢寄生和反寄生行为策略对于巢寄生理论假说的验证具有重要意义。通过模型卵和真卵放置试验,探究白骨顶对不同类型寄生卵的识别能力,并分析其繁殖期不同阶段的寄生防御策略。结果表明:白骨顶具有较高的种内巢寄生频率,拒卵行为包括埋卵、将寄生卵排出巢外等,对模型卵有较强的识别能力,尤其是深色模型卵未出现错误拒卵,而对于真卵的识别能力相对较弱,模型卵和真卵的拒卵成功率差异显著(χ^(2)=4.304,p=0.038)。不同繁殖阶段白骨顶的拒卵反应差别很大,在产卵期拒卵强度高于孵卵后,两个阶段的拒卵成功率差异极显著(χ^(2)=9.580,p=0.002)。白骨顶通过寄生卵识别和拒卵行为,减少种内巢寄生的影响,同时又权衡拒卵防御成本,在不同繁殖阶段采取有利于自身适合度的行为适应策略。

融合翻筋斗觅食和正余弦策略的白骨顶鸡优化算法

针对白骨顶鸡优化算法(COOT)全局搜索能力差、优化精度低和易陷入局部最优等缺陷,提出了一种融合翻筋斗觅食和正余弦策略的白骨顶鸡优化算法,引入一种翻筋斗觅食策略,增加了白骨顶鸡个体的多样性。此外,在领导者位置更新过程中采用正余弦策略和随机因子,以提高算法的搜索能力。通过10个标准检验函数,以及Wilcoxon秩和检验,对改进的COOT性能进行了全面评估,并将其与其他几种算法进行了比较。仿真结果表明,改进的COOT在迭代速度和收敛精度方面取得了显著的提升,并展现了出色的鲁棒性。

增强型白骨顶鸡优化算法及其应用

针对白骨顶鸡优化算法全局搜索能力弱、收敛速度慢、易陷于局部最优的不足,本文提出一种增强型白骨顶鸡优化算法.首先,利用拉丁超立方抽样方法均匀初始化种群,从而改善种群的多样性和算法的全局性能.其次,在跟随领导者的位置更新策略中加入掉队机制,增强算法跳出局部最优能力.最后,引入二次插值策略,提高算法收敛速度与寻优精度.实验使用CEC2017测试函数对算法进行测试比较,测试结果表明改进的算法在精度、收敛速度、稳定性方面优于原算法与六种流行的群智能算法.此外,通过3个实际工程优化问题验证了所提出的增强型白骨顶鸡优化算法的实用性.

基于改进白骨顶鸡算法的局部荫蔽光伏MPPT

针对光伏发电系统在局部荫蔽下传统最大功率点追踪方法极易陷入局部最优而导致功率震荡范围较大等问题,提出一种基于改进白骨顶鸡算法的光伏MPPT方法。该算法在传统白骨顶鸡算法的基础上,将Logistic-Sine-Cosine混沌映射因子引入种群跟随者的链式移动中,从而使链式移动变为混沌移动,让算法具备跳出局部最优解的能力;对每次寻优结束后的当前最优位置进行柯西变异,对比变异前后择优更新替代,增加算法的全局搜索能力。在四种光照模式下,将ICOOT与另外三种算法的MPPT进行仿真分析。结果显示,所提改进算法的追踪速度为0.14 s,1.13 s,0.13 s,1.07 s,系统稳定率为99.43%,99.34%,98.73%,98.80%。综合来看,ICOOT在用于光伏发电局部隐蔽MPPT时能有效解决传统算法易于陷入局部最大功率点而导致寻优速度慢、功率震荡大的问题。

基于窦性变异的改进人工蜂群白骨顶鸡算法及应用

针对白骨顶鸡算法(COOT)存在求解精度低、收敛速度较慢和易陷入局部最优的问题,该文提出一种基于窦性变异的改进人工蜂群白骨顶鸡算法(ICOOT)。首先,采用精英反向学习策略初始化个体位置,增加初始个体寻优多样性;其次,考虑到人工蜂群算法强大的搜索能力,提出一种以全局最优值引导的改进人工蜂群搜索策略,更新白骨顶鸡个体的位置,以提高COOT的搜索能力和收敛精度;最后,引入窦性变异策略对最优个体进行扰动,一方面使算法能够有效跳出局部最优,另一方面提高最优个体质量。利用12个基准测试函数对ICOOT进行寻优性能测试,将ICOOT应用于拉力/压力弹簧优化工程设计问题,并与其他元启发式算法进行了比较和分析,结果验证了改进的算法的可行性和优越性。

基于改进白骨顶鸡优化算法的ECT图像重建

电容层析成像(ECT)技术求解图像重建问题存在严重的不适定性。为提高图像重建精度,提出了一种改进白骨顶鸡优化算法的ECT图像重建算法。针对标准白骨顶鸡优化算法(COA)中初始种群随机生成而导致的算法稳定性差和容易陷入局部最优的问题,引入了佳点集和正余弦优化方法,并融合了ART重建算法,根据ECT成像特点改进了目标函数。最后,进行仿真和静态实验,并与线性反投影(LBP)算法、Landweber算法、ART算法进行对比。结果表明,该方法可有效提高图像重建精度。

基于柯西变异和差分进化的混沌白骨顶鸟算法

针对白骨顶鸟优化算法(COOT)寻优精度低、容易陷入局部最优、收敛速度慢等问题,提出了基于柯西变异和差分进化的混沌白骨顶鸟算法(Logistic Chaos Coot bird algorithm based on Cauchy mutation and Differential evolution,CDLCOOT)。首先,通过柯西变异使白骨顶鸟位置发生扰动,扩大搜索范围,提高算法的全局搜索能力;其次,对领导者白骨顶鸟采取差分进化策略,增加种群多样性,使适应度更好的领导者带领种群寻优,引导白骨顶鸟个体向最优解前进,帮助其更快地搜索;最后,在白骨顶鸟进行链式运动时加入logistic混沌因子,从而实现混沌的链式跟随运动,提高算法跳出局部最优的能力。在12个经典的测试函数和9个CEC2017测试函数上进行仿真实验,将CDLCOOT算法与正余弦算法(SCA)、灰狼优化算法(GWO)、蚁狮优化算法(ALO)、黑洞模拟算法(MVO)等其他先进算法及原始COOT算法、具有单一策略的原算法进行对比,验证改进算法的有效性。实验结果表明,CDLCOOT算法相比其他启发式算法和改进算法具有更好的全局寻优能力和更快的收敛速度。在经典测试函数中,对于4个单模态函数,CDLCOOT算法寻优平均值相比原始算法平均提高了76个数量级;在2个多模态函数上寻到理论最优值,在另外2个多模态函数上寻优平均值分别比原始算法提高了三四个数量级;在4个固定维度多模态函数上,算法都能寻到理论最优值,收敛速度更快。在CEC2017测试函数中,所提算法在单模态、多模态和混合模态上的收敛精度相比原算法都有所提升,且其收敛速度也比原算法和其他算法更快,算法稳定性更高。

WPD-COA-EL M模型在汛期月降水量时间序列预测中的应用研究

针对月降水量时间序列多尺度非平稳性特点,提出小波包分解(WPD)-白骨顶鸟优化算法(COA)-极限学习机(ELM)相融合的降水量预测模型。首先,利用WPD将非平稳月降水量时间序列分解为若干子序列分量;然后在不同维度条件下利用6个典型函数对COA进行仿真测试;利用COA优化ELM输入层权值和隐含层偏值,对每一个子序列分量分别建立COA-ELM模型进行预测,将预测结果叠加重构后即为最终预测结果;最后,以云南省龙潭站汛期和主汛期月降水量数据为例进行实验,并与WPD-COA-BP、WPD-ELM、WPD-BP预测模型进行比较。结果表明:COA在不同维度条件下均具有较好的寻优精度和全局搜索能力。WPD-COA-ELM模型对实例汛期、主汛期月降水量时间序列预测的平均绝对百分比误差分别为3.91%、3.59%,预测精度优于WPD-COA-BP模型,远优于WPD-ELM.WPD-BP模型。WPD能科学降低月降水时间序列数据的复杂性,提高预测效果;COA能有效优化ELM输入层权值和隐含层偏值,提高ELM网络性能。

基于扰动因子和贪心策略的白骨顶优化算法

针对白骨顶优化算法存在收敛精度低、速度慢且鲁棒性较差等问题,提出了基于扰动因子和贪心策略的白骨顶优化算法(PGCOOT)。首先,该算法在种群初始化阶段采用了精英反向学习策略,提高白骨顶种群多样性;其次通过贪心策略在白骨顶移动中选择最优移动方式,以降低搜索的盲目性和低效性;然后引入扰动因子平衡算法的局部和全局探索能力。最后,将改进算法与多个群智能算法在8个经典测试函数下进行仿真实验,其结果表明,优化后的白骨顶优化算法拥有更优的寻优精度、速度以及更好的稳定性,Wilcoxon秩和检验证明了PGCOOT算法与其他算法相较存在显著性优异。