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Training Neuro-Fuzzy by Using Meta-Heuristic Algorithms for MPPT
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作者 Ceren Baştemur Kaya Ebubekir Kaya Göksel Gökkuş 《Computer Systems Science & Engineering》 SCIE EI 2023年第4期69-84,共16页
It is one of the topics that have been studied extensively on maximum power point tracking(MPPT)recently.Traditional or soft computing methods are used for MPPT.Since soft computing approaches are more effective than ... It is one of the topics that have been studied extensively on maximum power point tracking(MPPT)recently.Traditional or soft computing methods are used for MPPT.Since soft computing approaches are more effective than traditional approaches,studies on MPPT have shifted in this direction.This study aims comparison of performance of seven meta-heuristic training algorithms in the neuro-fuzzy training for MPPT.The meta-heuristic training algorithms used are particle swarm optimization(PSO),harmony search(HS),cuckoo search(CS),artificial bee colony(ABC)algorithm,bee algorithm(BA),differential evolution(DE)and flower pollination algorithm(FPA).The antecedent and conclusion parameters of neuro-fuzzy are determined by these algorithms.The data of a 250 W photovoltaic(PV)is used in the applications.For effective MPPT,different neuro-fuzzy structures,different membership functions and different control parameter values are evaluated in detail.Related training algorithms are compared in terms of solution quality and convergence speed.The strengths and weaknesses of these algorithms are revealed.It is seen that the type and number of membership function,colony size,number of generations affect the solution quality and convergence speed of the training algorithms.As a result,it has been observed that CS and ABC algorithm are more effective than other algorithms in terms of solution quality and convergence in solving the related problem. 展开更多
关键词 OPTIMIZATION meta-heuristic algorithm NEURO-FUZZY MPPT photovoltaic system
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Damage Identification of A TLP Floating Wind Turbine by Meta-Heuristic Algorithms 被引量:4
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作者 M.M.Ettefagh 《China Ocean Engineering》 SCIE EI CSCD 2015年第6期891-902,共12页
Damage identification of the offshore floating wind turbine by vibration/dynamic signals is one of the important and new research fields in the Structural Health Monitoring(SHM). In this paper a new damage identific... Damage identification of the offshore floating wind turbine by vibration/dynamic signals is one of the important and new research fields in the Structural Health Monitoring(SHM). In this paper a new damage identification method is proposed based on meta-heuristic algorithms using the dynamic response of the TLP(Tension-Leg Platform) floating wind turbine structure. The Genetic Algorithms(GA), Artificial Immune System(AIS), Particle Swarm Optimization(PSO), and Artificial Bee Colony(ABC) are chosen for minimizing the object function, defined properly for damage identification purpose. In addition to studying the capability of mentioned algorithms in correctly identifying the damage, the effect of the response type on the results of identification is studied. Also, the results of proposed damage identification are investigated with considering possible uncertainties of the structure. Finally, for evaluating the proposed method in real condition, a 1/100 scaled experimental setup of TLP Floating Wind Turbine(TLPFWT) is provided in a laboratory scale and the proposed damage identification method is applied to the scaled turbine. 展开更多
关键词 floating wind turbine multi-body dynamics damage identification meta-heuristic algorithms OPTIMIZATION
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Prediction of seismic collapse risk of steel moment frame mid-rise structures by meta-heuristic algorithms 被引量:2
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作者 Fooad Karimi Ghaleh Jough Serhan Sensoy 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2016年第4期743-757,共15页
Different performance levels may be obtained for sideway collapse evaluation of steel moment frames depending on the evaluation procedure used to handle uncertainties. In this article, the process of representing mode... Different performance levels may be obtained for sideway collapse evaluation of steel moment frames depending on the evaluation procedure used to handle uncertainties. In this article, the process of representing modelling uncertainties, record to record (RTR) variations and cognitive uncertainties for moment resisting steel frames of various heights is discussed in detail. RTR uncertainty is used by incremental dynamic analysis (IDA), modelling uncertainties are considered through backbone curves and hysteresis loops of component, and cognitive uncertainty is presented in three levels of material quality. IDA is used to evaluate RTR uncertainty based on strong ground motion records selected by the k-means algorithm, which is favoured over Monte Carlo selection due to its time saving appeal. Analytical equations of the Response Surface Method are obtained through IDA results by the Cuckoo algorithm, which predicts the mean and standard deviation of the collapse fragility curve. The Takagi-Sugeno-Kang model is used to represent material quality based on the response surface coefficients. Finally, collapse fragility curves with the various sources of uncertainties mentioned are derived through a large number of material quality values and meta variables inferred by the Takagi-Sugeno-Kang fuzzy model based on response surface method coefficients. It is concluded that a better risk management strategy in countries where material quality control is weak, is to account for cognitive uncertainties in fragility curves and the mean annual frequency. 展开更多
关键词 modelling uncertainty cognitive uncertainty TSK model Cuckoo algorithm
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Domain Knowledge Used in Meta-Heuristic Algorithms for the Job-Shop Scheduling Problem:Review and Analysis
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作者 Lin Gui Xinyu Li +1 位作者 Qingfu Zhang Liang Gao 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第5期1368-1389,共22页
Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable timeframe.By combining domain knowledge of the specific optimization problem,the search efficiency and ... Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable timeframe.By combining domain knowledge of the specific optimization problem,the search efficiency and quality of meta-heuristic algorithms can be significantly improved,making it crucial to identify and summarize domain knowledge within the problem.In this paper,we summarize and analyze domain knowledge that can be applied to meta-heuristic algorithms in the job-shop scheduling problem(JSP).Firstly,this paper delves into the importance of domain knowledge in optimization algorithm design.After that,the development of different methods for the JSP are reviewed,and the domain knowledge in it for meta-heuristic algorithms is summarized and classified.Applications of this domain knowledge are analyzed,showing it is indispensable in ensuring the optimization performance of meta-heuristic algorithms.Finally,this paper analyzes the relationship among domain knowledge,optimization problems,and optimization algorithms,and points out the shortcomings of the existing research and puts forward research prospects.This paper comprehensively summarizes the domain knowledge in the JSP,and discusses the relationship between the optimization problems,optimization algorithms and domain knowledge,which provides a research direction for the metaheuristic algorithm design for solving the JSP in the future. 展开更多
关键词 domain knowledge job-shop scheduling problem meta-heuristic algorithm
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Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks 被引量:1
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作者 Shereen K.Refaay Samia A.Ali +2 位作者 Moumen T.El-Melegy Louai A.Maghrabi Hamdy H.El-Sayed 《Computers, Materials & Continua》 SCIE EI 2024年第1期873-897,共25页
The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few hav... The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station. 展开更多
关键词 Wireless sensor networks Rao algorithms OPTIMIZATION LEACH PEAGSIS
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Q-Learning-Assisted Meta-Heuristics for Scheduling Distributed Hybrid Flow Shop Problems
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作者 Qianyao Zhu Kaizhou Gao +2 位作者 Wuze Huang Zhenfang Ma Adam Slowik 《Computers, Materials & Continua》 SCIE EI 2024年第9期3573-3589,共17页
The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow S... The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted meta-heuristics.This work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned DHFSP.Second,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are proposed.According to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local space.Instead of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during iterations.Finally,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed algorithms.The experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random strategy.To verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving DHFSP.The Friedman test is executed on the results by five algorithms.It is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness. 展开更多
关键词 Distributed scheduling hybrid flow shop meta-heuristicS local search Q-LEARNING
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A comparative study on using meta-heuristic algorithms for road maintenance planning:Insights from field study in a developing country
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作者 Ali Gerami Matin Reza Vatani Nezafat Amir Golroo 《Journal of Traffic and Transportation Engineering(English Edition)》 2017年第5期477-486,共10页
Optimized road maintenance planning seeks for solutions that can minimize the life-cycle cost of a road network and concurrently maximize pavement condition. Aiming at pro- posing an optimal set of road maintenance so... Optimized road maintenance planning seeks for solutions that can minimize the life-cycle cost of a road network and concurrently maximize pavement condition. Aiming at pro- posing an optimal set of road maintenance solutions, robust meta-heuristic algorithms are used in research. Two main optimization techniques are applied including single-objective and multi-objective optimization. Genetic algorithms (GA), particle swarm optimization (PSO), and combination of genetic algorithm and particle swarm optimization (GAPSO) as single-objective techniques are used, while the non-domination sorting genetic algorithm II (NSGAII) and multi-objective particle swarm optimization (MOPSO) which are sufficient for solving computationally complex large-size optimization problems as multi-objective techniques are applied and compared. A real case study from the rural transportation network of Iran is employed to illustrate the sufficiency of the optimum algorithm. The formulation of the optimization model is carried out in such a way that a cost-effective maintenance strategy is reached by preserving the performance level of the road network at a desirable level. So, the objective functions are pavement performance maximization and maintenance cost minimization. It is concluded that multi-objective algorithms including non-domination sorting genetic algorithm II (NSGAII) and multi-objective particle swarm optimization performed better than the single objective algorithms due to the capability to balance between both objectives. And between multi-objective algorithms the NSGAII provides the optimum solution for the road maintenance planning. 展开更多
关键词 meta-heuristic algorithms Particle swarm optimization Non-domination sorting geneticalgorithm Multi-objective particle swarmoptimization
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Optimizing Connections:Applied Shortest Path Algorithms for MANETs
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作者 Ibrahim Alameri Jitka Komarkova +2 位作者 Tawfik Al-Hadhrami Abdulsamad Ebrahim Yahya Atef Gharbi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期787-807,共21页
This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of nodes.Themain objective was to del... This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of nodes.Themain objective was to delve into and refine the application of the Dijkstra’s algorithm in this context,a method conventionally esteemed for its efficiency in static networks.Thus,this paper has carried out a comparative theoretical analysis with the Bellman-Ford algorithm,considering adaptation to the dynamic network conditions that are typical for MANETs.This paper has shown through detailed algorithmic analysis that Dijkstra’s algorithm,when adapted for dynamic updates,yields a very workable solution to the problem of real-time routing in MANETs.The results indicate that with these changes,Dijkstra’s algorithm performs much better computationally and 30%better in routing optimization than Bellman-Ford when working with configurations of sparse networks.The theoretical framework adapted,with the adaptation of the Dijkstra’s algorithm for dynamically changing network topologies,is novel in this work and quite different from any traditional application.The adaptation should offer more efficient routing and less computational overhead,most apt in the limited resource environment of MANETs.Thus,from these findings,one may derive a conclusion that the proposed version of Dijkstra’s algorithm is the best and most feasible choice of the routing protocol for MANETs given all pertinent key performance and resource consumption indicators and further that the proposed method offers a marked improvement over traditional methods.This paper,therefore,operationalizes the theoretical model into practical scenarios and also further research with empirical simulations to understand more about its operational effectiveness. 展开更多
关键词 Dijkstra’s algorithm optimization complexity analysis shortest path first comparative algorithm analysis nondeterministic polynomial(NP)-complete
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Intelligent Solution System for Cloud Security Based on Equity Distribution:Model and Algorithms
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作者 Sarah Mustafa Eljack Mahdi Jemmali +3 位作者 Mohsen Denden Mutasim Al Sadig Abdullah M.Algashami Sadok Turki 《Computers, Materials & Continua》 SCIE EI 2024年第1期1461-1479,共19页
In the cloud environment,ensuring a high level of data security is in high demand.Data planning storage optimization is part of the whole security process in the cloud environment.It enables data security by avoiding ... In the cloud environment,ensuring a high level of data security is in high demand.Data planning storage optimization is part of the whole security process in the cloud environment.It enables data security by avoiding the risk of data loss and data overlapping.The development of data flow scheduling approaches in the cloud environment taking security parameters into account is insufficient.In our work,we propose a data scheduling model for the cloud environment.Themodel is made up of three parts that together help dispatch user data flow to the appropriate cloudVMs.The first component is the Collector Agent whichmust periodically collect information on the state of the network links.The second one is the monitoring agent which must then analyze,classify,and make a decision on the state of the link and finally transmit this information to the scheduler.The third one is the scheduler who must consider previous information to transfer user data,including fair distribution and reliable paths.It should be noted that each part of the proposedmodel requires the development of its algorithms.In this article,we are interested in the development of data transfer algorithms,including fairness distribution with the consideration of a stable link state.These algorithms are based on the grouping of transmitted files and the iterative method.The proposed algorithms showthe performances to obtain an approximate solution to the studied problem which is an NP-hard(Non-Polynomial solution)problem.The experimental results show that the best algorithm is the half-grouped minimum excluding(HME),with a percentage of 91.3%,an average deviation of 0.042,and an execution time of 0.001 s. 展开更多
关键词 Cyber-security cloud computing cloud security algorithms HEURISTICS
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Topological optimization of ballistic protective structures through genetic algorithms in a vulnerability-driven environment
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作者 Salvatore Annunziata Luca Lomazzi +1 位作者 Marco Giglio Andrea Manes 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第10期125-137,共13页
Reducing the vulnerability of a platform,i.e.,the risk of being affected by hostile objects,is of paramount importance in the design process of vehicles,especially aircraft.A simple and effective way to decrease vulne... Reducing the vulnerability of a platform,i.e.,the risk of being affected by hostile objects,is of paramount importance in the design process of vehicles,especially aircraft.A simple and effective way to decrease vulnerability is to introduce protective structures to intercept and possibly stop threats.However,this type of solution can lead to a significant increase in weight,affecting the performance of the aircraft.For this reason,it is crucial to study possible solutions that allow reducing the vulnerability of the aircraft while containing the increase in structural weight.One possible strategy is to optimize the topology of protective solutions to find the optimal balance between vulnerability and the weight of the added structures.Among the many optimization techniques available in the literature for this purpose,multiobjective genetic algorithms stand out as promising tools.In this context,this work proposes the use of a in-house software for vulnerability calculation to guide the process of topology optimization through multi-objective genetic algorithms,aiming to simultaneously minimize the weight of protective structures and vulnerability.In addition to the use of the in-house software,which itself represents a novelty in the field of topology optimization of structures,the method incorporates a custom mutation function within the genetic algorithm,specifically developed using a graph-based approach to ensure the continuity of the generated structures.The tool developed for this work is capable of generating protections with optimized layouts considering two different types of impacting objects,namely bullets and fragments from detonating objects.The software outputs a set of non-dominated solutions describing different topologies that the user can choose from. 展开更多
关键词 Topological optimization Protective structure Genetic algorithm SURVIVABILITY VULNERABILITY
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An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms
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作者 Asma Hassan Alshehri 《Computers, Materials & Continua》 SCIE EI 2024年第2期2767-2786,共20页
Online review platforms are becoming increasingly popular,encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services.Using Sybil accounts,bot farms,... Online review platforms are becoming increasingly popular,encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services.Using Sybil accounts,bot farms,and real account purchases,immoral actors demonize rivals and advertise their goods.Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years.The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones.This paper adopts a semi-supervised machine learning method to detect fake reviews on any website,among other things.Online reviews are classified using a semi-supervised approach(PU-learning)since there is a shortage of labeled data,and they are dynamic.Then,classification is performed using the machine learning techniques Support Vector Machine(SVM)and Nave Bayes.The performance of the suggested system has been compared with standard works,and experimental findings are assessed using several assessment metrics. 展开更多
关键词 SECURITY fake review semi-supervised learning ML algorithms review detection
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Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms
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作者 Junjie Zhao Diyuan Li +1 位作者 Jingtai Jiang Pingkuang Luo 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期275-304,共30页
Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining envir... Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines. 展开更多
关键词 Uniaxial compression strength strength prediction machine learning optimization algorithm
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Data-Driven Learning Control Algorithms for Unachievable Tracking Problems
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作者 Zeyi Zhang Hao Jiang +1 位作者 Dong Shen Samer S.Saab 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第1期205-218,共14页
For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to in... For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings. 展开更多
关键词 Data-driven algorithms incomplete information iterative learning control gradient information unachievable problems
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Landslide susceptibility mapping(LSM)based on different boosting and hyperparameter optimization algorithms:A case of Wanzhou District,China
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作者 Deliang Sun Jing Wang +2 位作者 Haijia Wen YueKai Ding Changlin Mi 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第8期3221-3232,共12页
Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)studies.However,these algorithms possess distinct computational strategies and hyperparameters,making it challen... Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)studies.However,these algorithms possess distinct computational strategies and hyperparameters,making it challenging to propose an ideal LSM model.To investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM,this study constructed a geospatial database comprising 12 conditioning factors,such as elevation,stratum,and annual average rainfall.The XGBoost(XGB),LightGBM(LGBM),and CatBoost(CB)algorithms were employed to construct the LSM model.Furthermore,the Bayesian optimization(BO),particle swarm optimization(PSO),and Hyperband optimization(HO)algorithms were applied to optimizing the LSM model.The boosting algorithms exhibited varying performances,with CB demonstrating the highest precision,followed by LGBM,and XGB showing poorer precision.Additionally,the hyperparameter optimization algorithms displayed different performances,with HO outperforming PSO and BO showing poorer performance.The HO-CB model achieved the highest precision,boasting an accuracy of 0.764,an F1-score of 0.777,an area under the curve(AUC)value of 0.837 for the training set,and an AUC value of 0.863 for the test set.The model was interpreted using SHapley Additive exPlanations(SHAP),revealing that slope,curvature,topographic wetness index(TWI),degree of relief,and elevation significantly influenced landslides in the study area.This study offers a scientific reference for LSM and disaster prevention research.This study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou District.It proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM models.However,limitations exist concerning the generalizability of the model and the data processing,which require further exploration in subsequent studies. 展开更多
关键词 Landslide susceptibility Hyperparameter optimization Boosting algorithms SHapley additive exPlanations(SHAP)
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Structure-preserving algorithms for guiding center dynamics based on the slow manifold of classical Pauli particle
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作者 张若涵 王正汹 +1 位作者 肖建元 王丰 《Plasma Science and Technology》 SCIE EI CAS CSCD 2024年第6期88-102,共15页
The classical Pauli particle(CPP) serves as a slow manifold, substituting the conventional guiding center dynamics. Based on the CPP, we utilize the averaged vector field(AVF) method in the computations of drift orbit... The classical Pauli particle(CPP) serves as a slow manifold, substituting the conventional guiding center dynamics. Based on the CPP, we utilize the averaged vector field(AVF) method in the computations of drift orbits. Demonstrating significantly higher efficiency, this advanced method is capable of accomplishing the simulation in less than one-third of the time of directly computing the guiding center motion. In contrast to the CPP-based Boris algorithm, this approach inherits the advantages of the AVF method, yielding stable trajectories even achieved with a tenfold time step and reducing the energy error by two orders of magnitude. By comparing these two CPP algorithms with the traditional RK4 method, the numerical results indicate a remarkable performance in terms of both the computational efficiency and error elimination. Moreover, we verify the properties of slow manifold integrators and successfully observe the bounce on both sides of the limiting slow manifold with deliberately chosen perturbed initial conditions. To evaluate the practical value of the methods, we conduct simulations in non-axisymmetric perturbation magnetic fields as part of the experiments,demonstrating that our CPP-based AVF method can handle simulations under complex magnetic field configurations with high accuracy, which the CPP-based Boris algorithm lacks. Through numerical experiments, we demonstrate that the CPP can replace guiding center dynamics in using energy-preserving algorithms for computations, providing a new, efficient, as well as stable approach for applying structure-preserving algorithms in plasma simulations. 展开更多
关键词 structure-preserving algorithm averaged vector field classical Pauli particle guiding center dynamics
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A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems
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作者 Elif Varol Altay Osman Altay Yusuf Ovik 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期1039-1094,共56页
Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as ... Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions. 展开更多
关键词 Metaheuristic optimization algorithms real-world engineering design problems multidisciplinary design optimization problems
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Systematic Review:Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms
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作者 Darakhshan Syed Ghulam Muhammad Safdar Rizvi 《Intelligent Automation & Soft Computing》 2024年第3期437-476,共40页
Cloud Computing has the ability to provide on-demand access to a shared resource pool.It has completely changed the way businesses are managed,implement applications,and provide services.The rise in popularity has led... Cloud Computing has the ability to provide on-demand access to a shared resource pool.It has completely changed the way businesses are managed,implement applications,and provide services.The rise in popularity has led to a significant increase in the user demand for services.However,in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization.This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms.Specifically,metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic.In a cloud-based context,this paper describes load balancing measurements,including the benefits and drawbacks associated with the selected load balancing techniques.It also summarizes the algorithms based on implementation,time complexity,adaptability,associated issue(s),and targeted QoS parameters.Additionally,the analysis evaluates the tools and instruments utilized in each investigated study.Moreover,comparative analysis among static,traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed.Finally,the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed. 展开更多
关键词 Cloud computing load balancing metaheuristic algorithm dynamic algorithm load balancer QOS
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Decoupling Algorithms for the Gravitational Wave Spacecraft
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作者 XueWang Weizhou Zhu +4 位作者 Zhao Cui Xingguang Qian Jinke Yang Jianjun Jia Yikun Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期325-337,共13页
The gravitational wave spacecraft is a complex multi-input multi-output dynamic system.The gravitational wave detection mission requires the spacecraft to achieve single spacecraft with two laser links and high-precis... The gravitational wave spacecraft is a complex multi-input multi-output dynamic system.The gravitational wave detection mission requires the spacecraft to achieve single spacecraft with two laser links and high-precision control.Establishing one spacecraftwith two laser links,compared to one spacecraft with a single laser link,requires an upgraded decoupling algorithmfor the link establishment.The decoupling algorithmwe designed reassigns the degrees of freedomand forces in the control loop to ensure sufficient degrees of freedomfor optical axis control.In addressing the distinct dynamic characteristics of different degrees of freedom,a transfer function compensation method is used in the decoupling process to further minimize motion coupling.The open-loop frequency response of the systemis obtained through simulation.The upgraded decoupling algorithms effectively reduce the open-loop frequency response by 30 dB.The transfer function compensation method efficiently suppresses the coupling of low-frequency noise. 展开更多
关键词 Gravitational waves spacecraft laser acquisition decoupling algorithms dynamical model optical axis control
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Quantification of the concrete freeze–thaw environment across the Qinghai–Tibet Plateau based on machine learning algorithms
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作者 QIN Yanhui MA Haoyuan +3 位作者 ZHANG Lele YIN Jinshuai ZHENG Xionghui LI Shuo 《Journal of Mountain Science》 SCIE CSCD 2024年第1期322-334,共13页
The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering ma... The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering managers should take into account. In this paper, we propose a more realistic method to calculate the number of concrete freeze–thaw cycles(NFTCs) on the QTP. The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7. Four machine learning methods, i.e., the random forest(RF) model, generalized boosting method(GBM), generalized linear model(GLM), and generalized additive model(GAM), are used to fit the NFTCs. The root mean square error(RMSE) values of the RF, GBM, GLM, and GAM are 32.3, 4.3, 247.9, and 161.3, respectively. The R^(2) values of the RF, GBM, GLM, and GAM are 0.93, 0.99, 0.48, and 0.66, respectively. The GBM method performs the best compared to the other three methods, which was shown by the results of RMSE and R^(2) values. The quantitative results from the GBM method indicate that the lowest, medium, and highest NFTC values are distributed in the northern, central, and southern parts of the QTP, respectively. The annual NFTCs in the QTP region are mainly concentrated at 160 and above, and the average NFTCs is 200 across the QTP. Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP. 展开更多
关键词 Freeze–thaw cycles Quantification Machine learning algorithms Qinghai–Tibet Plateau CONCRETE
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Accelerating Large-Scale Sorting through Parallel Algorithms
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作者 Yahya Alhabboub Fares Almutairi +3 位作者 Mohammed Safhi Yazan Alqahtani Adam Almeedani Yasir Alguwaifli 《Journal of Computer and Communications》 2024年第1期131-138,共8页
This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison ... This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison of their performance. This study investigates the efficacy of both techniques through the lens of array generation and pivot selection to manage datasets of varying sizes. This study meticulously documents the performance metrics, recording 16,499.2 milliseconds for the serial implementation and 16,339 milliseconds for the parallel implementation when sorting an array by using C++ chrono library. These results suggest that while the performance gains of the parallel approach over its serial counterpart are not immediately pronounced for smaller datasets, the benefits are expected to be more substantial as the dataset size increases. 展开更多
关键词 Sorting algorithm Quick Sort QuickSort Parallel Parallel algorithms
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