Modified Elite Opposition-Based Artificial Hummingbird Algorithm for Designing FOPID Controlled Cruise Control System

Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter tuning.Our approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision.Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and reliability.We highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous vehicles.This study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe operation.Our research provides a promising avenue for further research and development in this area.

Hummingbird算法在射频识别标签中的应用

Hummingbird加密算法因实现所需面积较小、功耗较低而适用于低成本射频识别标签的安全加密。为此采用SMIC 0.13μm工艺对Hummingbird算法进行硬件实现,通过降低时钟翻转频率和数据翻转频率减少动态功耗。仿真实验结果证明,Hummingbird算法可以满足电子标签对硬件开销、功耗及响应时间的实际应用要求。

Hummingbird加密算法的硬件架构设计

Hummingbird加密算法是针对RFID标签等硬件受限系统的轻型加密算法。其已在不同平台上得到了验证。文中提出了一种针对Hummingbird算法的硬件架构,与目前其他方法相比,在响应时间基本相同的情况下,该硬件架构所需的硬件资源更少。其采用Xilinx的低端Spartan-3系列FPGA芯片作为验证平台。实验结果表明,该硬件架构可较好地嵌入到硬件受限系统中,尤其是嵌入式系统。

Numerical analysis of the three-dimensional aerodynamics of a hovering rufous hummingbird(Selasphorus rufus)

Hummingbirds have a unique way of hover- ing. However, only a few published papers have gone into details of the corresponding three-dimensional vortex struc- tures and transient aerodynamic forces. In order to deepen the understanding in these two realms, this article presents an integrated computational fluid dynamics study on the hovering aerodynamics of a rufous hummingbird. The original morphological and kinematic data came from a former researcher＇s experiments. We found that conical and sta- ble leading-edge vortices （LEVs） with spanwise flow inside their cores existed on the hovering hummingbird＇s wing surfaces. When the LEVs and other near-field vortices were all shed into the wake after stroke reversals, periodically shed bilateral vortex rings were formed. In addition, a strong downwash was present throughout the flapping cycle. Time histories of lift and drag were also obtained. Combining the three-dimensional flow field and time history of lift, we believe that high lift mechanisms （i.e., rotational circulation and wake capture） which take place at stroke reversals in insect flight was not evident here. For mean lift throughout a whole cycle, it is calculated to be 3.60 g （104.0 % of the weight support）. The downstroke and upstroke provide 64.2 % and 35.8 % of the weight support, respectively.

An optimization method: hummingbirds optimization algorithm

This paper introduces an optimization algorithm, the hummingbirds optimization algorithm(HOA), which is inspired by the foraging process of hummingbirds. The proposed algorithm includes two phases: a self-searching phase and a guide-searching phase. With these two phases, the exploration and exploitation abilities of the algorithm can be balanced. Both the constrained and unconstrained benchmark functions are employed to test the performance of HOA. Ten classic benchmark functions are considered as unconstrained benchmark functions. Meanwhile, two engineering design optimization problems are employed as constrained benchmark functions. The results of these experiments demonstrate HOA is efficient and capable of global optimization.

Binary Hybrid Artificial Hummingbird with Flower Pollination Algorithm for Feature Selection in Parkinson’s Disease Diagnosis

Parkinson’s disease is a neurodegenerative disorder that inflicts irreversible damage on humans.Some experimental data regarding Parkinson’s patients are redundant and irrelevant,posing significant challenges for disease detection.Therefore,there is a need to devise an effective method for the selective extraction of disease-specific information,ensuring both accuracy and the utilization of fewer features.In this paper,a Binary Hybrid Artificial Hummingbird and Flower Pollination Algorithm(FPA),called BFAHA,is proposed to solve the problem of Parkinson’s disease diagnosis based on speech signals.First,combining FPA with Artificial Hummingbird Algorithm(AHA)can take advantage of the strong global exploration ability possessed by FPA to improve the disadvantages of AHA,such as premature convergence and easy falling into local optimum.Second,the Hemming distance is used to determine the difference between the other individuals in the population and the optimal individual after each iteration,if the difference is too significant,the cross-mutation strategy in the genetic algorithm(GA)is used to induce the population individuals to keep approaching the optimal individual in the random search process to speed up finding the optimal solution.Finally,an S-shaped function converts the improved algorithm into a binary version to suit the characteristics of the feature selection(FS)tasks.In this paper,10 high-dimensional datasets from UCI and the ASU are used to test the performance of BFAHA and apply it to Parkinson’s disease diagnosis.Compared with other state-of-the-art algorithms,BFAHA shows excellent competitiveness in both the test datasets and the classification problem,indicating that the algorithm proposed in this study has apparent advantages in the field of feature selection.

Enhanced asphalt dynamic modulus prediction: A detailed analysis of artificial hummingbird algorithm-optimised boosted trees

This study introduces and evaluates a novel artificial hummingbird algorithm-optimised boosted tree(AHAboosted)model for predicting the dynamic modulus(E*)of hot mix asphalt concrete.Using a substantial dataset from NCHRP Report-547,the model was trained and rigorously tested.Performance metrics,specifically RMSE,MAE,and R2,were employed to assess the model's predictive accuracy,robustness,and generalisability.When benchmarked against well-established models like support vector machines(SVM)and gaussian process regression(GPR),the AHA-boosted model demonstrated enhanced performance.It achieved R2 values of 0.997 in training and 0.974 in testing,using the traditional Witczak NCHRP 1-40D model inputs.Incorporating features such as test temperature,frequency,and asphalt content led to a 1.23%increase in the test R2,signifying an improvement in the model's accuracy.The study also explored feature importance and sensitivity through SHAP and permutation importance plots,highlighting binder complex modulus|G*|as a key predictor.Although the AHA-boosted model shows promise,a slight decrease in R2 from training to testing indicates a need for further validation.Overall,this study confirms the AHA-boosted model as a highly accurate and robust tool for predicting the dynamic modulus of hot mix asphalt concrete,making it a valuable asset for pavement engineering.

The Application of Hybrid Krill Herd Artificial Hummingbird Algorithm for Scientific Workflow Scheduling in Fog Computing

Fog Computing(FC)provides processing and storage resources at the edge of the Internet of Things(IoT).By doing so,FC can help reduce latency and improve reliability of IoT networks.The energy consumption of servers and computing resources is one of the factors that directly affect conservation costs in fog environments.Energy consumption can be reduced by efficacious scheduling methods so that tasks are offloaded on the best possible resources.To deal with this problem,a binary model based on the combination of the Krill Herd Algorithm(KHA)and the Artificial Hummingbird Algorithm(AHA)is introduced as Binary KHA-AHA(BAHA-KHA).KHA is used to improve AHA.Also,the BAHA-KHA local optimal problem for task scheduling in FC environments is solved using the dynamic voltage and frequency scaling(DVFS)method.The Heterogeneous Earliest Finish Time(HEFT)method is used to discover the order of task flow execution.The goal of the BAHA-KHA model is to minimize the number of resources,the communication between dependent tasks,and reduce energy consumption.In this paper,the FC environment is considered to address the workflow scheduling issue to reduce energy consumption and minimize makespan on fog resources.The results were tested on five different workflows(Montage,CyberShake,LIGO,SIPHT,and Epigenomics).The evaluations show that the BAHA-KHA model has the best performance in comparison with the AHA,KHA,PSO and GA algorithms.The BAHA-KHA model has reduced the makespan rate by about 18%and the energy consumption by about 24%in comparison with GA.This is a preview of subscription content,log in via an institution to check access.

基于蜂鸟E203的多级动态分支预测器

近年来,以蜂鸟E203为代表的开源RISC-V微处理器由于功耗低、性能好等优势,受到了学术界和工业界的广泛关注和应用。为提高微处理器性能,降低分支指令造成的流水线停顿,指令分支预测技术成为现代微处理器中广泛应用的重要技术之一。然而,蜂鸟E203现采用的分支预测器是轻量级的静态分支预测器,面临分支预测准确率较低的挑战。由于使用预测准确率较高的动态分支预测器,可以进一步降低由于预测错误导致的重定向取指所产生的开销,因此,针对上述挑战,在原微架构的基础上探索了多种动态分支预测器的实现,提高了分支预测精度并且兼顾了资源开销。实验结果表明,多种动态分支预测器中获得最优结果的是使用静态分支预测结合基于分支历史寄存器BHR的自适应动态分支预测器,在Dhrystone基准测试程序上其分支预测精度可从原来的84.6%最高提升至94.8%,分数从原来的1.296463提高到1.314418,在Coremark基准测试程序上其分支预测精度可从原来的67%提升至78.7%,分数从原来的2.120000提升至2.138008。

基于孪生人工蜂鸟算法的多作业模式半导体封测环节调度

针对多作业模式的半导体封装测试环节调度问题,以最小化最大完工时间为目标,提出了孪生人工蜂鸟算法。设计了孪生种群机制,通过构建双解码、孪生种群生成与协作方法,扩大解的搜索空间,提高初始解的质量,增加优化过程中解的多样性,进而提高求解精度。通过双向引导觅食策略,平衡算法多样性与收敛性,增强算法稳定性。通过构建四邻域搜索策略,增强算法局部优化能力。实验结果表明,该方法能有效缩短半导体封测环节的最大完工时间。

基于多策略人工蜂鸟优化PF的SLAM研究

针对粒子滤波算法(PF)重采样导致粒子贫乏及需增加粒子数以提高估计精度的问题,提出一种基于多策略人工蜂鸟算法优化的粒子重组粒子滤波算法。首先,引入中垂线算法提高人工蜂鸟算法收敛速度,通过其智能觅食机制,使得最优粒子引导粒子集向高似然区域移动,以此提高估计精度;其次,实时计算最优粒子附近的粒子密度,当密度大于设置的区域搜索阈值时引入Levy飞行策略以扩大搜索空间,当其大于最大密度值时,自适应调整迭代次数;最后,重采样阶段将筛选后保留的粒子与剩余粒子重新组合成新的粒子,以此增加粒子多样性。通过仿真实验检验改进算法在SLAM中的性能,结果表明该算法较其他3种算法相比,其位姿与路标估计精度更高且鲁棒性更佳。

变权-混合决策评估的复合功能并网逆变器多目标协同优化控制方法

复合功能并网逆变器(multi-functional grid-connected inverter,MFGCI)在完成功率输出的同时,具备解决配电网中多种电能质量问题的能力,但该能力往往受其可用于电能质量治理的补偿容量的限制。以MFGCI的控制结构为基础,给出了无锁相环补偿指令电流及并网跟踪电流指令,提出了基于变权-混合决策评估的多目标协同优化方法,以更好适用于因新能源不确定性及非线性负荷接入导致的电能质量指标波动问题。构建了以电能质量补偿效果最佳和所需补偿容量最小的多目标函数,采用基于多目标人工蜂鸟算法(multi-objective artificial hummingbird algorithm,MOAHA)更新机制的优化算法,求解补偿各电能质量问题的最优容量分配系数,并通过多种场景下仿真,验证了所提方法的正确性和有效性。

Hummingbird XRP无人机发展历程及技术特点

随着紧凑型油电混合动力技术及能力的不断发展,垂直起降类无人机的创新探索备受瞩目。作为新型多旋翼布局无人机的典型代表,介绍了美国Reference Technology公司垂直起降无人机从基本型Hummingbird到改进型Hummingbird XRP的背景需求及发展历程;分析了HummingbirdXRP无人机总体布局、动力系统、推进系统和控制系统的技术特点;在此基础上,针对HummingbirdXRP无人机典型特点,从悬停构型形式、涵道风扇推进、油电混合动力和需求定位四个方面,探讨了垂直起降类无人机发展的相关启示与建议。

基于多策略改进的人工蜂鸟算法

针对人工蜂鸟算法(AHA)在迭代过程中出现全局勘探能力不足和收敛速度较慢的问题,提出了一种多策略改进的人工蜂鸟算法(IAHA)。首先,采用融合Tent混沌映射与反向学习的策略对种群进行初始化,生成高质量的初始种群,为算法全局寻优奠定基础;然后,在引导觅食阶段引入莱维飞行策略以提高全局搜索能力,使算法能够快速跳出局部最优,加快收敛速度;最后,将单纯形法引入算法中,在每一次迭代结束前对质量较差的种群进行处理,提高算法的局部寻优能力。将IAHA分别与4种基本算法、3种单改进阶段的人工蜂鸟算法、2种现有的改进人工蜂鸟算法进行对比,对8个基准测试函数进行仿真实验以及Wilcoxon秩和检验,对IAHA性能进行评估,并分析其时间复杂度。实验结果表明,与上述所提的算法相比,IAHA的收敛速度更快,全局寻优能力更强,算法性能更佳。

Factors affecting the dominance hierarchy dynamics in a hummingbird assemblage

Intra and in terspecific competiti on for n ectar play an imports nt role in hummingbird communities. Larger sized species usually exclude smaller species from the rich floral resources. However, it has been recently postulated that the competitive advantages of a large body size decline as the evolutionary distance between the contending species in creases. In this study, we analyzed dominance hierarchy dynamics in a hummingbird assemblage in central Mexico. By monitoring hummingbird territories established in three plant species through 1 year, we assessed the effects of energy within territories and the territory owners identity in the frequency of inter and intraspecific encounters. We also evaluated if these factors affect the dominance of larger species when they compete against smaller distantly related contenders. Our results show that their frequency of intraspecific encounters was related with the identity of the territory's owner. On the contrary, the frequency of interspecific encounters was related with both the territory and the identity of the territory's owner. We did not find a significant difference between the number of encounters dominated by larger and smaller species and their conte nders. However, the in crease in genetic dista nee between contenders was positively associated with a higher frequency of encounters dominated by small hummingbirds.Our results showed that the ecological factors and evolutionary relationships among contenders play important roles in the dominance hierarchy dynamics.

From a sequential pattern,temporal adjustments emerge in hummingbird traplining

Animals that feed from resources that are constant in space and that refill may benefit from repeating the order in which they visit locations.This is a behavior known as traplining,a spatial phenomenon.Hummingbirds,like other central-place foragers,use short traplines when moving between several rewarding sites.Here we investi­gated whether traplining hummingbirds also use relevant temporal information when choosing which flowers to visit.Wild rufous hummingbirds that were allowed to visit 3 artificial flower patches in which flowers were re­filled 20 min after they had been depleted repeated the order in which they visited the 3 patches.Although they tended to visit the first 2 patches sooner than 20 min,they visited the third patch at approximately 20-min inter­vals.The time between visits to the patches increased across the experiment,suggesting that the birds learned to wait longer before visiting a patch.The birds appeared to couple the sequential pattern of a trapline with tem­poral regularity,to some degree.This suggests that there is a temporal component to the repeated spatial move­ments flown by foraging wild hummingbirds.

改进人工蜂鸟算法的无线传感器网络部署优化

人工蜂鸟算法在求解高维度复杂优化问题时,易陷入局部极小值,导致算法收敛停滞。提出一种改进的人工蜂鸟算法(improved artificial hummingbird algorithm,IAHA),并用其优化无线传感器网络部署。首先对蜂鸟个体和最优蜂鸟之间距离进行正切函数变换,以最优蜂鸟位置为基准,以变换的距离为飞行尺度,提出一种新的觅食策略。其次,在迁徙觅食阶段,使用柯西分布对最优蜂鸟信息进行扰动,将扰动结果赋予最差蜂鸟,取代基本人工蜂鸟算法的随机赋值方法。在12个基准函数上的数值实验表明,IAHA的寻优性能优于6种对比算法。在4种监测区域上进行了无线传感器网络部署优化仿真实验,结果表明,IAHA获得的平均覆盖率高于对比算法,且传感器分布均匀,适合求解无线传感器网络部署优化问题。