Pilot Allocation Optimization Using Enhanced Salp Swarm Algorithm for Sparse Channel Estimation

Pilot pattern has a significant effect on the performance of channel estimation based on compressed sensing.However,because of the influence of the number of subcarriers and pilots,the complexity of the enumeration method is computationally impractical.The meta-heuristic algorithm of the salp swarm algorithm(SSA)is employed to address this issue.Like most meta-heuristic algorithms,the SSA algorithm is prone to problems such as local optimal values and slow convergence.In this paper,we proposed the CWSSA to enhance the optimization efficiency and robustness by chaotic opposition-based learning strategy,adaptive weight factor,and increasing local search.Experiments show that the test results of the CWSSA on most benchmark functions are better than those of other meta-heuristic algorithms.Besides,the CWSSA algorithm is applied to pilot pattern optimization,and its results are better than other methods in terms of BER and MSE.

Optimization of Cognitive Radio System Using Self-Learning Salp Swarm Algorithm

CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the systemparameters.The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation.As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user throughput.Self-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of salps.In this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary users.The results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.

Multi-Strategy-Driven Salp Swarm Algorithm for Global Optimization

In response to the shortcomings of the Salp Swarm Algorithm (SSA) such as low convergence accuracy and slow convergence speed, a Multi-Strategy-Driven Salp Swarm Algorithm (MSD-SSA) was proposed. First, food sources or random leaders were associated with the current bottle sea squirt at the beginning of the iteration, to which Levy flight random walk and crossover operators with small probability were added to improve the global search and ability to jump out of local optimum. Secondly, the position mean of the leader was used to establish a link with the followers, which effectively avoided the blind following of the followers and greatly improved the convergence speed of the algorithm. Finally, Brownian motion stochastic steps were introduced to improve the convergence accuracy of populations near food sources. The improved method switched under changes in the adaptive parameters, balancing the exploration and development of SSA. In the simulation experiments, the performance of the algorithm was examined using SSA and MSD-SSA on the commonly used CEC benchmark test functions and CEC2017-constrained optimization problems, and the effectiveness of MSD-SSA was verified by solving three real engineering problems. The results showed that MSD-SSA improved the convergence speed and convergence accuracy of the algorithm, and achieved good results in practical engineering problems.

Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation

This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding,called RSA-SSA.The proposed method introduces a better search space to find the optimal solution at each iteration.However,we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds.The obtained solutions by the proposed method are represented using the image histogram.The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level.The performance measure for the proposed method is valid by detecting fitness function,structural similarity index,peak signal-to-noise ratio,and Friedman ranking test.Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA.The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.

Adaptive Barebones Salp Swarm Algorithm with Quasi-oppositional Learning for Medical Diagnosis Systems: A Comprehensive Analysis

The Salp Swarm Algorithm(SSA)may have trouble in dropping into stagnation as a kind of swarm intelligence method.This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning to compensate for the above weakness called QBSSA.In the proposed QBSSA,an adaptive barebones strategy can help to reach both accurate convergence speed and high solution quality;quasi-oppositional-based learning can make the population away from traping into local optimal and expand the search space.To estimate the performance of the presented method,a series of tests are performed.Firstly,CEC 2017 benchmark test suit is used to test the ability to solve the high dimensional and multimodal problems;then,based on QBSSA,an improved Kernel Extreme Learning Machine(KELM)model,named QBSSA–KELM,is built to handle medical disease diagnosis problems.All the test results and discussions state clearly that the QBSSA is superior to and very competitive to all the compared algorithms on both convergence speed and solutions accuracy.

Hybrid Chaotic Salp Swarm with Crossover Algorithm for Underground Wireless Sensor Networks

Resource management in Underground Wireless Sensor Networks(UWSNs)is one of the pillars to extend the network lifetime.An intriguing design goal for such networks is to achieve balanced energy and spectral resource utilization.This paper focuses on optimizing the resource efficiency in UWSNs where underground relay nodes amplify and forward sensed data,received from the buried source nodes through a lossy soil medium,to the aboveground base station.A new algorithm called the Hybrid Chaotic Salp Swarm and Crossover(HCSSC)algorithm is proposed to obtain the optimal source and relay transmission powers to maximize the network resource efficiency.The proposed algorithm improves the standard Salp Swarm Algorithm(SSA)by considering a chaotic map to initialize the population along with performing the crossover technique in the position updates of salps.Through experimental results,the HCSSC algorithm proves its outstanding superiority to the standard SSA for resource efficiency optimization.Hence,the network’s lifetime is prolonged.Indeed,the proposed algorithm achieves an improvement performance of 23.6%and 20.4%for the resource efficiency and average remaining relay battery per transmission,respectively.Furthermore,simulation results demonstrate that the HCSSC algorithm proves its efficacy in the case of both equal and different node battery capacities.

A Boosted Communicational Salp Swarm Algorithm: Performance Optimization and Comprehensive Analysis

The Salp Swarm Algorithm (SSA) is a recently proposed swarm intelligence algorithm inspired by salps, a marine creature similar to jellyfish. Despite its simple structure and solid exploratory ability, SSA suffers from low convergence accuracy and slow convergence speed when dealing with some complex problems. Therefore, this paper proposes an improved algorithm based on SSA and adds three improvements. First, the Real-time Update Mechanism (RUM) underwrites the role of ensuring that excellent individual information will not be lost and information exchange will not lag in the iterative process. Second, the Communication Strategy (CMS), on the other hand, uses the multiplicative relationship of multiple individuals to regulate the exploration and exploitation process dynamically. Third, the Selective Replacement Strategy (SRS) is designed to adaptively adjust the variance ratio of individuals to enhance the accuracy and depth of convergence. The new proposal presented in this study is named RCSSSA. The global optimization capability of the algorithm was tested against various high-performance and novel algorithms at IEEE CEC 2014, and its constrained optimization capability was tested at IEEE CEC 2011. The experimental results demonstrate that the proposed algorithm can converge faster while obtaining better optimization results than traditional swarm intelligence and other improved algorithms. The statistical data in the table support its optimization capabilities, and multiple graphs deepen the understanding and analysis of the proposed algorithm.

Double Mutational Salp Swarm Algorithm:From Optimal Performance Design to Analysis

The Salp Swarm Algorithm(SSA)is a population-based Meta-heuristic Algorithm(MA)that simulates the behavior of a group of salps foraging in the ocean.Although the basic SSA has stable exploration capability and convergence speed,it still can fall into local optimum when solving complex optimization problems,which may be due to low utilization of population information and unbalanced exploration-to-exploitation ratio.Therefore,this study proposes a Double Mutation Salp Swarm Algorithm(DMSSA).In this study,a Cuckoo Mutation Strategy(CMS)and an Adaptive DE Mutation Strategy(ADMS)are introduced into the structure of the original SSA.The former mutation strategy is summarized as three basic operations:judgment,shuffling,and mutation.The purpose is to fully consider the information among search agents and use the differences between different search agents to participate in the update of positions,making the optimization process both diverse in exploration and minor in randomness.The latter strategy employs three basic operations:selection,mutation,and adaptation.As the follower part,some individuals do not blindly adopt the original follow method.Instead,the global optimal position and differences are considered,and the variation factor is adjusted adaptively,allowing the new algorithm to balance exploration,exploitation,and convergence efficiency.To evaluate the performance of DMSSA,comparisons are made with numerous algorithms on 30 IEEE CEC2014 benchmark functions.The statistical results confirm the better performance and significant difference of DMSSA in solving benchmark function tests.Finally,the applicability and scalability of DMSSA to optimization problems with constraints are further confirmed in three experiments on classical engineering design optimization problems.The source code of the proposed algorithm will be available at:https://github.com/ncjsq/Double-Mutational-Salp-Swarm-Algorithm.

Locomotion-based Hybrid Salp Swarm Algorithm for Parameter Estimation of Fuzzy Representation-based Photovoltaic Modules

Identifying the parameters of photovoltaic(PV)modules is significant for their design and simulation.Because of the instabilities in the weather action and land surface of the earth,which cause errors in measuring,a novel fuzzy representation-based PV module is formulated and developed.In this paper,a novel locomotion-based hybrid salp swarm algorithm(LHSSA)is presented to identify the parameters of PV modules accurately and reliably.In the LHSSA,better leader salps based on particle swarm optimization(PSO)are incorporated to the traditional salp swarm algorithm(SSA)in a serialized scheme with the aim of providing more valuable information for the leader salps of the SSA.By this integration,the proposed LHSSA can escape the local optima as well as guide the seeking process to attain the promising region.The proposed LHSSA is investigated on different PV models,i.e.,single-diode(SD),double-diode(DD),and PV module in crisp and fuzzy aspects.By comparing with different algorithms,the comprehensive results affirm that the LHSSA can achieve a highly competitive performance,especially on quality and reliability.

Availability Capacity Evaluation and Reliability Assessment of Integrated Systems Using Metaheuristic Algorithm

Contemporarily,the development of distributed generations(DGs)technologies is fetching more,and their deployment in power systems is becom-ing broad and diverse.Consequently,several glitches are found in the recent studies due to the inappropriate/inadequate penetrations.This work aims to improve the reliable operation of the power system employing reliability indices using a metaheuristic-based algorithm before and after DGs penetration with feeder system.The assessment procedure is carried out using MATLAB software and Mod-ified Salp Swarm Algorithm(MSSA)that helps assess the Reliability indices of the proposed integrated IEEE RTS79 system for seven different configurations.This algorithm modifies two control parameters of the actual SSA algorithm and offers a perfect balance between the exploration and exploitation.Further,the effectiveness of the proposed schemes is assessed using various reliability indices.Also,the available capacity of the extended system is computed for the best configuration of the considered system.The results confirm the level of reli-able operation of the extended DGs along with the standard RTS system.Speci-fically,the overall reliability of the system displays superior performance when the tie lines 1 and 2 of the DG connected with buses 9 and 10,respectively.The reliability indices of this case namely SAIFI,SAIDI,CAIDI,ASAI,AUSI,EUE,and AEUE shows enhancement about 12.5%,4.32%,7.28%,1.09%,4.53%,12.00%,and 0.19%,respectively.Also,a probability of available capacity at the low voltage bus side is accomplished a good scale about 212.07 times/year.

基于Salp群算法的多堆燃料电池系统效率优化控制方法

为提高多堆燃料电池系统(multi-stack fuel cell system,MFCS)整体效率和维持母线电压的稳定,该文提出一种基于Salp群算法(Salp swarm algorithm,SSA)的MFCS效率优化控制方法。利用SSA算法的快速搜索能力实时优化系统整体效率,实现多个燃料电池间功率的合理分配,并通过下垂控制策略维持母线电压长期稳定。最后,在RT-LAB上搭建硬件在环(hardware-in-the-loop,HIL)仿真平台,与平均功率分配方法和Daisy链式功率分配方法进行对比分析,从功率、效率、容错能力三方面做实验测试。结果表明,所提控制方法既可以保证MFCS整体效率实时优化,稳定母线电压,也可以增强系统容错性,减少MFCS运行成本且能提高燃料电池耐久性和抗扰动能力。

Swarm-Based Extreme Learning Machine Models for Global Optimization

Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space complexity.In ELM,the hidden layer typically necessitates a huge number of nodes.Furthermore,there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear period.In the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.

基于双层优化VMD-LSTM的农村超短期电力负荷预测

稳定的供电是农村发展建设的有力保障,而电力负荷水平是建设效果的重要衡量标准,因此建立精确的负荷预测模型可以更准确直观显现电力负荷情况,为供电公司制定决策提供有力支撑。由于LSTM负荷预测模型在数据预测方面存在收敛性差、预测精度不高等问题,为提高模型的预测精度,提出一种基于双层优化VMD-LSTM的超短期电力负荷预测方法。首先提出麻雀算法优化变分模态分解(sparrow variational mode decomposition,SVMD),通过SVMD将原始数据转化为模态分量(intrinsic mode functions,IMF);其次采用改进樽海鞘群算法(association salp swarm algorithm,ASSSA)优化LSTM模型。通过引入4种策略增强标准樽海鞘算法优化能力;最后将各模态分量分别代入到新模型并进行叠加预测。选取辽宁省某市某乡村10kV变压器真实历史负荷数据,以均方根误差(RMSE)、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)、拟合度(R^(2))作为评价指标,并与其他基础预测模型进行对比,结果表明,改进后的算法在计算精度、稳定性方面均优于其他基础预测模型。

路径规划问题的多策略改进樽海鞘群算法研究

针对移动机器人寻找最优路径问题,提出了一种融合无标度网络、自适应权重和黄金正弦算法变异策略的樽海鞘群算法BAGSSA(Adaptive Salp Swarm Algorithm with Scale-free of BA Network and Golden Sine)。首先,生成一个无标度网络来映射跟随者的关系,增强算法全局寻优的能力,在追随者进化过程中集成自适应权重ω,以实现算法探索和开发的平衡;同时选用黄金正弦算法变异进一步提高解的精度。其次,对12个基准函数进行仿真求解,实验数据表明平均值、标准差、Wilcoxon检验和收敛曲线均优于基本樽海鞘群和其他群体智能算法,证明了所提算法具有较高的寻优精度和收敛速度。最后,将BAGSSA应用于移动机器人路径规划问题中,并在两种测试环境中进行仿真实验,仿真结果表明,改进樽海鞘群算法较其他算法所寻路径更优,并具有一定理论与实际应用价值。

Salp Swarm Incorporated Adaptive Dwarf Mongoose Optimizer with Lévy Flight and Gbest-Guided Strategy

In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.

井下电力电缆故障定位研究

针对传统井下电力电缆故障定位方法依赖主观参数选择和抗噪性能较差,无法满足强噪声背景下井下电力电缆故障精确定位要求的问题,提出了一种基于樽海鞘群算法(SSA)优化变分模态分解(VMD)并结合改进型Teager能量算子(NTEO)的井下电力电缆故障定位方法。针对VMD在信号分解上存在的模态混叠、过分解和欠分解问题,采用SSA以模糊熵为适应度函数对VMD模态数K和惩罚因子α2个参数进行优化,得到更能反映故障特征信息的本征模态函数;采用NTEO对本征模态函数进行首波波头标定,得到首末两端的波头到达时刻,根据双端测距法得出故障位置。采用PSCAD/EMTDC进行井下电力电缆故障仿真,模拟具有强背景噪声的井下故障信号,结果表明:①在理想电流信号中加入9,12 dB噪声后,SSA-VMD的信噪比最低,皮尔逊相关系数最大,说明SSA-VMD在最大程度降噪的同时,能很好地保留信号的特征信息。②在不同过渡电阻下,SSA-VMD-NTEO的定位精度较高。③在不同故障相角下,SSA-VMD-NTEO在采样点上出现不同,但定位位置没有改变,依旧保持较高的定位精度。④在不同故障距离下,SSA-VMD-NTEO均能保证较高的定位精度。⑤在井下较大噪声和10 MHz采样频率下,SSA-VMD-NTEO较小波模极大值和VMD+NTEO 2种方法的定位精度具有明显优势。

基于改进樽海鞘群算法的含瓦斯煤破裂过程信号特征识别

针对标准樽海鞘群算法存在的计算精度不足、易陷入局部停滞等缺陷,提出一种多策略融合的樽海鞘群算法。在初始化阶段,引入线性同余法随机发生器;利用野马算法优化樽海鞘领导者位置;采用金豺算法改进樽海鞘种群追随机制。通过测试函数寻优对比实验,证明多策略融合的樽海鞘群算法相比于其他智能算法在鲁棒性与稳定性方面均有显著提升。将多策略融合的樽海鞘群算法应用到含瓦斯煤破裂过程信号特征识别,实验结果表明:提出的含瓦斯煤破裂过程信号特征识别模型具有更好的表现,准确率可达93.33%,相比其他识别模型,识别率更高。

基于改进SSA结合模糊RBF神经网络的悬臂梁振动主动控制

随着航空航天事业的发展,为了节省燃料,同时提高航天器速度,航天器采用更轻的材料来减少质量。然而,此举也引入了柔性振动,灵活的振动增加了姿态控制的时间,导致姿态精度控制不尽如人意。因此,有效抑制柔性振动以实现高精度姿态控制非常重要。论文以柔性压电悬臂梁作被控对象,并利用压电薄膜(Polyvinylidene Fluoride,PVDF)作传感器和致动器,分析其振动的控制问题。基于PID和模糊理论的局限性,结合模糊控制器能模仿专家经验和径向基神经网络(Radial Basis Function Network,RBFNN)善于学习的优点,设计了模糊径向基(Fuzzy Radial Basis Function,FRBF)神经网络控制器来抑制悬臂梁的振动,并采用混沌映射的种群初始化策略、疯狂算子的领导者位置更新策略、精英保留及动态惯性权重的追随者位置更新策略改进的樽海鞘群算法(Salp Swarm Algorithm,SSA)来优化模糊神经网络权值。将改进后的控制方法在Matlab软件环境下进行了数值仿真,仿真结果表明,应用改进的模糊径向基神经网络控制器可以有效地提升主动控制的振动效果。

基于改进樽海鞘群算法的多目标柔性作业车间调度问题研究

针对多目标柔性作业车间调度问题,构建了以最小化总能耗、最小化生产成本及最小化惩罚值为优化目标的数学模型,并设计改进的多目标樽海鞘群算法(IMSSA)进行求解。改进算法主要由樽海鞘领导者和樽海鞘追随者两部分构成,其中,领导者位置更新结合正余弦算法来实现,追随者位置更新基于线性微分递减的惯性权重方法来完成。此外,引入食物源存储库用于保留非支配解。最后通过对比实验证明了所提策略及改进算法的有效性。

基于疯狂自适应樽海鞘群优化算法的异构多核任务调度

为了解决当前异构多核环境下的任务调度效率不能满足应用程序的多样性要求的问题,论文基于疯狂自适应的樽海鞘群优化算法(Crazy and Adaptive Salp Swarm Algorithm,CASSA),提出一种异构多核处理器任务调度算法。该算法以缩短全部任务的完成时间为目标,根据任务优先权规则设计任务分配的编码方案,利用CASSA算法中领导者的全局搜索能力和追随者的局部搜索能力,使CASSA算法在异构多核任务调度问题上有更高的收敛效率和更高质量的解。实验表明,CASSA算法的性能优良,最优解的质量高,在异构多核处理器任务调度领域中具有良好的研究意义。