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
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
Harvesting the power coming from the wind provides a green andenvironmentally friendly approach to producing electricity. To facilitate theongoing advancement in wind energy applications, deep knowledge aboutwind regi...Harvesting the power coming from the wind provides a green andenvironmentally friendly approach to producing electricity. To facilitate theongoing advancement in wind energy applications, deep knowledge aboutwind regime behavior is essential. Wind speed is typically characterized bya statistical distribution, and the two-parameters Weibull distribution hasshown its ability to represent wind speeds worldwide. Estimation of Weibullparameters, namely scale (c) and shape (k) parameters, is vital to describethe observed wind speeds data accurately. Yet, it is still a challenging task.Several numerical estimation approaches have been used by researchers toobtain c and k. However, utilizing such methods to characterize wind speedsmay lead to unsatisfactory accuracy. Therefore, this study aims to investigatethe performance of the metaheuristic optimization algorithm, Neural NetworkAlgorithm (NNA), in obtaining Weibull parameters and comparing itsperformance with five numerical estimation approaches. In carrying out thestudy, the wind characteristics of three sites in Saudi Arabia, namely HaferAl Batin, Riyadh, and Sharurah, are analyzed. Results exhibit that NNA hashigh accuracy fitting results compared to the numerical estimation methods.The NNA demonstrates its efficiency in optimizing Weibull parameters at allthe considered sites with correlations exceeding 98.54.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
Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging environments.In aut...Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging environments.In autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of CNNs.In this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best results.Literature has proven the superiority of metaheuristic algorithms over the manual-tuning of CNNs.However,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle prediction.Thus,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction problem.To validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear unit.As per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower ones.Results show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning approach.Infield testing was also performed using the model trained with the optimal architecture,which we developed using our approach.展开更多
Tunnel surrounding rock(TSR)deformation exhibits time-and space-dependent behavior,making it challenging for a single prediction model to capture these characteristics over extended periods.Utilizing 8 years of TSR de...Tunnel surrounding rock(TSR)deformation exhibits time-and space-dependent behavior,making it challenging for a single prediction model to capture these characteristics over extended periods.Utilizing 8 years of TSR deformation data from the Beishan exploration tunnel(BET)test platform,the metaheuristic algorithm crested porcupine optimizer(CPO)was applied for the first time to optimize the time series of TSR deformation,and an integrated model incorporating convolutional neural network(CNN),long short-term memory network(LSTM),and attention mechanism(ATT)was proposed.This model integrates the strong feature extraction capabilities of CNN,the superior sequence prediction performance of LSTM,and the effective attention mechanism of ATT.The results show that during blasting excavation,the internal displacement of TSR exhibits a stepwise change pattern.After excavation,the internal displacement enters a phase of gradual increase,ultimately reaching a stable convergence stage.The CPO-CNN-LSTM-ATT(CPO-CLA)integrated model demonstrated excellent predictive accuracy and stability across various evaluation metrics,achieving a determination coefficient(R^(2))of 0.985.Compared to the CNN-LSTM-ATT(CLA)model,the CPO-CLA model showed a 14.1%increase in R^(2),a 61.5%decrease in root mean square error(RMSE),and a 72.9%decrease in mean absolute error(MAE).In comparison with current mainstream metaheuristic integrated models,the CPO-CLA model is better suited for predicting long-term TSR deformation.It offers high computational efficiency,accurate predictions,and expertise in optimizing large datasets.展开更多
The electric distribution system(EDS)is prone to faults leading to power interruptions.The present energy market demands that electricity utilities invest more in different measures to improve the performance of the E...The electric distribution system(EDS)is prone to faults leading to power interruptions.The present energy market demands that electricity utilities invest more in different measures to improve the performance of the EDS.The approach proposed here details a composite dual-phased methodology to improve the reliability and efficiency of the power delivered by the EDS.In the first phase,the optimal allocation of auto-reclosers(AR)is undertaken by employing a newly formulated algorithm.The determination of the total number and location for AR placement is based on the economic analysis of two factors,i.e.,AR investment-maintenance cost and total benefit earned in terms of reliability improvement due to AR placement.The analysis also takes into account the impact of power outages on different load types,the load growth rate,and the inflation rate.Further,to enhance the efficiency of the AR-incorpo-rated EDS,the technique of Radial Distribution System Remodelling is employed in the second phase.This method searches for a radial configuration that delivers power at minimum line losses.These phases comprising complex combinatorial operations are aided by a fresh hybrid of the Sine Cosine Algorithm,Krill Herd Algorithm,and a genetic operator of Differential Evolution.The results obtained from its application on the IEEE 69-bus distribution test system prove the credibility of the suggested formulation.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.
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the Deputyship for Research&Innovation,Ministry of Education,Saudi Arabia for funding this research work through the project number (QUIF-4-3-3-31466).
文摘Harvesting the power coming from the wind provides a green andenvironmentally friendly approach to producing electricity. To facilitate theongoing advancement in wind energy applications, deep knowledge aboutwind regime behavior is essential. Wind speed is typically characterized bya statistical distribution, and the two-parameters Weibull distribution hasshown its ability to represent wind speeds worldwide. Estimation of Weibullparameters, namely scale (c) and shape (k) parameters, is vital to describethe observed wind speeds data accurately. Yet, it is still a challenging task.Several numerical estimation approaches have been used by researchers toobtain c and k. However, utilizing such methods to characterize wind speedsmay lead to unsatisfactory accuracy. Therefore, this study aims to investigatethe performance of the metaheuristic optimization algorithm, Neural NetworkAlgorithm (NNA), in obtaining Weibull parameters and comparing itsperformance with five numerical estimation approaches. In carrying out thestudy, the wind characteristics of three sites in Saudi Arabia, namely HaferAl Batin, Riyadh, and Sharurah, are analyzed. Results exhibit that NNA hashigh accuracy fitting results compared to the numerical estimation methods.The NNA demonstrates its efficiency in optimizing Weibull parameters at allthe considered sites with correlations exceeding 98.54.
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.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation,Ministry of Education,Kingdom of Saudi Arabia for this research through a grant(NU/IFC/INT/01/008)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging environments.In autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of CNNs.In this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best results.Literature has proven the superiority of metaheuristic algorithms over the manual-tuning of CNNs.However,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle prediction.Thus,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction problem.To validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear unit.As per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower ones.Results show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning approach.Infield testing was also performed using the model trained with the optimal architecture,which we developed using our approach.
基金supported by the China Atomic Energy Authority(CAEA)for China’s URL Development Program and the Geological Disposal Program(Grant No.FZ2105)the National Natural Science Foundation of China(Grant No.52278420).
文摘Tunnel surrounding rock(TSR)deformation exhibits time-and space-dependent behavior,making it challenging for a single prediction model to capture these characteristics over extended periods.Utilizing 8 years of TSR deformation data from the Beishan exploration tunnel(BET)test platform,the metaheuristic algorithm crested porcupine optimizer(CPO)was applied for the first time to optimize the time series of TSR deformation,and an integrated model incorporating convolutional neural network(CNN),long short-term memory network(LSTM),and attention mechanism(ATT)was proposed.This model integrates the strong feature extraction capabilities of CNN,the superior sequence prediction performance of LSTM,and the effective attention mechanism of ATT.The results show that during blasting excavation,the internal displacement of TSR exhibits a stepwise change pattern.After excavation,the internal displacement enters a phase of gradual increase,ultimately reaching a stable convergence stage.The CPO-CNN-LSTM-ATT(CPO-CLA)integrated model demonstrated excellent predictive accuracy and stability across various evaluation metrics,achieving a determination coefficient(R^(2))of 0.985.Compared to the CNN-LSTM-ATT(CLA)model,the CPO-CLA model showed a 14.1%increase in R^(2),a 61.5%decrease in root mean square error(RMSE),and a 72.9%decrease in mean absolute error(MAE).In comparison with current mainstream metaheuristic integrated models,the CPO-CLA model is better suited for predicting long-term TSR deformation.It offers high computational efficiency,accurate predictions,and expertise in optimizing large datasets.
文摘The electric distribution system(EDS)is prone to faults leading to power interruptions.The present energy market demands that electricity utilities invest more in different measures to improve the performance of the EDS.The approach proposed here details a composite dual-phased methodology to improve the reliability and efficiency of the power delivered by the EDS.In the first phase,the optimal allocation of auto-reclosers(AR)is undertaken by employing a newly formulated algorithm.The determination of the total number and location for AR placement is based on the economic analysis of two factors,i.e.,AR investment-maintenance cost and total benefit earned in terms of reliability improvement due to AR placement.The analysis also takes into account the impact of power outages on different load types,the load growth rate,and the inflation rate.Further,to enhance the efficiency of the AR-incorpo-rated EDS,the technique of Radial Distribution System Remodelling is employed in the second phase.This method searches for a radial configuration that delivers power at minimum line losses.These phases comprising complex combinatorial operations are aided by a fresh hybrid of the Sine Cosine Algorithm,Krill Herd Algorithm,and a genetic operator of Differential Evolution.The results obtained from its application on the IEEE 69-bus distribution test system prove the credibility of the suggested formulation.
