A Robust Video Watermarking Scheme with Squirrel Search Algorithm

Advancement in multimedia technology has resulted in protection against distortion,modification,and piracy.For implementing such protection,we have an existing technique called watermarking but obtaining desired distortion level with sufficient robustness is a challenging task for watermarking in multimedia applications.In the paper,we proposed a smart technique for video watermarking associating meta-heuristic algorithms along with an embedding method to gain an optimized efficiency.The main aim of the optimization algorithm is to obtain solutions with maximum robustness,and which should not exceed the set threshold of quality.To represent the accuracy of the proposed scheme,we employ a popular video watermarking technique(DCT domain)having frame selection and embedding method for watermarking.A squirrel search algorithm is chosen as a meta-heuristic algorithm that utilizes the stated fitness function.The results indicate that quality constraint is fulfilled,and the proposed technique gives improved robustness against different attacks with several quality thresholds.The proposed technique could be practically implemented in several multimedia applications such as the films industry,medical imagery,OOT platforms,etc.

A Discrete Multi-Objective Squirrel Search Algorithm for Energy-Efficient Distributed Heterogeneous Permutation Flowshop with Variable Processing Speed

In the manufacturing industry,reasonable scheduling can greatly improve production efficiency,while excessive resource consumption highlights the growing significance of energy conservation in production.This paper studies the problem of energy-efficient distributed heterogeneous permutation flowshop problem with variable processing speed(DHPFSP-VPS),considering both the minimum makespan and total energy consumption(TEC)as objectives.A discrete multi-objective squirrel search algorithm(DMSSA)is proposed to solve the DHPFSPVPS.DMSSA makes four improvements based on the squirrel search algorithm.Firstly,in terms of the population initialization strategy,four hybrid initialization methods targeting different objectives are proposed to enhance the quality of initial solutions.Secondly,enhancements are made to the population hierarchy system and position updating methods of the squirrel search algorithm,making it more suitable for discrete scheduling problems.Additionally,regarding the search strategy,six local searches are designed based on problem characteristics to enhance search capability.Moreover,a dynamic predator strategy based on Q-learning is devised to effectively balance DMSSA’s capability for global exploration and local exploitation.Finally,two speed control energy-efficient strategies are designed to reduce TEC.Extensive comparative experiments are conducted in this paper to validate the effectiveness of the proposed strategies.The results of comparing DMSSA with other algorithms demonstrate its superior performance and its potential for efficient solving of the DHPFSP-VPS problem.

Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique

Concrete is the most commonly used construction material.However,its production leads to high carbon dioxide(CO_(2))emissions and energy consumption.Therefore,developing waste-substitutable concrete components is necessary.Improving the sustainability and greenness of concrete is the focus of this research.In this regard,899 data points were collected from existing studies where cement,slag,fly ash,superplasticizer,coarse aggregate,and fine aggregate were considered potential influential factors.The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult.Instead of the traditional compressive strength test,this study combines five novel metaheuristic algorithms with extreme gradient boosting(XGB)to predict the compressive strength of green concrete based on fly ash and blast furnace slag.The intelligent prediction models were assessed using the root mean square error(RMSE),coefficient of determination(R^(2)),mean absolute error(MAE),and variance accounted for(VAF).The results indicated that the squirrel search algorithm-extreme gradient boosting(SSA-XGB)yielded the best overall prediction performance with R^(2) values of 0.9930 and 0.9576,VAF values of 99.30 and 95.79,MAE values of 0.52 and 2.50,RMSE of 1.34 and 3.31 for the training and testing sets,respectively.The remaining five prediction methods yield promising results.Therefore,the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete.Finally,the developed SSA-XGB considered the effects of all the input factors on the compressive strength.The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.

Single and multi-area multi-fuel economic dispatch using a fuzzified squirrel search algorithm

Multi-Area Multi-Fuel Economic Dispatch (MAMFED) aims to allocate the best generation schedule in each area and to offer the best power transfers between different areas by minimizing the objective functions among the available fuel alternatives for each unit while satisfying various constraints in power systems. In this paper, a Fuzzified Squirrel Search Algorithm (FSSA) algorithm is proposed to solve the single-area multi-fuel economic dispatch (SAMFED) and MAMFED problems. Squirrel Search Algorithm (SSA) mimics the foraging behavior of squirrels based on the dynamic jumping and gliding strategies. In the SSA approach, predator presence behavior and a seasonal monitoring condition are employed to increase the search ability of the algorithm, and to balance the exploitation and exploration. The suggested approach considers the line losses, valve point loading impacts, multi-fuel alternatives, and tie-line limits of the power system. Because of the contradicting nature of fuel cost and pollutant emission objectives, weighted sum approach and price penalty factor are used to transfer the bi-objective function into a single objective function. Furthermore, a fuzzy decision strategy is introduced to find one of the Pareto optimal fronts as the best compromised solution. The feasibility of the FSSA is tested on a three-area test system for both the SAMFED and MAMFED problems. The results of FSSA approach are compared with other heuristic approaches in the literature. Multi-objective performance indicators such as generational distance, spacing metric and ratio of non-dominated individuals are evaluated to validate the effectiveness of FSSA. The results divulge that the FSSA is a promising approach to solve the SAMFED and MAMFED problems while providing a better compromise solution in comparison with other heuristic approaches.

Squirrel Search Optimization with Deep Convolutional Neural Network for Human Pose Estimation

Human pose estimation(HPE)is a procedure for determining the structure of the body pose and it is considered a challenging issue in the computer vision(CV)communities.HPE finds its applications in several fields namely activity recognition and human-computer interface.Despite the benefits of HPE,it is still a challenging process due to the variations in visual appearances,lighting,occlusions,dimensionality,etc.To resolve these issues,this paper presents a squirrel search optimization with a deep convolutional neural network for HPE(SSDCNN-HPE)technique.The major intention of the SSDCNN-HPE technique is to identify the human pose accurately and efficiently.Primarily,the video frame conversion process is performed and pre-processing takes place via bilateral filtering-based noise removal process.Then,the EfficientNet model is applied to identify the body points of a person with no problem constraints.Besides,the hyperparameter tuning of the EfficientNet model takes place by the use of the squirrel search algorithm(SSA).In the final stage,the multiclass support vector machine(M-SVM)technique was utilized for the identification and classification of human poses.The design of bilateral filtering followed by SSA based EfficientNetmodel for HPE depicts the novelty of the work.To demonstrate the enhanced outcomes of the SSDCNN-HPE approach,a series of simulations are executed.The experimental results reported the betterment of the SSDCNN-HPE system over the recent existing techniques in terms of different measures.

基于改进松鼠搜索算法优化神经网络的数控机床进给系统热误差预测

为探究数控机床进给系统中各因素对热误差的影响规律,建立精准的热误差预测模型。在进给速度为10 m/min、环境温度20℃的条件下进行进给系统热误差测量实验,获得进给系统关键点的温升及热误差。为提高预测精度,采用Tent混沌改进松鼠搜索算法,并利用改进的算法对神经网络进行优化,建立热误差预测模型。利用热误差测量实验获得的数据进行验证,结果表明改进前的神经网络预测误差为12.23%,改进后的模型预测误差为8.92%,精度有较大提升。利用预测模型针对不同进给速度下相同位置处热误差进行分析,结果表明,进给系统中关键测温点的温度和丝杠各点的热误差随着进给速度的增加而增加。因此提出的预测模型可实现进给系统热误差的准确预测,为误差补偿提供理论依据。

基于ISSA和IA^(*)的AGV集成作业调度及其路径规划

针对单一算法在求解车间调度和路径问题时最优性和多样性方面的缺陷,提出了优化飞鼠搜索算法ISSA(improved squirrel search algorithm)和优化A^(*)算法并建立集成作业调度和AGV路径规划的双层模型。首先,采用贪婪策略融合飞鼠搜索算法建立考虑能耗的AGV集成作业调度上层模型;其次,将安全距离因子引入A^(*)算法,构建AGV路径规划下层模型,并通过梯度下降法进行路径平滑;进而,运用6个测试函数和kacem实例验证ISSA的寻优能力,结果表明ISSA的其收敛速度较快,运行效率较高,且不容易陷入局部最优;最后,基于栅格法建模进行对比仿真实验,IA^(*)比A^(*)算法拐点数量降低了22%,同时节约了21%的行驶时间,ISSA和IA^(*)均得到了良好的验证。结果表明,ISSA和IA^(*)能够更有效求解AGV集成作业调度及其路径规划问题。

虚拟同步发电机参数设计及优化研究

虚拟同步发电机的参数众多,参数设计较为复杂,常规方法难以保证设计效果。针对该问题,采用控制变量的方法改变阻尼系数、转动惯量、下垂系数和积分系数值大小,在分析比较不同参数值下虚拟同步发电机的有功、无功动态响应效果的基础上,通过建立以系统动态响应稳定性能为目标的优化函数,提出了一种基于松鼠搜索算法的虚拟同步发电机参数优化设计方法。仿真结果表明,提出的参数优化设计方法能够更准确地选择出合理参数,有效提升了虚拟同步发电机的动态响应性能,具有较好实用性。

融合松鼠搜索策略的混沌飞蛾算法

飞蛾算法是一种结构简单、配置参数少且适用范围广的群智能算法,但在收敛精度和收敛速度等方面还有待提高,且存在易收敛到局部最优的问题,为此提出一种融合松鼠搜索策略的混沌飞蛾算法。该策略采用sinusoidal混沌映射获取高质量初始种群;在飞蛾寻优过程中引入松鼠算法中松鼠的寻优途径,设置高质量火焰个体与近距离火焰个体指导飞蛾高质量寻优,通过余弦控制因子触发的捕食者概率促使飞蛾跳出原始火焰对其的吸引,提高飞蛾算法全局搜索能力;改造自适应t分布因子与火焰自适应减少公式,控制适应度较差的种群通过列维飞行进行随机迁移,增加算法的局部搜索能力。通过CEC2017测试集、CEC2022测试集与两个工程应用实例分别与其他15种智能算法进行对比验证,结果表明改进算法在收敛速度、搜索能力和跳出局部最优等方面具有一定优势。

改进松鼠搜索算法求解分布式节能柔性调度

为了优化同时考虑最大完工时间和机器能耗的双目标分布式柔性作业车间调度问题,提出了一种改进的多目标松鼠搜索算法。引入了基于升序排列规则的转换机制,实现了松鼠位置向量与调度解之间的转换,并针对机器空闲时间设计了从半主动到主动的解码策略。针对不同优化目标设计了三种种群初始化策略。同时提出了动态捕食者策略来更好地协调算法的全局探索和局部开发能力。设计了四种领域搜索策略用于增加种群多样。20个实例上的实验结果验证了改进后的算法求得解的质量和多样性更好,从而证明了其可有效求解分布式节能柔性调度问题。

基于自适应参数优化RSSD-CYCBD的行星齿轮箱复合故障诊断

针对行星齿轮箱多振源耦合导致故障源辨识困难、较弱故障特征容易被噪声和较强故障特征掩盖,以及由传播路径引起的信号衰减导致的故障特征微弱等问题,提出一种自适应参数优化的共振稀疏分解(RSSD)和最大二阶循环平稳盲解卷积(CYCBD)的行星齿轮箱多故障耦合信号分离及诊断算法。根据轴承和齿轮故障的不同共振属性,用RSSD算法将多故障耦合信号分解为包含齿轮故障特征的高共振分量和主要包含轴承故障特征的低共振分量后,通过CYCBD算法分别对高、低分量进行解卷积,消除传播路径影响和噪声干扰,实现微弱故障特征的增强和提取。特别地,针对RSSD和CYCBD中参数优化困难、依赖人工经验和自适应差等问题,使用基于松鼠算法(SSA)对参数进行自适应优化选取,设计了融合包络谱峭度、自相关函数最大值均方根和特征频率比在内的复合指标作为优化目标。对解卷积后的信号进行包络解调提取故障特征频率,识别不同故障源。通过行星齿轮箱多故障模拟信号和实测信号验证了所提算法的有效性和可行性,进一步地,将所提算法集成在边缘计算设备中,为行星齿轮箱等旋转机械的状态检测诊断及远程运维提供解决方案。

基于ISSA的有杆泵抽油井产量计量方法

油井产量计量是油田开采过程中最重要的工作之一,准确计量产量对提高油田开采效率和经济效益至关重要。传统翻斗、两相分离式和示功图等量油技术存在投入大、维护成本高和适应性差等问题。为解决上述问题,提出一种基于改进松鼠搜索算法(improved squirrel search algorithm, ISSA)的油井产量计量方法。通过分析抽油井的工作过程,提出一组描述抽油泵漏失和充满度的故障参数,建立故障条件下的抽油系统过程仿真模型。针对油井故障参数优化问题,提出一种基于食物定位因子的改进松鼠搜索算法,建立基于ISSA的产量计量模型。采用大庆油田数据验证模型,算法平均预测精度为94.52%,表明本方法可为低成本数字油田建设提供支撑。

混合随机反向学习和高斯变异的混沌松鼠搜索算法

针对松鼠搜索算法(SSA)易陷入局部最优、过早收敛等问题,提出一种混合随机反向学习和高斯变异的混沌松鼠搜索算法(RGCSSA)。该算法通过Tent混沌映射初始化策略生成混沌初始种群,增强初始种群分布的均匀性,实现对解空间更高效的搜索;采用非线性递减的捕食者概率策略,平衡SSA的全局搜索和局部开发能力;利用位置贪婪选择策略在迭代过程中不断保留种群中的优势个体,以提升算法收敛速度;引入随机反向学习和高斯变异策略,在增加种群多样性的同时提高算法跳出局部最优的能力。使用10个不同的基准测试函数进行仿真实验,并利用Wilcoxon符号秩检验验证所提算法的寻优性能,结果表明,RGCSSA算法在求解精度、收敛速度和稳定性等方面均有极大提升。

基于VMD-ISSA-KELM的短期光伏发电功率预测

针对光伏发电功率存在随机性和波动性较强、预测精度较低的问题,提出了一种基于变分模态分解(variational mode decomposition,VMD)和改进松鼠觅食算法优化核极限学习机(improved squirrel search algorithm optimization kernel extreme learning machine,ISSA-KELM)的预测模型。首先,利用高斯混合模型(Gaussian mixture model,GMM)将光伏发电功率数据进行聚类,得到不同天气类型下的相似日样本。其次,利用VMD对原始光伏发电功率序列进行平稳化处理,得到若干个规律性较强的子序列。然后,对不同子序列构建KELM预测模型,并使用ISSA优化KELM的核参数和正则化系数。最后,将不同子序列的预测值进行重构,得到最终预测结果。结合实际算例,结果表明:所提出的VMD-ISSA-KELM模型在不同天气条件下均能得到满意的预测精度,且明显优于其他模型,验证了其有效性和优越性。

基于自适应VMD-KPCA特征提取与SSA-SVM方法的滚动轴承故障诊断

为降低滚动轴承故障特征维度,更好地选取算法参数,提高故障诊断率,提出了自适应VMD-KPCA特征提取与SSA-SVM相结合的滚动轴承故障诊断方法。首先,利用飞鼠搜索算法(SSA)对VMD中分解层数k和惩罚因子α的最优组合进行寻优,形成自适应的VMD对振动信号进行分解;其次,利用SSA对SVM中核函数参数g和惩罚因子c进行寻优,构建了SSA-SVM故障诊断模型;最后,对利用自适应VMD分解出的时域、频域、能量熵等IMF分量的故障特征进行计算,并经KPCA降维后输入SSA-SVM模型中,与多种故障诊断模型进行仿真对比分析。结果表明,SSA-SVM从适应度、准确率、运行时间上,都具有优越性;同时将用KPCA降维与未降维的SSA-SVM进行对比,证明用KPCA降维的SSA-SVM虽牺牲了少量准确率,却换取了运行时间上的大幅度提高。

Removal of Ocular Artifacts from Electroencephalo-Graph by Improving Variational Mode Decomposition

Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30.

Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target data.There are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard approaches.Consequently,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been proposed.When using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to instability.Because of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization process.For the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed before.According to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance vs.eleven other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and bGAmethods.Experimental results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

基于多种群竞争松鼠搜索算法的机械臂时间最优轨迹规划

针对传统智能优化算法在机械臂关节空间进行时间最优轨迹规划应用中存在的寻优效率低、优化结果全局性和稳定性差的问题,提出新的机械臂时间最优轨迹规划方法.在建立机械臂关节空间内的时间最优轨迹规划模型时考虑位置约束,根据输入的关节点列,使用S形曲线估算时间的取值区间,对生成算法的所有个体进行多种群竞争迭代,得出机械臂关节空间轨迹规划的时间最优解.与不同算法的仿真对比试验结果表明,所提方法较传统的优化算法具有更高的寻优效率和更好的优化全局性;所提方法的稳定性好,其多次优化结果的方差相较单种群算法低3个数量级.

基于卷积神经网络和松鼠优化算法的机翼结构混合优化设计

针对大展弦比机翼的结构轻量化优化设计,提出了一种高效的布局和尺寸混合优化方法.在CFD/CSD气动弹性计算的基础上,对不同的结构变量进行统一编码,使用一维卷积神经网络建立代理模型,并使用松鼠优化算法建立了混合优化模型进行搜索寻优.以某型太阳能无人机的机翼结构优化为例,优化结果表明翼肋的布局变量和翼梁的尺寸变量之间存在着耦合关系,使用松鼠优化算法相比于遗传算法节省了35%~45%的计算成本,且混合优化后的结构比原始结构减重4.1%,验证了该方法的有效性.

基于共振稀疏分解和松鼠优化算法的滚动轴承故障诊断

共振稀疏分解方法在滚动轴承故障诊断方面得到广泛应用,分解参数的选取对故障分离效果起决定性影响。为保证参数选择的准确性,提出基于松鼠算法的自适应共振稀疏分解多参数优化方法。以信号低共振分量峭度最大作为目标,使用松鼠算法同时优化共振稀疏分解的品质因子与权重系数;利用最优品质因子和权重系数对滚动轴承振动信号进行共振稀疏分解,得到高低共振分量;对低共振分量进行希尔伯特包络谱分析。通过仿真试验和应用实例证明,所提方法可以有效提取轴承的微弱故障信息,实现共振稀疏分解小波基函数库与耗散函数之间的最优匹配,具有较高的分离精度。