Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

This paper focuses on wireless-powered communication systems,which are increasingly relevant in the Internet of Things(IoT)due to their ability to extend the operational lifetime of devices with limited energy.The main contribution of the paper is a novel approach to minimize the secrecy outage probability(SOP)in these systems.Minimizing SOP is crucial for maintaining the confidentiality and integrity of data,especially in situations where the transmission of sensitive data is critical.Our proposed method harnesses the power of an improved biogeography-based optimization(IBBO)to effectively train a recurrent neural network(RNN).The proposed IBBO introduces an innovative migration model.The core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and exploitation.This is accomplished by integrating tactics such as advancing towards a random habitat,adopting the crossover operator from genetic algorithms(GA),and utilizing the global best(Gbest)operator from particle swarm optimization(PSO)into the IBBO framework.The IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters,resulting in significant outage probability reduction.Through comprehensive simulations,we showcase the superiority of the IBBO-RNN over existing approaches,highlighting its capability to achieve remarkable gains in SOP minimization.This paper compares nine methods for predicting outage probability in wireless-powered communications.The IBBO-RNN achieved the highest accuracy rate of 98.92%,showing a significant performance improvement.In contrast,the standard RNN recorded lower accuracy rates of 91.27%.The IBBO-RNN maintains lower SOP values across the entire signal-to-noise ratio(SNR)spectrum tested,suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs.

Handling Multiple Objectives with Biogeography-based Optimization

Biogeography-based optimization （BBO） is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization （BBMO） is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability efficiently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II （NSGA-II） and archive-based micro genetic algorithm （AMGA） in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.

BEVGGC:Biogeography-Based Optimization Expert-VGG for Diagnosis COVID-19 via Chest X-ray Images

Purpose:As to January 11,2021,coronavirus disease(COVID-19)has caused more than 2 million deaths worldwide.Mainly diagnostic methods of COVID-19 are:(i)nucleic acid testing.This method requires high requirements on the sample testing environment.When collecting samples,staff are in a susceptible environment,which increases the risk of infection.(ii)chest computed tomography.The cost of it is high and some radiation in the scan process.(iii)chest X-ray images.It has the advantages of fast imaging,higher spatial recognition than chest computed tomography.Therefore,our team chose the chest X-ray images as the experimental dataset in this paper.Methods:We proposed a novel framework—BEVGG and three methods(BEVGGC-I,BEVGGC-II,and BEVGGC-III)to diagnose COVID-19 via chest X-ray images.Besides,we used biogeography-based optimization to optimize the values of hyperparameters of the convolutional neural network.Results:The experimental results show that the OA of our proposed three methods are 97.65%±0.65%,94.49%±0.22%and 94.81%±0.52%.BEVGGC-I has the best performance of all methods.Conclusions:The OA of BEVGGC-I is 9.59%±1.04%higher than that of state-of-the-art methods.

Parkinson’s Disease Detection Using Biogeography-Based Optimization

In recent years,Parkinson’s Disease(PD)as a progressive syndrome of the nervous system has become highly prevalent worldwide.In this study,a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network(MLP)with the Biogeography-based Optimization(BBO)to classify PD based on a series of biomedical voice measurements.BBO is employed to determine the optimal MLP parameters and boost prediction accuracy.The inputs comprised of 22 biomedical voice measurements.The proposed approach detects two PD statuses:0-disease status and 1-good control status.The performance of proposed methods compared with PSO,GA,ACO and ES method.The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection.The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms,and consequently,it served as a promising new robust tool with excellent PD diagnosis performance.

Novel constrained multi-objective biogeography-based optimization algorithm for robot path planning

A constrained multi-objective biogeography-based optimization algorithm （CMBOA） was proposed to solve robot path planning （RPP）. For RPP, the length and smoothness of path were taken as the optimization objectives, and the distance from the obstacles was constraint. In CMBOA, a new migration operator with disturbance factor was designed and applied to the feasible population to generate many more non-dominated feasible individuals; meanwhile, some infeasible individuals nearby feasible region were recombined with the nearest feasible ones to approach the feasibility. Compared with classical multi-objective evolutionary algorithms, the current study indicates that CM- BOA has better performance for RPP.

Solution-Distance-Based Migration Rate Calculating for Biogeography-Based Optimization

Biogeography-based optimization(BBO),a natureinspired optimization algorithm(NIOA),has exhibited a huge potential in optimization.In BBO,the good solutions have a large probability to share information with poor solutions,while poor solutions have a large probability to accept the information from others.In original BBO,calculating for migration rates is based on solutions' ranking.From the ranking,it can be known that which solution is better and which one is worse.Based on the ranking,the migration rates are calculated to help BBO select good features and poor features.The differences among results can not be reflected,which will result in an improper migration rate calculating.Two new ways are proposed to calculate migration rates,which is helpful for BBO to obtain a suitable assignment of migration rates and furthermore affect algorithms ' performance.The ranking of solutions is no longer integers,but decimals.By employing the strategies,the ranking can not only reflect the orders of solutions,but also can reflect more details about solutions' distances.A set of benchmarks,which include 14 functions,is employed to compare the proposed approaches with other algorithms.The results demonstrate that the proposed approaches are feasible and effective to enhance BBO's performance.

A Hybrid Approach for COVID-19 Detection Using Biogeography-Based Optimization and Deep Learning

The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services.An early diagnosis of COVID-19 may reduce the impact of the coronavirus.To achieve this objective,modern computation methods,such as deep learning,may be applied.In this study,a computational model involving deep learning and biogeography-based optimization(BBO)for early detection and management of COVID-19 is introduced.Specifically,BBO is used for the layer selection process in the proposed convolutional neural network(CNN).The computational model accepts images,such as CT scans,X-rays,positron emission tomography,lung ultrasound,and magnetic resonance imaging,as inputs.In the comparative analysis,the proposed deep learning model CNNis compared with other existingmodels,namely,VGG16,InceptionV3,ResNet50,and MobileNet.In the fitness function formation,classification accuracy is considered to enhance the prediction capability of the proposed model.Experimental results demonstrate that the proposed model outperforms InceptionV3 and ResNet50.

Hybrid Optimization Based PID Controller Design for Unstable System

PID controllers play an important function in determining tuning para-meters in any process sector to deliver optimal and resilient performance for non-linear,stable and unstable processes.The effectiveness of the presented hybrid metaheuristic algorithms for a class of time-delayed unstable systems is described in this study when applicable to the problems of PID controller and Smith PID controller.The Direct Multi Search(DMS)algorithm is utilised in this research to combine the local search ability of global heuristic algorithms to tune a PID controller for a time-delayed unstable process model.A Metaheuristics Algorithm such as,SA(Simulated Annealing),MBBO(Modified Biogeography Based Opti-mization),BBO(Biogeography Based Optimization),PBIL(Population Based Incremental Learning),ES(Evolution Strategy),StudGA(Stud Genetic Algo-rithms),PSO(Particle Swarm Optimization),StudGA(Stud Genetic Algorithms),ES(Evolution Strategy),PSO(Particle Swarm Optimization)and ACO(Ant Col-ony Optimization)are used to tune the PID controller and Smith predictor design.The effectiveness of the suggested algorithms DMS-SA,DMS-BBO,DMS-MBBO,DMS-PBIL,DMS-StudGA,DMS-ES,DMS-ACO,and DMS-PSO for a class of dead-time structures employing PID controller and Smith predictor design controllers is illustrated using unit step set point response.When compared to other optimizations,the suggested hybrid metaheuristics approach improves the time response analysis when extended to the problem of smith predictor and PID controller designed tuning.

Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.

Optimizing Feedforward Neural Networks Using Biogeography Based Optimization for E-Mail Spam Identification

Spam e-mail has a significant negative impact on individuals and organizations, and is considered as a serious waste of resources, time and efforts. Spam detection is a complex and challenging task to solve. In literature, researchers and practitioners proposed numerous approaches for automatic e-mail spam detection. Learning-based filtering is one of the important approaches used for spam detection where a filter needs to be trained to extract the knowledge that can be used to detect the spam. In this context, Artificial Neural Networks is a widely used machine learning based filter. In this paper, we propose the use of a common type of Feedforward Neural Network called Multi-Layer Perceptron (MLP) for the purpose of e-mail spam identification, where the weights of this network model are found using a new nature-inspired metaheuristic algorithm called Biogeography Based Optimization (BBO). Experiments and results based on two different spam datasets show that the developed MLP model trained by BBO gets high generalization performance compared to other optimization methods used in the literature for e-mail spam detection.

V2G模式下基于SaDE-BBO算法的有源配电网优化

为了解决大规模电动汽车入网难以实现个体调度以及集群调度存在“维数灾”的问题,建立基于车辆到电网(vehicle-to-grid,V2G)模式的有源配电网分层分区优化运行模型。其中,上层优化模型对电动汽车集控中心(electric vehicle agent,EVA)进行调度,优化各区域EVA的充放电功率并作为下层优化模型的输入;下层优化模型调整各调压方式。在优化算法方面,提出一种自适应差分进化-生物地理学优化(self-adaptive differential evolution-biogeography-based optimization,SaDE-BBO)算法,并在改进的IEEE 33节点配电系统中进行仿真分析。结果表明:在不同充电控制策略下,V2G模式与各调压方式的协调互动在降低各区域EVA运营成本、平抑负荷波动以及保证有源配电网的安全和经济运行方面优势显著,与其他优化算法相比,SaDE-BBO算法具有更优质的解和更好的收敛性。

Biogeography-Based Combinatorial Strategy for Efficient Autonomous Underwater Vehicle Motion Planning and Task-Time Management

自治在水下车辆(AUV ) 能够花执行的时间的长时期各种各样在水下使命和海洋的任务。在这份报纸，计划框架的一个新奇没有冲突的运动被介绍提高在水下由通过大规模 waypoint 在一有限时间完成最高的优先级任务的最大的数字的车辆使命性能混乱操作地，和保证安全推广在使命期间。建议组合线路路径规划者模型为单个车辆操作向两个高度降低的水平运动规划者的令人满意的目的和使命生产率的保证最大化拿基于生物地理学的优化(BBO ) 算法的优点。模型的性能在变化时间的操作的域里包括特别费用限制在不同情形下面被调查。为了显示出建议模型，的可靠性，独立估计的每个运动规划者然后统计分析的性能被承担评估全部模型的全部的表演。模拟结果为真实实验显示贡献模型和它的可行申请的稳定性。

基于改进生物地理学算法的列车ATO多目标优化研究

高速列车运行过程优化是一个多目标、非线性优化问题.为研究列车自动驾驶系统(automatic train operation,ATO)多目标优化问题,以列车运行的准时性、停车精确性、舒适性、能耗性为控制目标,列车动力学模型为约束条件,同时考虑列车惰行过分相区,建立列车多目标优化模型,提出了一种改进的生物地理学(biogeography-based optimization,BBO)优化ATO速度曲线方法.为提高算法优化性能,使用更加倾向自然法则的双曲正切变体迁移模型;在变异过程中使用差分进化(differential evolutionary,DE)变异策略,提高种群多样性,同时加入柯西分布随机数帮助算法跳出局部最优;利用反向学习提高变异后个体的多样性,保证算法的全域搜索.同时通过基准测试函数验证该算法收敛速度和全局优化能力的优越性.以CRH3型高速列车和汉宜客运某线路进行仿真实验,结果表明,所提方法可以使列车追踪运行更加高效、舒适、安全和节能,其中舒适度提升了39.24%,能耗降低了3.5653%.

BBO晶体四倍频全固态小功率紫外激光器

利用KTP晶体和BBO晶体,进行了激光二极管泵浦的Nd:YVO4声光调Q激光脉冲四倍频实验。在不同绿光功率入射时,获得光束的束腰半径和紫外转换效率的依赖关系:当绿光功率为1.10 W,束腰半径为12.4μm时,得到了210 mW的准连续266 nm紫外脉冲输出,四倍频转换效率为19.1%。实验还对紫外远场光斑分别在o光振动面和e光振动面内进行分析,指出了BBO晶体在该两平面内不同的倍频接受角是造成椭圆形紫外光斑和主光斑附近明暗条纹的主要原因。

基于PSO-BBO混合优化算法的动态经济调度问题

动态经济调度(Dynamic Economic Dispatch,DED)问题是电力系统运行与控制领域比较经典的多变量、非线性、强约束优化问题。为解决该问题,提出了将粒子群优化算法(Particle Swarm Optimization,PSO)和基本生物地理学优化算法(Biogeography-Based Optimization,BBO)相结合的改进生物地理学优化算法,并将该改进方法应用于一天24时段10机39节点标准算例。在考虑网损与不考虑网损两种情况下分别进行仿真分析,并将仿真结果与PSO和基本BBO算法以及参考文献中提出的六种智能算法进行对比,验证了该改进算法的有效性及在寻优能力上的提高。

采用改进BBO算法的并网型微电网电源优化配置

针对并网型微电网的分布式电源优化配置问题,以年综合经济投资最小为目标,计及设备年等值投资成本、运行维护成本、燃料成本、环境折算成本以及年购电成本和余电出售收益,考虑分布式电源出力约束、系统自平衡度和冗余度等约束,建立了并网型微电网优化配置模型;同时提出一种基于余弦迁移模型、变尺度分段Lo-gistic混沌和高斯变异策略改进的生物地理学优化(BBO)算法用于模型求解。仿真结果验证了所提模型的合理性,并表明改进的BBO算法具有良好的收敛速度和收敛精度。

基于MIC-BBO-SVM的大坝渗流预测模型

为监控大坝运行过程中的异常状态,准确预测大坝渗流量的变化趋势,采用最大信息系数(MIC)量化渗流量与影响因子之间的相关性大小并从中选取主导因子作为输入变量,通过引入生物地理学优化算法(BBO)并以K折交叉验证意义下的平均均方根误差为损失函数来优化支持向量机(SVM)作为预测模型,以某水电站工程的拦河大坝为例进行模型验证。结果表明:MIC-BBO-SVM模型的拟合优度、均方根误差、平均绝对误差和平均绝对百分比误差分别为0.9575,0.1550 m^3/h,0.1356 m^3/h,11.51%,预测性能明显优于逐步回归模型、SVM模型和MIC-SVM模型,可为大坝渗流安全监测提供参考与借鉴。

一种改进的BBO算法及在热工PID优化中的应用

为了提高BBO算法在热工系统PID控制器参数优化方面的性能,改善其寻优能力,给出一种改进的生物地理学优化算法。在原有的迁移操作的基础上引入粒子群优化算法的寻优策略,使整个迁移过程具备一定的方向性,同时应用淘汰策略剔除迁移突变后较差的参数。一方面方向性的迁移及淘汰机制保证其快速的寻优收敛特性,另一方面突变机制保证广域搜索的全局特性,避免陷入局部极值。将其与原BBO算法进行比较,仿真结果表明改进的BBO算法在收敛速度和收敛精度上较标准BBO算法有较大提高,应用于PID控制器参数优化是可行的。

基于BBO优化BP神经网络的乳腺癌诊断

乳腺癌已成为世界上妇女发病率最高的癌症。医学研究发现,乳腺肿瘤病灶组织的细胞核显微图像与正常组织的细胞核显微图像不同,但是用一般的图像处理方法很难对其进行区分。因此,本文提出用生物地理学优化算法(BBO)优化BP神经网络对乳腺癌进行诊断,将乳腺肿瘤病灶组织的细胞核显微图像的10个量化特征作为网络的输入,良性乳腺肿瘤和恶性乳腺肿瘤作为网络的输出。用训练集数据对设计的BBOBP神经网络进行训练,然后对测试集数据进行测试并对测试结果进行分析。结果表明BBOBP有很好的分类性能,能对乳腺癌进行有效的诊断,且误诊率较低。

基于改进BBO算法的二维交叉熵多阈值图像分割（英文）

为了提高生物地理学优化(BBO)算法在多阈值图像分割中的全局搜索能力,提出一种基于改进的BBO算法的多阈值图像分割。在运用BBO算法进行优化阈值时,首先,采用精英选择算子保留出最优的几组解。其次,引入一种基于优秀解和待迁出解融合的迁移策略,以减少传统迁移操作的过早收敛以及无效迁移等行为。再次,为了减少传统变异操作的盲目性,创建一种通过二进制计算的变异操作。然后将其应用到二维交叉熵的多阈值图像分割中。最后,使用该方法对典型图像进行分割实验,并与粒子群算法的二维多阈值分割,以及基于标准的BBO算法的二维多阈值图像分割进行比较,实验结果表明:该方法具有良好的收敛稳定性,可以有效缩短迭代的时间,并且优化性能优于标准的BBO算法。