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 o...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.展开更多
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 me...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.展开更多
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 ut...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.展开更多
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 ...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.展开更多
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 st...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.展开更多
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 rapid...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.展开更多
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-S...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.展开更多
When building geotechnical constructions like retaining walls and dams is of interest,one of the most important factors to consider is the soil’s shear strength parameters.This study makes an effort to propose a nove...When building geotechnical constructions like retaining walls and dams is of interest,one of the most important factors to consider is the soil’s shear strength parameters.This study makes an effort to propose a novel predictive model of shear strength.The study implements an extreme gradient boosting(XGBoost)technique coupled with a powerful optimization algorithm,the salp swarm algorithm(SSA),to predict the shear strength of various soils.To do this,a database consisting of 152 sets of data is prepared where the shear strength(τ)of the soil is considered as the model output and some soil index tests(e.g.,dry unit weight,water content,and plasticity index)are set as model inputs.Themodel is designed and tuned using both effective parameters of XGBoost and SSA,and themost accuratemodel is introduced in this study.Thepredictionperformanceof theSSA-XGBoostmodel is assessedbased on the coefficient of determination(R2)and variance account for(VAF).Overall,the obtained values of R^(2) and VAF(0.977 and 0.849)and(97.714%and 84.936%)for training and testing sets,respectively,confirm the workability of the developed model in forecasting the soil shear strength.To investigate the model generalization,the prediction performance of the model is tested for another 30 sets of data(validation data).The validation results(e.g.,R^(2) of 0.805)suggest the workability of the proposed model.Overall,findings suggest that when the shear strength of the soil cannot be determined directly,the proposed hybrid XGBoost-SSA model can be utilized to assess this parameter.展开更多
This article proposes a novel method for maintaining the trajectory of an aerial manipulator by utilizing a fast nonsingular terminal sliding mode(FNTSM)manifold and a linear extended state observer(LESO).The develope...This article proposes a novel method for maintaining the trajectory of an aerial manipulator by utilizing a fast nonsingular terminal sliding mode(FNTSM)manifold and a linear extended state observer(LESO).The developed controlmethod applies an FNTSMto ensure the tracking performance’s control accuracy,and an LESO to estimate the system’s unmodeled dynamics and external disturbances.Additionally,an improved salp swarm algorithm(ISSA)is employed to parameter tune the suggested controller by integrating the salp swarmtechnique with a cloud model.This approach also uses a model-free scheme to reduce the complexity of controller design without relying on complex and precise dynamics models.The simulation results show that the proposed controller outperforms linear active rejection disturbance control and PID controllers in terms of transient performance and resilience against lumped disturbances,and the ISSA can help the proposed controller find optimal control parameters.展开更多
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 fro...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.展开更多
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 converg...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.展开更多
Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but ...Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions.The outcomes of the models are usually validated with the experimental results,but a large gap usually exists between them.Therefore,a model that can give a close prediction of the experimental results is imperative.This study proposes a grasshopper optimization algorithm(GOA)and salp swarm algorithm(SSA)to optimize artificial neural networks(ANNs)using the existing UBC experimental database.The performances of the proposed models are evaluated using various statistical indices.The obtained results are compared with the existing models.The proposed models outperformed the existing models.The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.展开更多
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 GLS...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.展开更多
To reduce the difficulty of obtaining the unconfined compressive strength(UCS) value of fiber-reinforced cemented paste backfill(CPB) and analyze the comprehensive impact of conventional and fiber variables on the com...To reduce the difficulty of obtaining the unconfined compressive strength(UCS) value of fiber-reinforced cemented paste backfill(CPB) and analyze the comprehensive impact of conventional and fiber variables on the compressive property, a new artificial intelligence model was proposed by combining a newly invented meta-heuristics algorithm(salp swarm algorithm, SSA) and extreme learning machine(ELM) technology. Aiming to test the reliability of that model, 720 UCS tests with different cement-to-tailing mass ratio, solid mass concentration, fiber content, fiber length, and curing time were carried out, and a strength evaluation database was collected. The obtained results show that the optimized SSA-ELM model can accurately predict the uniaxial compressive strength of the fiber-reinforced CPB, and the model performance of SSA-ELM model is better than ANN, SVR and ELM models. Variable sensitivity analysis indicates that fiber content and fiber length have a significant effect on the UCS of fiber-reinforced CPB.展开更多
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 t...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.展开更多
Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation tasks.Currently,the introduction o...Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation tasks.Currently,the introduction of deep learning(DL)and hardware technologies paves amethod in detecting the current traffic status,data offloading,and cyberattacks in MEC.This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC(AIMDO-SMEC)systems.The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks(SNN)to determine the traffic status in the MEC system.Also,an adaptive sampling cross entropy(ASCE)technique is utilized for data offloading in MEC systems.Moreover,the modified salp swarm algorithm(MSSA)with extreme gradient boosting(XGBoost)technique was implemented to identification and classification of cyberattack that exist in the MEC systems.For examining the enhanced outcomes of the AIMDO-SMEC technique,a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOSMEC technique with the minimal completion time of tasks(CTT)of 0.680.展开更多
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 n...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.展开更多
In this paper,an optimal nonlinear robust sliding mode control(ONRSMC)based on mixed H_(2)/H_(∞)linear matrix inequalities(LMIs)is designed for the excitation system in a“one machine-infinite bus system”(OMIBS)to e...In this paper,an optimal nonlinear robust sliding mode control(ONRSMC)based on mixed H_(2)/H_(∞)linear matrix inequalities(LMIs)is designed for the excitation system in a“one machine-infinite bus system”(OMIBS)to enhance system stability.Initially,the direct feedback linearization method is used to establish a mathematical model of the OMIBS incorporating uncertainties.ONRSMC is then designed for this model,employing the mixed H_(2)/H_(∞)LMIs.The chaos mapping-based adaptive salp swarm algorithm(CASSA)is introduced to fully optimize the parameters of the sliding mode control,ensuring optimal performance under a specified condition.CASSA demonstrates rapid convergence and reduced like-lihood of falling into local optima during optimization.Finally,ONRSMC is obtained through inverse transformation,exhibiting the advantages of simple structure,high reliability,and independence from the accuracy of system models.Four simulation scenarios are employed to validate the effectiveness and robustness of ONRSMC,including mechanical power variation,generator three-phase short circuit,transmission line short circuit,and generator parameter uncertainty.The results indicate that ONRSMC achieves optimal dynamic performance in various operating conditions,facilitating the stable operation of power systems following faults.展开更多
文摘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.
文摘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.
文摘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.
基金The authors would like to thank for the support from Taif University Researchers Supporting Project Number(TURSP-2020/239),Taif University,Taif,Saudi Arabia。
文摘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.
文摘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.
文摘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.
文摘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.
文摘When building geotechnical constructions like retaining walls and dams is of interest,one of the most important factors to consider is the soil’s shear strength parameters.This study makes an effort to propose a novel predictive model of shear strength.The study implements an extreme gradient boosting(XGBoost)technique coupled with a powerful optimization algorithm,the salp swarm algorithm(SSA),to predict the shear strength of various soils.To do this,a database consisting of 152 sets of data is prepared where the shear strength(τ)of the soil is considered as the model output and some soil index tests(e.g.,dry unit weight,water content,and plasticity index)are set as model inputs.Themodel is designed and tuned using both effective parameters of XGBoost and SSA,and themost accuratemodel is introduced in this study.Thepredictionperformanceof theSSA-XGBoostmodel is assessedbased on the coefficient of determination(R2)and variance account for(VAF).Overall,the obtained values of R^(2) and VAF(0.977 and 0.849)and(97.714%and 84.936%)for training and testing sets,respectively,confirm the workability of the developed model in forecasting the soil shear strength.To investigate the model generalization,the prediction performance of the model is tested for another 30 sets of data(validation data).The validation results(e.g.,R^(2) of 0.805)suggest the workability of the proposed model.Overall,findings suggest that when the shear strength of the soil cannot be determined directly,the proposed hybrid XGBoost-SSA model can be utilized to assess this parameter.
基金supported by the National Natural Science Foundation of China(52005231)Social Development Science and Technology Support Project of Changzhou(CE20215050)Jiangsu Province Graduate Student Practice Innovation Plan(SJCX21_1313,SJCX21_1314).
文摘This article proposes a novel method for maintaining the trajectory of an aerial manipulator by utilizing a fast nonsingular terminal sliding mode(FNTSM)manifold and a linear extended state observer(LESO).The developed controlmethod applies an FNTSMto ensure the tracking performance’s control accuracy,and an LESO to estimate the system’s unmodeled dynamics and external disturbances.Additionally,an improved salp swarm algorithm(ISSA)is employed to parameter tune the suggested controller by integrating the salp swarmtechnique with a cloud model.This approach also uses a model-free scheme to reduce the complexity of controller design without relying on complex and precise dynamics models.The simulation results show that the proposed controller outperforms linear active rejection disturbance control and PID controllers in terms of transient performance and resilience against lumped disturbances,and the ISSA can help the proposed controller find optimal control parameters.
基金supported by the Key R&D Program of Zhejiang(2022C03114)Zhejiang Provincial Natural Science Foundation of China(LJ19F020001,LZ22F020005)+1 种基金National Natural Science Foundation of China(62076185,U1809209)Guangdong Natural Science Foundation(2021A1515011994).
文摘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.
基金supported by the Key R&D Program of Zhejiang(2022C03114)Zhejiang Provincial Natural Science Foundation of China(LJ19F020001,LZ22F020005)+1 种基金National Natural Science Foundation of China(U1809209,71803136)Guangdong Natural Science Foundation(2021A1515011994).
文摘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.
基金supported by Korea Research Fellowship Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(Grant No.2019H1D3A1A01102993)the Inha University Research Grant(2022).
文摘Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions.The outcomes of the models are usually validated with the experimental results,but a large gap usually exists between them.Therefore,a model that can give a close prediction of the experimental results is imperative.This study proposes a grasshopper optimization algorithm(GOA)and salp swarm algorithm(SSA)to optimize artificial neural networks(ANNs)using the existing UBC experimental database.The performances of the proposed models are evaluated using various statistical indices.The obtained results are compared with the existing models.The proposed models outperformed the existing models.The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.
基金National Natural Science Foundation of China,Grant No.52375264.
文摘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.
基金financial supports from the National Natural Science Foundation of China (51874350,41807259)the National Key Research and Development Program of China (2017YFC0602902)+1 种基金the Fundamental Research Funds for the Central Universities of Central South University of China (2018zzts217)the Innovation-Driven Project of Central South University of China (2020CX040)。
文摘To reduce the difficulty of obtaining the unconfined compressive strength(UCS) value of fiber-reinforced cemented paste backfill(CPB) and analyze the comprehensive impact of conventional and fiber variables on the compressive property, a new artificial intelligence model was proposed by combining a newly invented meta-heuristics algorithm(salp swarm algorithm, SSA) and extreme learning machine(ELM) technology. Aiming to test the reliability of that model, 720 UCS tests with different cement-to-tailing mass ratio, solid mass concentration, fiber content, fiber length, and curing time were carried out, and a strength evaluation database was collected. The obtained results show that the optimized SSA-ELM model can accurately predict the uniaxial compressive strength of the fiber-reinforced CPB, and the model performance of SSA-ELM model is better than ANN, SVR and ELM models. Variable sensitivity analysis indicates that fiber content and fiber length have a significant effect on the UCS of fiber-reinforced CPB.
基金supported by the National Natural Science Foundation of China(62076185,U1809209)supported by Zhejiang Provincial Natural Science Foundation of China(LY21F020030)+1 种基金Wenzhou Major Scientific and Technological Innovation Project(ZY2019019)Wenzhou Science and Technology Bureau(2018ZG016)。
文摘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.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 2/209/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R77),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation tasks.Currently,the introduction of deep learning(DL)and hardware technologies paves amethod in detecting the current traffic status,data offloading,and cyberattacks in MEC.This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC(AIMDO-SMEC)systems.The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks(SNN)to determine the traffic status in the MEC system.Also,an adaptive sampling cross entropy(ASCE)technique is utilized for data offloading in MEC systems.Moreover,the modified salp swarm algorithm(MSSA)with extreme gradient boosting(XGBoost)technique was implemented to identification and classification of cyberattack that exist in the MEC systems.For examining the enhanced outcomes of the AIMDO-SMEC technique,a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOSMEC technique with the minimal completion time of tasks(CTT)of 0.680.
文摘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.
基金supported by the National Natural Science Foundation of China(No.51979204 and No.52009096)the Fundamental Research Funds for the Central Universities(No.2042022kf1022)the Hubei Provincial Natural Science Foundation of China(No.2022CFD165).
文摘In this paper,an optimal nonlinear robust sliding mode control(ONRSMC)based on mixed H_(2)/H_(∞)linear matrix inequalities(LMIs)is designed for the excitation system in a“one machine-infinite bus system”(OMIBS)to enhance system stability.Initially,the direct feedback linearization method is used to establish a mathematical model of the OMIBS incorporating uncertainties.ONRSMC is then designed for this model,employing the mixed H_(2)/H_(∞)LMIs.The chaos mapping-based adaptive salp swarm algorithm(CASSA)is introduced to fully optimize the parameters of the sliding mode control,ensuring optimal performance under a specified condition.CASSA demonstrates rapid convergence and reduced like-lihood of falling into local optima during optimization.Finally,ONRSMC is obtained through inverse transformation,exhibiting the advantages of simple structure,high reliability,and independence from the accuracy of system models.Four simulation scenarios are employed to validate the effectiveness and robustness of ONRSMC,including mechanical power variation,generator three-phase short circuit,transmission line short circuit,and generator parameter uncertainty.The results indicate that ONRSMC achieves optimal dynamic performance in various operating conditions,facilitating the stable operation of power systems following faults.
