BAS-ADAM:An ADAM Based Approach to Improve the Performance of Beetle Antennae Search Optimizer

In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation(ADAM)update rule.The proposed algorithm also increases the convergence rate in a narrow valley.A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size.Since ADAM is traditionally used with gradient-based optimization algorithms,therefore we first propose a gradient estimation model without the need to differentiate the objective function.Resultantly,it demonstrates excellent performance and fast convergence rate in searching for the optimum of noin-convex functions.The efficiency of the proposed algorithm was tested on three different benchmark problems,including the training of a high-dimensional neural network.The performance is compared with particle swarm optimizer(PSO)and the original BAS algorithm.

Adaptive Fractional-Order PID Control for VSC-HVDC Systems via Cooperative Beetle Antennae Search with Offshore Wind Integration

Since the voltage source converter based high voltage direct current(VSC-HVDC)systems owns the features of nonlinearity,strong coupling and multivariable,the classical proportional integral(PI)control is hard to obtain content control effect.Hence,a new perturbation observer based fractional-order PID(PoFoPID)control strategy is designed in this paper for(VSC-HVDC)systems with offshore wind integration,which can efficiently boost the robustness and control performance of entire system.Particularly,it employs a fractional-order PID(FoPID)fra-mework for the sake of compensating the perturbation estimate,which dramatically boost the dynamical responds of the closed-loop system,and the cooperative beetle antennae search(CBAS)algorithm is adopted to quickly and effi-ciently search its best control parameters.Besides,CBAS algorithm is able to efficiently escape a local optimum because of a suitable trade-off between global exploration and local exploitation can be realized.At last,comprehensive case studies are carried out,namely,active and reactive power tracking,5-cycle line-line-line-ground(LLLG)fault,and offshore wind farm integration.Simulation results validate superiorities and effectiveness of PoFoPID control in com-parison of that of PID control and feedback linearization sliding-mode control(FLSMC),respectively.

Optimised trajectory tracking control for quadrotors based on an improved beetle antennae search algorithm

This paper focuses on the trajectory tracking of quadrotors under bounded external disturbances.An optimised robust controller is proposed to drive the position and attitude ofa quadrotor converge to their references quickly. At first, nonsingular fast terminal slidingmode control is developed, which can guarantee not only the stability but also finite-timeconvergence of the closed-loop system. As the parameters of the designed controllers playa vital role for control performance, an improved beetle antennae search algorithm is proposedto optimise them. By employing the historical information of the beetle’s antennaeand dynamically updating the step size as well as the range of its searching, the optimisingis accelerated considerably to ensure the efficiency of the quadrotor control. The superiorityof the proposed control scheme is demonstrated by simulation experiments, from whichone can see that both the error and the overshooting of the trajectory tracking are reducedeffectively.

Feature Selection with Deep Reinforcement Learning for Intrusion Detection System

An intrusion detection system(IDS)becomes an important tool for ensuring security in the network.In recent times,machine learning(ML)and deep learning(DL)models can be applied for the identification of intrusions over the network effectively.To resolve the security issues,this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique,called BBOFS-DRL for intrusion detection.The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network.To attain this,the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm(BOA)to elect feature subsets.Besides,DRL model is employed for the proper identification and classification of intrusions that exist in the network.Furthermore,beetle antenna search(BAS)technique is applied to tune the DRL parameters for enhanced intrusion detection efficiency.For ensuring the superior intrusion detection outcomes of the BBOFS-DRL model,a wide-ranging experimental analysis is performed against benchmark dataset.The simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches.

An Efficient Video Inpainting Approach Using Deep Belief Network

The video inpainting process helps in several video editing and restoration processes like unwanted object removal,scratch or damage rebuilding,and retargeting.It intends to fill spatio-temporal holes with reasonable content in the video.Inspite of the recent advancements of deep learning for image inpainting,it is challenging to outspread the techniques into the videos owing to the extra time dimensions.In this view,this paper presents an efficient video inpainting approach using beetle antenna search with deep belief network(VIA-BASDBN).The proposed VIA-BASDBN technique initially converts the videos into a set of frames and they are again split into a region of 5*5 blocks.In addition,the VIABASDBN technique involves the design of optimal DBN model,which receives input features from Local Binary Patterns(LBP)to categorize the blocks into smooth or structured regions.Furthermore,the weight vectors of the DBN model are optimally chosen by the use of BAS technique.Finally,the inpainting of the smooth and structured regions takes place using the mean and patch matching approaches respectively.The patch matching process depends upon the minimal Euclidean distance among the extracted SIFT features of the actual and references patches.In order to examine the effective outcome of the VIA-BASDBN technique,a series of simulations take place and the results denoted the promising performance.

UAV safe route planning based on PSO-BAS algorithm

In order to solve the current situation that unmanned aerial vehicles(UAVs)ignore safety indicators and cannot guarantee safe operation when operating in low-altitude airspace,a UAV route planning method that considers regional risk assessment is proposed.Firstly,the low-altitude airspace is discretized based on rasterization,and then the UAV operating characteristics and environmental characteristics are combined to quantify the risk value in the low-altitude airspace to obtain a 3D risk map.The path risk value is taken as the cost,the particle swarm optimization-beetle antennae search(PSO-BAS)algorithm is used to plan the spatial 3D route,and it effectively reduces the generated path redundancy.Finally,cubic B-spline curve is used to smooth the planned discrete path.A flyable path with continuous curvature and pitch angle is generated.The simulation results show that the generated path can exchange for a path with a lower risk value at a lower path cost.At the same time,the path redundancy is low,and the curvature and pitch angle continuously change.It is a flyable path that meets the UAV performance constraints.

改进的神经网络PID在空调温度控制中的应用

为提高候机楼中央空调温度控制水平,针对候机楼中央空调系统具有时滞性、扰动因素较多等特点,提出了一种基于改进天牛须搜索(IBAS,improved beetle antennae search)算法的模糊径向基函数(RBF,radial basis function)神经网络(PID,proportion integration differentiation)控制方法,建立了空调区域温度控制模型,通过模糊RBF神经网络实现PID参数在线整定,解决系统非线性、时变的问题。同时由于神经网络参数存在难以选取问题,提出利用天牛须搜索(BAS,beetle antennae search)算法优化模糊RBF神经网络参数的方法,并引入莱维飞行机制和变步长策略对BAS算法进行改进,提高其跳出局部最优的能力和稳定性。仿真结果表明,采用IBAS算法优化的模糊RBF神经网络PID控制方法有效提高了系统的鲁棒性和自适应能力,对候机楼中央空调系统具有良好的控制效果。