The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and u...The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and uncertainties during optimization remains a formidable challenge. In this study, a strategy combining interpretable machine learning with metaheuristic optimization algorithms is employed to optimize the reaction process. First, experimental data from a biodiesel production process are collected to establish a database. These data are then used to construct a predictive model based on artificial neural network (ANN) models. Subsequently, interpretable machine learning techniques are applied for quantitative analysis and verification of the model. Finally, four metaheuristic optimization algorithms are coupled with the ANN model to achieve the desired optimization. The research results show that the methanol: palm fatty acid distillate (PFAD) molar ratio contributes the most to the reaction outcome, accounting for 41%. The ANN-simulated annealing (SA) hybrid method is more suitable for this optimization, and the optimal process parameters are a catalyst concentration of 3.00% (mass), a methanol: PFAD molar ratio of 8.67, and a reaction time of 30 min. This study provides deeper insights into reaction process optimization, which will facilitate future applications in various reaction optimization processes.展开更多
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
Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This stu...Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from19 asphalt pavements with different crude oil sources on a 2.038km long full-scale field accelerated pavement test track(Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition,this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction(RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error(MSE), average mean absolute error(MAE), and a verage mean absolute percentage error(MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.展开更多
Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it ...Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction tasks.To prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were compared.The evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.展开更多
Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task cl...Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state classification.With the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various applications.Guided Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of channels.This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be eliminated.This enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset.The(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.展开更多
Rockburst is a common dynamic geological hazard,frequently occurring in underground engineering(e.g.,TBM tunnelling and deep mining).In order to achieve rockburst monitoring and warning,the microseismic moni-toring te...Rockburst is a common dynamic geological hazard,frequently occurring in underground engineering(e.g.,TBM tunnelling and deep mining).In order to achieve rockburst monitoring and warning,the microseismic moni-toring technique has been widely used in the field.However,the microseismic source location has always been a challenge,playing a vital role in the precise prevention and control of rockburst.To this end,this study proposes a novel microseismic source location model that considers the anisotropy of P-wave velocity.On the one hand,it assigns a unique P-wave velocity to each propagation path,abandoning the assumption of a homogeneous ve-locity field.On the other hand,it treats the P-wave velocity as a co-inversion parameter along with the source location,avoiding the predetermination of P-wave velocity.To solve this model,three various metaheuristic multi-objective optimization algorithms are integrated with it,including the whale optimization algorithm,the butterfly optimization algorithm,and the sparrow search algorithm.To demonstrate the advantages of the model in terms of localization accuracy,localization efficiency,and solution stability,four blasting cases are collected from a water diversion tunnel project in Xinjiang,China.Finally,the effect of the number of involved sensors on the microseismic source location is discussed.展开更多
Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes.This study introduces a hybrid ap...Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes.This study introduces a hybrid approach integrating Long Short-Term Memory(LSTM)networks with the Hybrid Greylag Goose and Particle Swarm Optimization(GGPSO)algorithm to optimize preterm birth classification using Electrohysterogram signals.The dataset consists of 58 samples of 1000-second-long Electrohysterogram recordings,capturing key physiological features such as contraction patterns,entropy,and statistical variations.Statistical analysis and feature selection methods are applied to identify the most relevant predictors and enhance model interpretability.LSTM networks effectively capture temporal patterns in uterine activity,while the GGPSO algorithm finetunes hyperparameters,mitigating overfitting and improving classification accuracy.The proposed GGPSO-optimized LSTM model achieved superior performance with 97.34%accuracy,96.91%sensitivity,97.74%specificity,and 97.23%F-score,significantly outperforming traditional machine learning approaches and demonstrating the effectiveness of hybrid metaheuristic optimization in enhancing deep learning models for clinical applications.By combining deep learning withmetaheuristic optimization,this study contributes to advancing intelligent auto-diagnosis systems,facilitating early detection of pretermbirth risks and timely medical interventions.展开更多
This research presents a novel nature-inspired metaheuristic optimization algorithm,called theNarwhale Optimization Algorithm(NWOA).The algorithm draws inspiration from the foraging and prey-hunting strategies of narw...This research presents a novel nature-inspired metaheuristic optimization algorithm,called theNarwhale Optimization Algorithm(NWOA).The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals,“unicorns of the sea”,particularly the use of their distinctive spiral tusks,which play significant roles in hunting,searching prey,navigation,echolocation,and complex social interaction.Particularly,the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the Arctic ice.These functions provide a strong assessment basis for investigating the algorithm’s prowess at balancing exploration and exploitation,convergence speed,and solution accuracy.The performance of the NWOA is evaluated on 30 benchmark test functions.A comparison study using the Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Perfumer Optimization Algorithm(POA),Candle Flame Optimization(CFO)Algorithm,Particle Swarm Optimization(PSO)Algorithm,and Genetic Algorithm(GA)validates the results.As evidenced in the experimental results,NWOA is capable of yielding competitive outcomes among these well-known optimizers,whereas in several instances.These results suggest thatNWOAhas proven to be an effective and robust optimization tool suitable for solving many different complex optimization problems from the real world.展开更多
The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy ...The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.展开更多
The N-body problem in classical physics, is the calculation of force ofgravitational attraction of heavenly bodies towards each other. Solving this problem for many heavenly bodies has always posed a challenge to phys...The N-body problem in classical physics, is the calculation of force ofgravitational attraction of heavenly bodies towards each other. Solving this problem for many heavenly bodies has always posed a challenge to physicists andmathematicians. Large number of bodies, huge masses, long distances and exponentially increasing number of equations of motion of the bodies have been themajor hurdles in solving this problem for large and complex galaxies. Adventof high performance computational machines have mitigated the problem to muchextent, but still for large number of bodies it consumes huge amount of resourcesand days for computation. Conventional algorithms have been able to reduce thecomputational complexity from O n2 ð Þ to O nlogn ð Þ by splitting the space into atree or mesh network, researchers are still looking for improvements. In thisresearch work we propose a novel solution to N-body problem inspired by metaheuristics algorithms. The proposed algorithm is simulated for various time periods of selected heavenly bodies and analyzed for speed and accuracy. Theresults are compared with that of conventional algorithms. The outcomes showabout 50% time saving with almost no loss in accuracy. The proposed approachbeing a metaheuristics optimization technique, attempts to find optimal solution tothe problem, searching the entire space in a unique and efficient manner in a verylimited amount of time.展开更多
The combined heat and power economic dispatch(CHPED)problem is a highly intricate energy dispatch challenge that aims to minimize fuel costs while adhering to various constraints.This paper presents a hybrid different...The combined heat and power economic dispatch(CHPED)problem is a highly intricate energy dispatch challenge that aims to minimize fuel costs while adhering to various constraints.This paper presents a hybrid differential evolution(DE)algorithm combined with an improved equilibrium optimizer(DE-IEO)specifically for the CHPED problem.The DE-IEO incorporates three enhancement strategies:a chaotic mechanism for initializing the population,an improved equilibrium pool strategy,and a quasi-opposite based learning mechanism.These strategies enhance the individual utilization capabilities of the equilibrium optimizer,while differential evolution boosts local exploitation and escape capabilities.The IEO enhances global search to enrich the solution space,and DE focuses on local exploitation for more accurate solutions.The effectiveness of DE-IEO is demonstrated through comparative analysis with other metaheuristic optimization algorithms,including PSO,DE,ABC,GWO,WOA,SCA,and equilibrium optimizer(EO).Additionally,improved algorithms such as the enhanced chaotic gray wolf optimization(ACGWO),improved particle swarm with adaptive strategy(MPSO),and enhanced SCA with elite and dynamic opposite learning(EDOLSCA)were tested on the CEC2017 benchmark suite and four CHPED systems with 24,84,96,and 192 units,respectively.The results indicate that the proposed DE-IEO algorithm achieves satisfactory solutions for both the CEC2017 test functions and real-world CHPED optimization problems,offering a viable approach to complex optimization challenges.展开更多
The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.Thi...The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.展开更多
This study explores the integration of Synthetic Aperture Radar(SAR)imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection.This study proposes a novel hybrid appr...This study explores the integration of Synthetic Aperture Radar(SAR)imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection.This study proposes a novel hybrid approach for oil spill detection.The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization(BHHO)and Principal Component Analysis(PCA)for improved feature extraction and selection from input SAR imagery.Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images.The BHHO and PCA algorithms were implemented to identify subsets of optimal features from the entire feature dataset extracted by MobileNet.A supplemented hybrid feature set was constructed from the PCA and BHHO-generated features.It was used as input for oil spill detection using the logistic regression supervised machine learning classification algorithm.Several feature set combinations were implemented to test the classification performance of the logistic regression classifier in comparison to that of the proposed hybrid feature set.Results indicate that the highest oil spill detection accuracy of 99.2%has been achieved using the logistic regression classification algorithm,with integrated feature input from subsets identified using the PCA and the BHHO feature selection techniques.The proposed method yielded a statistically significant improvement in the classification performance of the used machine learning model.The significance of our study lies in its unique integration of deep learning with optimized feature selection,unlike other published studies,to enhance oil spill detection accuracy.展开更多
A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six ...A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,respectively.The developed hybrid PSO-XGB can be introduced as an accurate,powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR.展开更多
Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird.DTO has its unique hunting technique by performing rapid bowing movements.To show the effi...Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird.DTO has its unique hunting technique by performing rapid bowing movements.To show the efficiency of the proposed algorithm,DTO is tested and compared to the algorithms of Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),Grey Wolf Optimizer(GWO),and Genetic Algorithm(GA)based on the seven unimodal benchmark functions.Then,ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques.Additionally,to demonstrate the proposed algorithm’s suitability for solving complex realworld issues,DTO is used to solve the feature selection problem.The strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine(UCI)repository.The findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues,demonstrating the proposed algorithm’s capabilities to solve complex real-world situations.展开更多
FSSP is a typical NP-Hard problem which is desired to be minimum makespan. This study consid- ers Migrating Birds Optimization (MBO) which is metaheuristic approach for the solution of Flow Shop Sequencing Problem (FS...FSSP is a typical NP-Hard problem which is desired to be minimum makespan. This study consid- ers Migrating Birds Optimization (MBO) which is metaheuristic approach for the solution of Flow Shop Sequencing Problem (FSSP). As the basic MBO algorithm is designed for discrete problems. The performance of basic MBO algorithm is tested via some FSSP data sets exist in literature. Obtained results are compared with optimal results of related data sets.展开更多
Joints shear strength is a critical parameter during the design and construction of geotechnical engineering structures.The prevailing models mostly adopt the form of empirical functions,employing mathematical regress...Joints shear strength is a critical parameter during the design and construction of geotechnical engineering structures.The prevailing models mostly adopt the form of empirical functions,employing mathematical regression techniques to represent experimental data.As an alternative approach,this paper proposes a new integrated intelligent computing paradigm that aims to predict joints shear strength.Five metaheuristic optimization algorithms,including the chameleon swarm algorithm(CSA),slime mold algorithm,transient search optimization algorithm,equilibrium optimizer and social network search algorithm,were employed to enhance the performance of the multilayered perception(MLP)model.Efficiency comparisons were conducted between the proposed CSA-MLP model and twelve classical models,employing statistical indicators such as root mean square error(RMSE),correlation coefficient(R2),mean absolute error(MAE),and variance accounted for(VAF)to evaluate the performance of each model.The sensitivity analysis of parameters that impact joints shear strength was conducted.Finally,the feasibility and limitations of this study were discussed.The results revealed that,in comparison to other models,the CSA-MLP model exhibited the most appropriate performance in terms of R2(0.88),RMSE(0.19),MAE(0.15),and VAF(90.32%)values.The result of sensitivity analysis showed that the normal stress and the joint roughness coefficient were the most critical factors influencing joints shear strength.This paper presented an efficacious attempt toward swift prediction of joints shear strength,thus avoiding the need for costly in-site and laboratory tests.展开更多
In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We ach...In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation(ADAM)update rule.The proposed algorithm also increases the convergence rate in a narrow valley.A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size.Since ADAM is traditionally used with gradient-based optimization algorithms,therefore we first propose a gradient estimation model without the need to differentiate the objective function.Resultantly,it demonstrates excellent performance and fast convergence rate in searching for the optimum of noin-convex functions.The efficiency of the proposed algorithm was tested on three different benchmark problems,including the training of a high-dimensional neural network.The performance is compared with particle swarm optimizer(PSO)and the original BAS algorithm.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
Heart disease is a primary cause of death worldwide and is notoriously difficult to cure without a proper diagnosis.Hence,machine learning(ML)can reduce and better understand symptoms associated with heart disease.Thi...Heart disease is a primary cause of death worldwide and is notoriously difficult to cure without a proper diagnosis.Hence,machine learning(ML)can reduce and better understand symptoms associated with heart disease.This study aims to develop a framework for the automatic and accurate classification of heart disease utilizing machine learning algorithms,grid search(GS),and the Aquila optimization algorithm.In the proposed approach,feature selection is used to identify characteristics of heart disease by using a method for dimensionality reduction.First,feature selection is accomplished with the help of the Aquila algorithm.Then,the optimal combination of the hyperparameters is selected using grid search.The experiments were conducted with three datasets from Kaggle:The Heart Failure Prediction Dataset,Heart Disease Binary Classification,and Heart Disease Dataset.Two classes can be distinguished:diseased and healthy(i.e.,uninfected).The Histogram Gradient Boosting(HGB)classifier produced the highest Weighted Sum Metric(WSM)scores of 98.65%concerning the Heart Failure Prediction Dataset.In contrast,the Decision Tree(DT)machine learning classifier had the highest WSM scores of 87.64%concerning the Heart Disease Health Indicators Dataset.Measures of accuracy,specificity,sensitivity,and other metrics are used to evaluate the proposed approach.The presented method demonstrates superior performance compared to different state-ofthe-art algorithms.展开更多
基金supported by the National Natural Science Foundation of China(22408227,22238005)the Postdoctoral Research Foundation of China(GZC20231576).
文摘The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and uncertainties during optimization remains a formidable challenge. In this study, a strategy combining interpretable machine learning with metaheuristic optimization algorithms is employed to optimize the reaction process. First, experimental data from a biodiesel production process are collected to establish a database. These data are then used to construct a predictive model based on artificial neural network (ANN) models. Subsequently, interpretable machine learning techniques are applied for quantitative analysis and verification of the model. Finally, four metaheuristic optimization algorithms are coupled with the ANN model to achieve the desired optimization. The research results show that the methanol: palm fatty acid distillate (PFAD) molar ratio contributes the most to the reaction outcome, accounting for 41%. The ANN-simulated annealing (SA) hybrid method is more suitable for this optimization, and the optimal process parameters are a catalyst concentration of 3.00% (mass), a methanol: PFAD molar ratio of 8.67, and a reaction time of 30 min. This study provides deeper insights into reaction process optimization, which will facilitate future applications in various reaction optimization processes.
文摘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.
基金supported by the Analytical Center for the Government of the Russian Federation (IGK 000000D730321P5Q0002) and Agreement Nos.(70-2021-00141)。
文摘Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from19 asphalt pavements with different crude oil sources on a 2.038km long full-scale field accelerated pavement test track(Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition,this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction(RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error(MSE), average mean absolute error(MAE), and a verage mean absolute percentage error(MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R323)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction tasks.To prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were compared.The evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.
基金Funding for this study is received from Taif University Researchers Supporting Project No.(Project No.TURSP-2020/150)Taif University,Taif,Saudi Arabia。
文摘Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state classification.With the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various applications.Guided Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of channels.This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be eliminated.This enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset.The(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.
基金supported by the National Natural Science Founda-tion of China under Grant Nos.42472351,42177140,52404127,and 42207235the Natural Science Foundation of Hubei Province under Grant No.2024AFD359+1 种基金the Young Elite Scientist Sponsorship Program by CAST under Grant No.YESS20230742the China Postdoctoral Science Foundation Program under Grant No.2024T170684.
文摘Rockburst is a common dynamic geological hazard,frequently occurring in underground engineering(e.g.,TBM tunnelling and deep mining).In order to achieve rockburst monitoring and warning,the microseismic moni-toring technique has been widely used in the field.However,the microseismic source location has always been a challenge,playing a vital role in the precise prevention and control of rockburst.To this end,this study proposes a novel microseismic source location model that considers the anisotropy of P-wave velocity.On the one hand,it assigns a unique P-wave velocity to each propagation path,abandoning the assumption of a homogeneous ve-locity field.On the other hand,it treats the P-wave velocity as a co-inversion parameter along with the source location,avoiding the predetermination of P-wave velocity.To solve this model,three various metaheuristic multi-objective optimization algorithms are integrated with it,including the whale optimization algorithm,the butterfly optimization algorithm,and the sparrow search algorithm.To demonstrate the advantages of the model in terms of localization accuracy,localization efficiency,and solution stability,four blasting cases are collected from a water diversion tunnel project in Xinjiang,China.Finally,the effect of the number of involved sensors on the microseismic source location is discussed.
基金funded by the National Plan for Science,Technology and Innovation(MAARIFAH)-King Abdulaziz City for Science and Technology-The Kingdom of Saudi Arabia-award number(13-MAT377-08).
文摘Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes.This study introduces a hybrid approach integrating Long Short-Term Memory(LSTM)networks with the Hybrid Greylag Goose and Particle Swarm Optimization(GGPSO)algorithm to optimize preterm birth classification using Electrohysterogram signals.The dataset consists of 58 samples of 1000-second-long Electrohysterogram recordings,capturing key physiological features such as contraction patterns,entropy,and statistical variations.Statistical analysis and feature selection methods are applied to identify the most relevant predictors and enhance model interpretability.LSTM networks effectively capture temporal patterns in uterine activity,while the GGPSO algorithm finetunes hyperparameters,mitigating overfitting and improving classification accuracy.The proposed GGPSO-optimized LSTM model achieved superior performance with 97.34%accuracy,96.91%sensitivity,97.74%specificity,and 97.23%F-score,significantly outperforming traditional machine learning approaches and demonstrating the effectiveness of hybrid metaheuristic optimization in enhancing deep learning models for clinical applications.By combining deep learning withmetaheuristic optimization,this study contributes to advancing intelligent auto-diagnosis systems,facilitating early detection of pretermbirth risks and timely medical interventions.
文摘This research presents a novel nature-inspired metaheuristic optimization algorithm,called theNarwhale Optimization Algorithm(NWOA).The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals,“unicorns of the sea”,particularly the use of their distinctive spiral tusks,which play significant roles in hunting,searching prey,navigation,echolocation,and complex social interaction.Particularly,the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the Arctic ice.These functions provide a strong assessment basis for investigating the algorithm’s prowess at balancing exploration and exploitation,convergence speed,and solution accuracy.The performance of the NWOA is evaluated on 30 benchmark test functions.A comparison study using the Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Perfumer Optimization Algorithm(POA),Candle Flame Optimization(CFO)Algorithm,Particle Swarm Optimization(PSO)Algorithm,and Genetic Algorithm(GA)validates the results.As evidenced in the experimental results,NWOA is capable of yielding competitive outcomes among these well-known optimizers,whereas in several instances.These results suggest thatNWOAhas proven to be an effective and robust optimization tool suitable for solving many different complex optimization problems from the real world.
基金Project supported by the Zhejiang Provincial Natural Science Foundation (Grant No.LQ20F020011)the Gansu Provincial Foundation for Distinguished Young Scholars (Grant No.23JRRA766)+1 种基金the National Natural Science Foundation of China (Grant No.62162040)the National Key Research and Development Program of China (Grant No.2020YFB1713600)。
文摘The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.
基金This research study has been supported by National Center in Big Data and Cloud Computing,NED University of Engincering and Technology,Karachi,Pakistan.
文摘The N-body problem in classical physics, is the calculation of force ofgravitational attraction of heavenly bodies towards each other. Solving this problem for many heavenly bodies has always posed a challenge to physicists andmathematicians. Large number of bodies, huge masses, long distances and exponentially increasing number of equations of motion of the bodies have been themajor hurdles in solving this problem for large and complex galaxies. Adventof high performance computational machines have mitigated the problem to muchextent, but still for large number of bodies it consumes huge amount of resourcesand days for computation. Conventional algorithms have been able to reduce thecomputational complexity from O n2 ð Þ to O nlogn ð Þ by splitting the space into atree or mesh network, researchers are still looking for improvements. In thisresearch work we propose a novel solution to N-body problem inspired by metaheuristics algorithms. The proposed algorithm is simulated for various time periods of selected heavenly bodies and analyzed for speed and accuracy. Theresults are compared with that of conventional algorithms. The outcomes showabout 50% time saving with almost no loss in accuracy. The proposed approachbeing a metaheuristics optimization technique, attempts to find optimal solution tothe problem, searching the entire space in a unique and efficient manner in a verylimited amount of time.
基金supported by the Scientific Research Project of Xiangsihu College of Guangxi Minzu University,Grant No.2024XJKY06the National Natural Science Foundation of China under Grant No.U21A20464.
文摘The combined heat and power economic dispatch(CHPED)problem is a highly intricate energy dispatch challenge that aims to minimize fuel costs while adhering to various constraints.This paper presents a hybrid differential evolution(DE)algorithm combined with an improved equilibrium optimizer(DE-IEO)specifically for the CHPED problem.The DE-IEO incorporates three enhancement strategies:a chaotic mechanism for initializing the population,an improved equilibrium pool strategy,and a quasi-opposite based learning mechanism.These strategies enhance the individual utilization capabilities of the equilibrium optimizer,while differential evolution boosts local exploitation and escape capabilities.The IEO enhances global search to enrich the solution space,and DE focuses on local exploitation for more accurate solutions.The effectiveness of DE-IEO is demonstrated through comparative analysis with other metaheuristic optimization algorithms,including PSO,DE,ABC,GWO,WOA,SCA,and equilibrium optimizer(EO).Additionally,improved algorithms such as the enhanced chaotic gray wolf optimization(ACGWO),improved particle swarm with adaptive strategy(MPSO),and enhanced SCA with elite and dynamic opposite learning(EDOLSCA)were tested on the CEC2017 benchmark suite and four CHPED systems with 24,84,96,and 192 units,respectively.The results indicate that the proposed DE-IEO algorithm achieves satisfactory solutions for both the CEC2017 test functions and real-world CHPED optimization problems,offering a viable approach to complex optimization challenges.
文摘The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0456.
文摘This study explores the integration of Synthetic Aperture Radar(SAR)imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection.This study proposes a novel hybrid approach for oil spill detection.The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization(BHHO)and Principal Component Analysis(PCA)for improved feature extraction and selection from input SAR imagery.Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images.The BHHO and PCA algorithms were implemented to identify subsets of optimal features from the entire feature dataset extracted by MobileNet.A supplemented hybrid feature set was constructed from the PCA and BHHO-generated features.It was used as input for oil spill detection using the logistic regression supervised machine learning classification algorithm.Several feature set combinations were implemented to test the classification performance of the logistic regression classifier in comparison to that of the proposed hybrid feature set.Results indicate that the highest oil spill detection accuracy of 99.2%has been achieved using the logistic regression classification algorithm,with integrated feature input from subsets identified using the PCA and the BHHO feature selection techniques.The proposed method yielded a statistically significant improvement in the classification performance of the used machine learning model.The significance of our study lies in its unique integration of deep learning with optimized feature selection,unlike other published studies,to enhance oil spill detection accuracy.
基金funded by the National Science Foundation of China(41807259)the Innovation-Driven Project of Central South University(No.2020CX040)the Shenghua Lieying Program of Central South University(Principle Investigator:Dr.Jian Zhou)。
文摘A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,respectively.The developed hybrid PSO-XGB can be introduced as an accurate,powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR.
文摘Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird.DTO has its unique hunting technique by performing rapid bowing movements.To show the efficiency of the proposed algorithm,DTO is tested and compared to the algorithms of Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),Grey Wolf Optimizer(GWO),and Genetic Algorithm(GA)based on the seven unimodal benchmark functions.Then,ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques.Additionally,to demonstrate the proposed algorithm’s suitability for solving complex realworld issues,DTO is used to solve the feature selection problem.The strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine(UCI)repository.The findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues,demonstrating the proposed algorithm’s capabilities to solve complex real-world situations.
基金supported by Scientific Research Project of Necmettin Erbakan University
文摘FSSP is a typical NP-Hard problem which is desired to be minimum makespan. This study consid- ers Migrating Birds Optimization (MBO) which is metaheuristic approach for the solution of Flow Shop Sequencing Problem (FSSP). As the basic MBO algorithm is designed for discrete problems. The performance of basic MBO algorithm is tested via some FSSP data sets exist in literature. Obtained results are compared with optimal results of related data sets.
基金This paper gets its funding from Projects(42277175)supported by National Natural Science Foundation of China,Project(2023JJ30657)+2 种基金supported by Hunan Provincial Natural Science Foundation of China and the National Key Research,Hunan Provincial Department of natural resources geological exploration project(BSDZSB43202403)The First National Natural Disaster Comprehensive Risk Survey in Hunan Province(2022-70the National Key Research and Development Program of China-2023 Key Special Project(No.2023YFC2907400).
文摘Joints shear strength is a critical parameter during the design and construction of geotechnical engineering structures.The prevailing models mostly adopt the form of empirical functions,employing mathematical regression techniques to represent experimental data.As an alternative approach,this paper proposes a new integrated intelligent computing paradigm that aims to predict joints shear strength.Five metaheuristic optimization algorithms,including the chameleon swarm algorithm(CSA),slime mold algorithm,transient search optimization algorithm,equilibrium optimizer and social network search algorithm,were employed to enhance the performance of the multilayered perception(MLP)model.Efficiency comparisons were conducted between the proposed CSA-MLP model and twelve classical models,employing statistical indicators such as root mean square error(RMSE),correlation coefficient(R2),mean absolute error(MAE),and variance accounted for(VAF)to evaluate the performance of each model.The sensitivity analysis of parameters that impact joints shear strength was conducted.Finally,the feasibility and limitations of this study were discussed.The results revealed that,in comparison to other models,the CSA-MLP model exhibited the most appropriate performance in terms of R2(0.88),RMSE(0.19),MAE(0.15),and VAF(90.32%)values.The result of sensitivity analysis showed that the normal stress and the joint roughness coefficient were the most critical factors influencing joints shear strength.This paper presented an efficacious attempt toward swift prediction of joints shear strength,thus avoiding the need for costly in-site and laboratory tests.
文摘In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation(ADAM)update rule.The proposed algorithm also increases the convergence rate in a narrow valley.A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size.Since ADAM is traditionally used with gradient-based optimization algorithms,therefore we first propose a gradient estimation model without the need to differentiate the objective function.Resultantly,it demonstrates excellent performance and fast convergence rate in searching for the optimum of noin-convex functions.The efficiency of the proposed algorithm was tested on three different benchmark problems,including the training of a high-dimensional neural network.The performance is compared with particle swarm optimizer(PSO)and the original BAS algorithm.
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Heart disease is a primary cause of death worldwide and is notoriously difficult to cure without a proper diagnosis.Hence,machine learning(ML)can reduce and better understand symptoms associated with heart disease.This study aims to develop a framework for the automatic and accurate classification of heart disease utilizing machine learning algorithms,grid search(GS),and the Aquila optimization algorithm.In the proposed approach,feature selection is used to identify characteristics of heart disease by using a method for dimensionality reduction.First,feature selection is accomplished with the help of the Aquila algorithm.Then,the optimal combination of the hyperparameters is selected using grid search.The experiments were conducted with three datasets from Kaggle:The Heart Failure Prediction Dataset,Heart Disease Binary Classification,and Heart Disease Dataset.Two classes can be distinguished:diseased and healthy(i.e.,uninfected).The Histogram Gradient Boosting(HGB)classifier produced the highest Weighted Sum Metric(WSM)scores of 98.65%concerning the Heart Failure Prediction Dataset.In contrast,the Decision Tree(DT)machine learning classifier had the highest WSM scores of 87.64%concerning the Heart Disease Health Indicators Dataset.Measures of accuracy,specificity,sensitivity,and other metrics are used to evaluate the proposed approach.The presented method demonstrates superior performance compared to different state-ofthe-art algorithms.
