This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic ...This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.展开更多
Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is ext...Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system.The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy.First,we use the information gain and Fisher Score to sort the features extracted from signals.Then,we employ a multi-objective ranking method to evaluate these features and assign different importance to them.Features with high rankings have a large probability of being selected.Finally,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local traps.Using random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification techniques.The results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.展开更多
The development of anode materials with high rate capability and long charge-discharge plateau is the key to improve per-formance of lithium-ion capacitors(LICs).Herein,the porous graphitic carbon(PGC-1300)derived fro...The development of anode materials with high rate capability and long charge-discharge plateau is the key to improve per-formance of lithium-ion capacitors(LICs).Herein,the porous graphitic carbon(PGC-1300)derived from a new triply interpenetrated co-balt metal-organic framework(Co-MOF)was prepared through the facile and robust carbonization at 1300°C and washing by HCl solu-tion.The as-prepared PGC-1300 featured an optimized graphitization degree and porous framework,which not only contributes to high plateau capacity(105.0 mAh·g^(−1)below 0.2 V at 0.05 A·g^(−1)),but also supplies more convenient pathways for ions and increases the rate capability(128.5 mAh·g^(−1)at 3.2 A·g^(−1)).According to the kinetics analyses,it can be found that diffusion regulated surface induced capa-citive process and Li-ions intercalation process are coexisted for lithium-ion storage.Additionally,LIC PGC-1300//AC constructed with pre-lithiated PGC-1300 anode and activated carbon(AC)cathode exhibited an increased energy density of 102.8 Wh·kg^(−1),a power dens-ity of 6017.1 W·kg^(−1),together with the excellent cyclic stability(91.6%retention after 10000 cycles at 1.0 A·g^(−1)).展开更多
Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning thes...Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.展开更多
Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Indu...Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.展开更多
Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexi...Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.展开更多
As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance le...As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.展开更多
For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based ...For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based species(N—C)/NOx is optimized using the Non-dominated Sorting Genetic Algorithm II(NSGA-II)with 200 generations.The optimized mechanism(named as 937b)is validated against combustion characteristics of ammonia/methane(which is used to examine the accuracy of N—C interactions)and ammonia/diesel blends.The ignition delay times(IDTs),the laminar flame speeds and most of key intermediate species during the combustion of ammonia/methane blends can be accurately simulated by 937b under a wide range of conditions.As for ammonia/diesel blends with various diesel energy fractions,reasonable predictions on the IDTs under pressures from 1.0 MPa to5.0 MPa as well as the laminar flame speeds are also achieved by 937b.In particular,with regard to the IDT simulations of ammonia/diesel blends,937b makes progress in both aspects of overall accuracy and computational efficiency,compared to a detailed ammonia/diesel mechanism.Further kinetic analysis reveals that the reaction pathway of ammonia during the combustion of ammonia/diesel blend mainly differs in the tendencies of oxygen additions to NH_2 and NH with different equivalence ratios.展开更多
Feature Subset Selection(FSS)is an NP-hard problem to remove redundant and irrelevant features particularly from medical data,and it can be effectively addressed by metaheuristic algorithms.However,existing binary ver...Feature Subset Selection(FSS)is an NP-hard problem to remove redundant and irrelevant features particularly from medical data,and it can be effectively addressed by metaheuristic algorithms.However,existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method,resulting in suboptimal solutions that hinder diagnosis and prediction accuracy.This paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm(IBQANA)for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic algorithms.The proposed IBQANA’s contributions include the Hybrid Binary Operator(HBO)and the Distance-based Binary Search Strategy(DBSS).HBO is designed to convert continuous values into binary solutions,even for values outside the[0,1]range,ensuring accurate binary mapping.On the other hand,DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates convergence.By combining exploration and exploitation phases based on an adaptive probability function,DBSS effectively avoids local optima.The effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets,with feature numbers ranging from 8 to 10,509.IBQANA's effectiveness is evaluated regarding the accuracy,fitness,and selected features and compared with seven binary metaheuristic algorithms.Furthermore,IBQANA is utilized to detect COVID-19.The results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical datasets.The proposed method presents a promising solution to the FSS problem in medical data preprocessing.展开更多
BACKGROUND A cure for Helicobacter pylori(H.pylori)remains a problem of global concern.The prevalence of antimicrobial resistance is widely rising and becoming a challenging issue worldwide.Optimizing sequential thera...BACKGROUND A cure for Helicobacter pylori(H.pylori)remains a problem of global concern.The prevalence of antimicrobial resistance is widely rising and becoming a challenging issue worldwide.Optimizing sequential therapy seems to be one of the most attractive strategies in terms of efficacy,tolerability and cost.The most common sequential therapy consists of a dual therapy[proton-pump inhibitors(PPIs)and amoxicillin]for the first period(5 to 7 d),followed by a triple therapy for the second period(PPI,clarithromycin and metronidazole).PPIs play a key role in maintaining a gastric pH at a level that allows an optimal efficacy of antibiotics,hence the idea of using new generation molecules.This open-label prospective study randomized 328 patients with confirmed H.pylori infection into three groups(1:1:1):The first group received quadruple therapy consisting of twice-daily(bid)omeprazole 20 mg,amoxicillin 1 g,clarith-romycin 500 mg and metronidazole 500 mg for 10 d(QT-10),the second group received a 14 d quadruple therapy following the same regimen(QT-14),and the third group received an optimized sequential therapy consisting of bid rabe-prazole 20 mg plus amoxicillin 1 g for 7 d,followed by bid rabeprazole 20 mg,clarithromycin 500 mg and metronidazole 500 mg for the next 7 d(OST-14).AEs were recorded throughout the study,and the H.pylori eradication rate was determined 4 to 6 wk after the end of treatment,using the 13C urea breath test.RESULTS In the intention-to-treat and per-protocol analysis,the eradication rate was higher in the OST-14 group compared to the QT-10 group:(93.5%,85.5%P=0.04)and(96.2%,89.5%P=0.03)respectively.However,there was no statist-ically significant difference in eradication rates between the OST-14 and QT-14 groups:(93.5%,91.8%P=0.34)and(96.2%,94.4%P=0.35),respectively.The overall incidence of AEs was significantly lower in the OST-14 group(P=0.01).Furthermore,OST-14 was the most cost-effective among the three groups.CONCLUSION The optimized 14-d sequential therapy is a safe and effective alternative.Its eradication rate is comparable to that of the 14-d concomitant therapy while causing fewer AEs and allowing a gain in terms of cost.展开更多
While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),...While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.展开更多
With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred...With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.展开更多
The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its p...The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its performance by implementing the algorithm on GPUs. In the previous research work, “Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm using GPUs,” the authors demonstrated first the Bundle Adjustment algorithmic performance improvement by reducing the mean square error using an additional radial distorting parameter and explicitly computed analytical derivatives and reducing the computational burden of the Bundle Adjustment algorithm using GPUs. The naïve implementation of the CUDA code, a speedup of 10× for the largest dataset of 13,678 cameras, 4,455,747 points, and 28,975,571 projections was achieved. In this paper, we present the optimization of the Bundle Adjustment algorithm CUDA code on GPUs to achieve higher speedup. We propose a new data memory layout for the parameters in the Bundle Adjustment algorithm, resulting in contiguous memory access. We demonstrate that it improves the memory throughput on the GPUs, thereby improving the overall performance. We also demonstrate an increase in the computational throughput of the algorithm by optimizing the CUDA kernels to utilize the GPU resources effectively. A comparative performance study of explicitly computing an algorithm parameter versus using the Jacobians instead is presented. In the previous work, the Bundle Adjustment algorithm failed to converge for certain datasets due to several block matrices of the cameras in the augmented normal equation, resulting in rank-deficient matrices. In this work, we identify the cameras that cause rank-deficient matrices and preprocess the datasets to ensure the convergence of the BA algorithm. Our optimized CUDA implementation achieves convergence of the Bundle Adjustment algorithm in around 22 seconds for the largest dataset compared to 654 seconds for the sequential implementation, resulting in a speedup of 30×. Our optimized CUDA implementation presented in this paper has achieved a 3× speedup for the largest dataset compared to the previous naïve CUDA implementation.展开更多
With the rapid development of communication technology,the problem of antenna array optimization plays a crucial role.Among many types of antennas,line antenna arrays(LAA)are the most commonly applied,but the side lob...With the rapid development of communication technology,the problem of antenna array optimization plays a crucial role.Among many types of antennas,line antenna arrays(LAA)are the most commonly applied,but the side lobe level(SLL)reduction is still a challenging problem.In the radiation process of the linear antenna array,the high side lobe level will interfere with the intensity of the antenna target radiation direction.Many conventional methods are ineffective in obtaining the maximumside lobe level in synthesis,and this paper proposed a quantum equilibrium optimizer(QEO)algorithm for line antenna arrays.Firstly,the linear antenna array model consists of an array element arrangement.Array factor(AF)can be expressed as the combination of array excitation amplitude and position in array space.Then,inspired by the powerful computing power of quantum computing,an improved quantum equilibrium optimizer combining quantum coding and quantum rotation gate strategy is proposed.Finally,the proposed quantum equilibrium optimizer is used to optimize the excitation amplitude of the array elements in the linear antenna array model by numerical simulation to minimize the interference of the side lobe level to the main lobe radiation.Six differentmetaheuristic algorithms are used to optimize the excitation amplitude in three different arrays of line antenna arrays,the experimental results indicated that the quantum equilibrium optimizer is more advantageous in obtaining the maximum side lobe level reduction.Compared with other metaheuristic optimization algorithms,the quantum equilibrium optimizer has advantages in terms of convergence speed and accuracy.展开更多
In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to ...In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation.In this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system.We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism.We also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters effectively.We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark functions.CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm.For the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection analyses.We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective.The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds.Lastly,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases.We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.展开更多
Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation.Recently,deep learning(DL)has emerged as a promising tool for pipeline leakage detection(PLD).However,most existing...Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation.Recently,deep learning(DL)has emerged as a promising tool for pipeline leakage detection(PLD).However,most existing DL methods have difficulty in achieving good performance in identifying leakage types due to the complex time dynamics of pipeline data.On the other hand,the initial parameter selection in the detection model is generally random,which may lead to unstable recognition performance.For this reason,a hybrid DL framework referred to as parameter-optimized recurrent attention network(PRAN)is presented in this paper to improve the accuracy of PLD.First,a parameter-optimized long short-term memory(LSTM)network is introduced to extract effective and robust features,which exploits a particle swarm optimization(PSO)algorithm with cross-entropy fitness function to search for globally optimal parameters.With this framework,the learning representation capability of the model is improved and the convergence rate is accelerated.Moreover,an anomaly-attention mechanism(AM)is proposed to discover class discriminative information by weighting the hidden states,which contributes to amplifying the normalabnormal distinguishable discrepancy,further improving the accuracy of PLD.After that,the proposed PRAN not only implements the adaptive optimization of network parameters,but also enlarges the contribution of normal-abnormal discrepancy,thereby overcoming the drawbacks of instability and poor generalization.Finally,the experimental results demonstrate the effectiveness and superiority of the proposed PRAN for PLD.展开更多
Hybrid metaheuristic algorithms play a prominent role in improving algorithms' searchability by combining each algorithm's advantages and minimizing any substantial shortcomings. The Quantum-based Avian Naviga...Hybrid metaheuristic algorithms play a prominent role in improving algorithms' searchability by combining each algorithm's advantages and minimizing any substantial shortcomings. The Quantum-based Avian Navigation Optimizer Algorithm (QANA) is a recent metaheuristic algorithm inspired by the navigation behavior of migratory birds. Different experimental results show that QANA is a competitive and applicable algorithm in different optimization fields. However, it suffers from shortcomings such as low solution quality and premature convergence when tackling some complex problems. Therefore, instead of proposing a new algorithm to solve these weaknesses, we use the advantages of the bonobo optimizer to improve global search capability and mitigate premature convergence of the original QANA. The effectiveness of the proposed Hybrid Quantum-based Avian Navigation Optimizer Algorithm (HQANA) is assessed on 29 test functions of the CEC 2018 benchmark test suite with different dimensions, 30, 50, and 100. The results are then statistically investigated by the Friedman test and compared with the results of eight well-known optimization algorithms, including PSO, KH, GWO, WOA, CSA, HOA, BO, and QANA. Ultimately, five constrained engineering optimization problems from the latest test suite, CEC 2020 are used to assess the applicability of HQANA to solve complex real-world engineering optimization problems. The experimental and statistical findings prove that the proposed HQANA algorithm is superior to the comparative algorithms.展开更多
Detecting communities in real and complex networks is a highly contested topic in network analysis.Although many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively ...Detecting communities in real and complex networks is a highly contested topic in network analysis.Although many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively fulfill large-scale and real-world networks.Thus,this paper presents a new discrete version of the Improved Grey Wolf Optimizer(I-GWO)algorithm named DI-GWOCD for effectively detecting communities of different networks.In the proposed DI-GWOCD algorithm,I-GWO is first armed using a local search strategy to discover and improve nodes placed in improper communities and increase its ability to search for a better solution.Then a novel Binary Distance Vector(BDV)is introduced to calculate the wolves’distances and adapt I-GWO for solving the discrete community detection problem.The performance of the proposed DI-GWOCD was evaluated in terms of modularity,NMI,and the number of detected communities conducted by some well-known real-world network datasets.The experimental results were compared with the state-of-the-art algorithms and statistically analyzed using the Friedman and Wilcoxon tests.The comparison and the statistical analysis show that the proposed DI-GWOCD can detect the communities with higher quality than other comparative algorithms.展开更多
This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering applications.As a newly mooted meta-heuristic algorithm,snake optimizer(SO)mathematically models the matin...This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering applications.As a newly mooted meta-heuristic algorithm,snake optimizer(SO)mathematically models the mating characteristics of snakes to find the optimal solution.SO has a simple structure and offers a delicate balance between exploitation and exploration.However,it also has some shortcomings to be improved.The proposed BEESO consequently aims to lighten the issues of lack of population diversity,convergence slowness,and the tendency to be stuck in local optima in SO.The presentation of Bi-Directional Search(BDS)is to approach the global optimal value along the direction guided by the best and the worst individuals,which makes the convergence speed faster.The increase in population diversity in BEESO benefits from Modified Evolutionary Population Dynamics(MEPD),and the replacement of poorer quality individuals improves population quality.The Elite Opposition-Based Learning(EOBL)provides improved local exploitation ability of BEESO by utilizing solid solutions with good performance.The performance of BEESO is illustrated by comparing its experimental results with several algorithms on benchmark functions and engineering designs.Additionally,the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank sum tests.The findings show that these introduced strategies provide some improvements in the performance of SO,and the accuracy and stability of the optimization results provided by the proposed BEESO are competitive among all algorithms.To conclude,the proposed BEESO offers a good alternative to solving optimization issues.展开更多
The aviation industry is one of the most competitive markets. Themost common approach for airline service providers is to improve passengersatisfaction. Passenger satisfaction in the aviation industry occurs whenpasse...The aviation industry is one of the most competitive markets. Themost common approach for airline service providers is to improve passengersatisfaction. Passenger satisfaction in the aviation industry occurs whenpassengers’ expectations are met during flights. Airline service quality iscritical in attracting new passengers and retaining existing ones. It is crucialto identify passengers’ pain points and enhance their satisfaction with theservices offered. The airlines used a variety of techniques to improve servicequality. They used data analysis approaches to analyze the passenger pointdata. These solutions have focused simply on surveys;consequently, deeplearningapproaches have received insufficient attention. In this study, deepneural networks with the adaptive moment estimation Adam optimizationalgorithm were applied to enhance classification performance. In previousstudies, the quality of the dataset has been ignored. The proposed approachwas applied to the airline passenger satisfaction dataset from the Kagglerepository. It was validated by applying artificial neural networks (ANNs),random forests, and support vector machine techniques to the same dataset. Itwas compared with other research papers that used the same dataset and had asimilar problem. The experimental results showed that the proposed approachoutperformed previous studies. It has achieved an accuracy of 99.3%.展开更多
基金supported by the National Research Foundation of Korea(NRF)Grant funded by the Korea government(MSIT)(No.RS-2023-00218176)the Soonchunhyang University Research Fund.
文摘This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.
文摘Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system.The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy.First,we use the information gain and Fisher Score to sort the features extracted from signals.Then,we employ a multi-objective ranking method to evaluate these features and assign different importance to them.Features with high rankings have a large probability of being selected.Finally,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local traps.Using random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification techniques.The results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.
基金the National Natural Science Foundation of China(No.52004179)the Natural Nat-ural Science Foundation of Guangxi Province,China(No.2020GXNSFAA159015)Shanxi Water and Wood New Carbon Materials Technology Co.,Ltd.,China,and Shanxi Wote Haimer New Materials Technology Co.,Ltd,China.
文摘The development of anode materials with high rate capability and long charge-discharge plateau is the key to improve per-formance of lithium-ion capacitors(LICs).Herein,the porous graphitic carbon(PGC-1300)derived from a new triply interpenetrated co-balt metal-organic framework(Co-MOF)was prepared through the facile and robust carbonization at 1300°C and washing by HCl solu-tion.The as-prepared PGC-1300 featured an optimized graphitization degree and porous framework,which not only contributes to high plateau capacity(105.0 mAh·g^(−1)below 0.2 V at 0.05 A·g^(−1)),but also supplies more convenient pathways for ions and increases the rate capability(128.5 mAh·g^(−1)at 3.2 A·g^(−1)).According to the kinetics analyses,it can be found that diffusion regulated surface induced capa-citive process and Li-ions intercalation process are coexisted for lithium-ion storage.Additionally,LIC PGC-1300//AC constructed with pre-lithiated PGC-1300 anode and activated carbon(AC)cathode exhibited an increased energy density of 102.8 Wh·kg^(−1),a power dens-ity of 6017.1 W·kg^(−1),together with the excellent cyclic stability(91.6%retention after 10000 cycles at 1.0 A·g^(−1)).
文摘Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia has funded this project under Grant No.(G:651-135-1443).
文摘Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.
基金the National Natural Science Foundation of China(Grant 42177164)the Distinguished Youth Science Foundation of Hunan Province of China(2022JJ10073).
文摘As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.
基金the National Natural Science Foundation of China(project code:52202470)Jilin Province Natural Science Foundation(project codes:20220101205JC,20220101212JC)+2 种基金Jilin Province Specific Project of Industrial Technology Research&Development(project code:2020C025-2)2021 Interdisciplinary Integration and Innovation Project of Jilin University(project code:XJRCYB07)Free Exploration Project of Changsha Automotive Innovation Research Institute of Jilin University(project code:CAIRIZT20220202)。
文摘For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based species(N—C)/NOx is optimized using the Non-dominated Sorting Genetic Algorithm II(NSGA-II)with 200 generations.The optimized mechanism(named as 937b)is validated against combustion characteristics of ammonia/methane(which is used to examine the accuracy of N—C interactions)and ammonia/diesel blends.The ignition delay times(IDTs),the laminar flame speeds and most of key intermediate species during the combustion of ammonia/methane blends can be accurately simulated by 937b under a wide range of conditions.As for ammonia/diesel blends with various diesel energy fractions,reasonable predictions on the IDTs under pressures from 1.0 MPa to5.0 MPa as well as the laminar flame speeds are also achieved by 937b.In particular,with regard to the IDT simulations of ammonia/diesel blends,937b makes progress in both aspects of overall accuracy and computational efficiency,compared to a detailed ammonia/diesel mechanism.Further kinetic analysis reveals that the reaction pathway of ammonia during the combustion of ammonia/diesel blend mainly differs in the tendencies of oxygen additions to NH_2 and NH with different equivalence ratios.
文摘Feature Subset Selection(FSS)is an NP-hard problem to remove redundant and irrelevant features particularly from medical data,and it can be effectively addressed by metaheuristic algorithms.However,existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method,resulting in suboptimal solutions that hinder diagnosis and prediction accuracy.This paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm(IBQANA)for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic algorithms.The proposed IBQANA’s contributions include the Hybrid Binary Operator(HBO)and the Distance-based Binary Search Strategy(DBSS).HBO is designed to convert continuous values into binary solutions,even for values outside the[0,1]range,ensuring accurate binary mapping.On the other hand,DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates convergence.By combining exploration and exploitation phases based on an adaptive probability function,DBSS effectively avoids local optima.The effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets,with feature numbers ranging from 8 to 10,509.IBQANA's effectiveness is evaluated regarding the accuracy,fitness,and selected features and compared with seven binary metaheuristic algorithms.Furthermore,IBQANA is utilized to detect COVID-19.The results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical datasets.The proposed method presents a promising solution to the FSS problem in medical data preprocessing.
文摘BACKGROUND A cure for Helicobacter pylori(H.pylori)remains a problem of global concern.The prevalence of antimicrobial resistance is widely rising and becoming a challenging issue worldwide.Optimizing sequential therapy seems to be one of the most attractive strategies in terms of efficacy,tolerability and cost.The most common sequential therapy consists of a dual therapy[proton-pump inhibitors(PPIs)and amoxicillin]for the first period(5 to 7 d),followed by a triple therapy for the second period(PPI,clarithromycin and metronidazole).PPIs play a key role in maintaining a gastric pH at a level that allows an optimal efficacy of antibiotics,hence the idea of using new generation molecules.This open-label prospective study randomized 328 patients with confirmed H.pylori infection into three groups(1:1:1):The first group received quadruple therapy consisting of twice-daily(bid)omeprazole 20 mg,amoxicillin 1 g,clarith-romycin 500 mg and metronidazole 500 mg for 10 d(QT-10),the second group received a 14 d quadruple therapy following the same regimen(QT-14),and the third group received an optimized sequential therapy consisting of bid rabe-prazole 20 mg plus amoxicillin 1 g for 7 d,followed by bid rabeprazole 20 mg,clarithromycin 500 mg and metronidazole 500 mg for the next 7 d(OST-14).AEs were recorded throughout the study,and the H.pylori eradication rate was determined 4 to 6 wk after the end of treatment,using the 13C urea breath test.RESULTS In the intention-to-treat and per-protocol analysis,the eradication rate was higher in the OST-14 group compared to the QT-10 group:(93.5%,85.5%P=0.04)and(96.2%,89.5%P=0.03)respectively.However,there was no statist-ically significant difference in eradication rates between the OST-14 and QT-14 groups:(93.5%,91.8%P=0.34)and(96.2%,94.4%P=0.35),respectively.The overall incidence of AEs was significantly lower in the OST-14 group(P=0.01).Furthermore,OST-14 was the most cost-effective among the three groups.CONCLUSION The optimized 14-d sequential therapy is a safe and effective alternative.Its eradication rate is comparable to that of the 14-d concomitant therapy while causing fewer AEs and allowing a gain in terms of cost.
文摘While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.
基金supported by the National Science and Technology Innovation 2030 Next-Generation Artifical Intelligence Major Project(2018AAA0101801)the National Natural Science Foundation of China(72271188)。
文摘With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.
文摘The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its performance by implementing the algorithm on GPUs. In the previous research work, “Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm using GPUs,” the authors demonstrated first the Bundle Adjustment algorithmic performance improvement by reducing the mean square error using an additional radial distorting parameter and explicitly computed analytical derivatives and reducing the computational burden of the Bundle Adjustment algorithm using GPUs. The naïve implementation of the CUDA code, a speedup of 10× for the largest dataset of 13,678 cameras, 4,455,747 points, and 28,975,571 projections was achieved. In this paper, we present the optimization of the Bundle Adjustment algorithm CUDA code on GPUs to achieve higher speedup. We propose a new data memory layout for the parameters in the Bundle Adjustment algorithm, resulting in contiguous memory access. We demonstrate that it improves the memory throughput on the GPUs, thereby improving the overall performance. We also demonstrate an increase in the computational throughput of the algorithm by optimizing the CUDA kernels to utilize the GPU resources effectively. A comparative performance study of explicitly computing an algorithm parameter versus using the Jacobians instead is presented. In the previous work, the Bundle Adjustment algorithm failed to converge for certain datasets due to several block matrices of the cameras in the augmented normal equation, resulting in rank-deficient matrices. In this work, we identify the cameras that cause rank-deficient matrices and preprocess the datasets to ensure the convergence of the BA algorithm. Our optimized CUDA implementation achieves convergence of the Bundle Adjustment algorithm in around 22 seconds for the largest dataset compared to 654 seconds for the sequential implementation, resulting in a speedup of 30×. Our optimized CUDA implementation presented in this paper has achieved a 3× speedup for the largest dataset compared to the previous naïve CUDA implementation.
基金supported by the National Science Foundation of China under Grant No.62066005Project of the Guangxi Science and Technology under Grant No.AD21196006.
文摘With the rapid development of communication technology,the problem of antenna array optimization plays a crucial role.Among many types of antennas,line antenna arrays(LAA)are the most commonly applied,but the side lobe level(SLL)reduction is still a challenging problem.In the radiation process of the linear antenna array,the high side lobe level will interfere with the intensity of the antenna target radiation direction.Many conventional methods are ineffective in obtaining the maximumside lobe level in synthesis,and this paper proposed a quantum equilibrium optimizer(QEO)algorithm for line antenna arrays.Firstly,the linear antenna array model consists of an array element arrangement.Array factor(AF)can be expressed as the combination of array excitation amplitude and position in array space.Then,inspired by the powerful computing power of quantum computing,an improved quantum equilibrium optimizer combining quantum coding and quantum rotation gate strategy is proposed.Finally,the proposed quantum equilibrium optimizer is used to optimize the excitation amplitude of the array elements in the linear antenna array model by numerical simulation to minimize the interference of the side lobe level to the main lobe radiation.Six differentmetaheuristic algorithms are used to optimize the excitation amplitude in three different arrays of line antenna arrays,the experimental results indicated that the quantum equilibrium optimizer is more advantageous in obtaining the maximum side lobe level reduction.Compared with other metaheuristic optimization algorithms,the quantum equilibrium optimizer has advantages in terms of convergence speed and accuracy.
文摘In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation.In this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system.We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism.We also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters effectively.We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark functions.CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm.For the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection analyses.We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective.The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds.Lastly,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases.We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.
基金This work was supported in part by the National Natural Science Foundation of China(U21A2019,61873058),Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105)the Alexander von Humboldt Foundation of Germany.
文摘Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation.Recently,deep learning(DL)has emerged as a promising tool for pipeline leakage detection(PLD).However,most existing DL methods have difficulty in achieving good performance in identifying leakage types due to the complex time dynamics of pipeline data.On the other hand,the initial parameter selection in the detection model is generally random,which may lead to unstable recognition performance.For this reason,a hybrid DL framework referred to as parameter-optimized recurrent attention network(PRAN)is presented in this paper to improve the accuracy of PLD.First,a parameter-optimized long short-term memory(LSTM)network is introduced to extract effective and robust features,which exploits a particle swarm optimization(PSO)algorithm with cross-entropy fitness function to search for globally optimal parameters.With this framework,the learning representation capability of the model is improved and the convergence rate is accelerated.Moreover,an anomaly-attention mechanism(AM)is proposed to discover class discriminative information by weighting the hidden states,which contributes to amplifying the normalabnormal distinguishable discrepancy,further improving the accuracy of PLD.After that,the proposed PRAN not only implements the adaptive optimization of network parameters,but also enlarges the contribution of normal-abnormal discrepancy,thereby overcoming the drawbacks of instability and poor generalization.Finally,the experimental results demonstrate the effectiveness and superiority of the proposed PRAN for PLD.
文摘Hybrid metaheuristic algorithms play a prominent role in improving algorithms' searchability by combining each algorithm's advantages and minimizing any substantial shortcomings. The Quantum-based Avian Navigation Optimizer Algorithm (QANA) is a recent metaheuristic algorithm inspired by the navigation behavior of migratory birds. Different experimental results show that QANA is a competitive and applicable algorithm in different optimization fields. However, it suffers from shortcomings such as low solution quality and premature convergence when tackling some complex problems. Therefore, instead of proposing a new algorithm to solve these weaknesses, we use the advantages of the bonobo optimizer to improve global search capability and mitigate premature convergence of the original QANA. The effectiveness of the proposed Hybrid Quantum-based Avian Navigation Optimizer Algorithm (HQANA) is assessed on 29 test functions of the CEC 2018 benchmark test suite with different dimensions, 30, 50, and 100. The results are then statistically investigated by the Friedman test and compared with the results of eight well-known optimization algorithms, including PSO, KH, GWO, WOA, CSA, HOA, BO, and QANA. Ultimately, five constrained engineering optimization problems from the latest test suite, CEC 2020 are used to assess the applicability of HQANA to solve complex real-world engineering optimization problems. The experimental and statistical findings prove that the proposed HQANA algorithm is superior to the comparative algorithms.
文摘Detecting communities in real and complex networks is a highly contested topic in network analysis.Although many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively fulfill large-scale and real-world networks.Thus,this paper presents a new discrete version of the Improved Grey Wolf Optimizer(I-GWO)algorithm named DI-GWOCD for effectively detecting communities of different networks.In the proposed DI-GWOCD algorithm,I-GWO is first armed using a local search strategy to discover and improve nodes placed in improper communities and increase its ability to search for a better solution.Then a novel Binary Distance Vector(BDV)is introduced to calculate the wolves’distances and adapt I-GWO for solving the discrete community detection problem.The performance of the proposed DI-GWOCD was evaluated in terms of modularity,NMI,and the number of detected communities conducted by some well-known real-world network datasets.The experimental results were compared with the state-of-the-art algorithms and statistically analyzed using the Friedman and Wilcoxon tests.The comparison and the statistical analysis show that the proposed DI-GWOCD can detect the communities with higher quality than other comparative algorithms.
基金supported by the National Natural Science Foundation of China (Grant No.51875454).
文摘This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering applications.As a newly mooted meta-heuristic algorithm,snake optimizer(SO)mathematically models the mating characteristics of snakes to find the optimal solution.SO has a simple structure and offers a delicate balance between exploitation and exploration.However,it also has some shortcomings to be improved.The proposed BEESO consequently aims to lighten the issues of lack of population diversity,convergence slowness,and the tendency to be stuck in local optima in SO.The presentation of Bi-Directional Search(BDS)is to approach the global optimal value along the direction guided by the best and the worst individuals,which makes the convergence speed faster.The increase in population diversity in BEESO benefits from Modified Evolutionary Population Dynamics(MEPD),and the replacement of poorer quality individuals improves population quality.The Elite Opposition-Based Learning(EOBL)provides improved local exploitation ability of BEESO by utilizing solid solutions with good performance.The performance of BEESO is illustrated by comparing its experimental results with several algorithms on benchmark functions and engineering designs.Additionally,the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank sum tests.The findings show that these introduced strategies provide some improvements in the performance of SO,and the accuracy and stability of the optimization results provided by the proposed BEESO are competitive among all algorithms.To conclude,the proposed BEESO offers a good alternative to solving optimization issues.
文摘The aviation industry is one of the most competitive markets. Themost common approach for airline service providers is to improve passengersatisfaction. Passenger satisfaction in the aviation industry occurs whenpassengers’ expectations are met during flights. Airline service quality iscritical in attracting new passengers and retaining existing ones. It is crucialto identify passengers’ pain points and enhance their satisfaction with theservices offered. The airlines used a variety of techniques to improve servicequality. They used data analysis approaches to analyze the passenger pointdata. These solutions have focused simply on surveys;consequently, deeplearningapproaches have received insufficient attention. In this study, deepneural networks with the adaptive moment estimation Adam optimizationalgorithm were applied to enhance classification performance. In previousstudies, the quality of the dataset has been ignored. The proposed approachwas applied to the airline passenger satisfaction dataset from the Kagglerepository. It was validated by applying artificial neural networks (ANNs),random forests, and support vector machine techniques to the same dataset. Itwas compared with other research papers that used the same dataset and had asimilar problem. The experimental results showed that the proposed approachoutperformed previous studies. It has achieved an accuracy of 99.3%.
