Welding deformation adversely affects the quality and precision of structural components,and traditional methods require significant material resources and time.Machine learning has demonstrated exceptional ac-curacy ...Welding deformation adversely affects the quality and precision of structural components,and traditional methods require significant material resources and time.Machine learning has demonstrated exceptional ac-curacy and efficiency in solving complex problems.Thus,the use of machine learning to predict welding de-formations is a novel approach.In this study,laser welding experiments were conducted on a TC4 titanium alloy to establish a welding deformation dataset.The deep neural network(DNN)and convolutional neural network(CNN)models were designed and constructed,with average prediction errors of 0.85 mm and 0.94 mm on the validation set,respectively.To further optimize the network parameters,a differential evolution algorithm was employed through mutation,crossover,and selection.The results indicated that after optimization,the pre-diction errors of the DNN and CNN models reduced to 0.75 mm and 0.85 mm,respectively.These represent accuracy improvements of 14.8%and 9.6%,respectively.The optimized models exhibited superior predictive performances for the validation set.展开更多
To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating t...To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine(ELM).Input weights and hidden layer biases were generated randomly in traditional ELM.To improve the estimation accuracy of ELM,the differential evolution algorithm was used to optimize these parameters in feasible solution spaces.First,incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests.Then,the ELM based on differential evolution algorithm(DE-ELM model)was used for a lithium-ion battery SOH estimation.At last,four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model.Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.展开更多
Learning control has been recognized as a powerful approach in quantum information technology. In this paper, we extend the application of differential evolution （DE） to design optimal control for various quantum sy...Learning control has been recognized as a powerful approach in quantum information technology. In this paper, we extend the application of differential evolution （DE） to design optimal control for various quantum systems. Various DE methods are introduced and analyzed, and EMSDE featuring in equally mixed strategies is employed for quantum control. Two classes of quantum control problems, including control of four-level open quantum ensembles and quantum superconducting systems, are investigated to demonstrate the performance of EMSDE for learning control of quantum systems. Numerical results verify the effectiveness of the FMSDE method for various quantum systems and show the potential for complex quantum control problems.展开更多
Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated.It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction.Early det...Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated.It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction.Early detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this condition.However,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per patient.To overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for training.Developing this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in Tehran.To address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority class.Additionally,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training phase.The effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance metrics.Overall,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.展开更多
Wire-fed laser-arc directed energy deposition(Wire-fed LA-DED)Technol.improves production speed while maintaining high quality and is particularly suited for manufacturing large,complex aluminum or titanium alloy comp...Wire-fed laser-arc directed energy deposition(Wire-fed LA-DED)Technol.improves production speed while maintaining high quality and is particularly suited for manufacturing large,complex aluminum or titanium alloy components.The geometry of the weld bead(height and width)is influenced by multiple intricate parameters and variables during the manufacturing process.Accurately predicting the weld bead shape enables precise control over the surface flatness of the part,helping to prevent defects such as lack of fusion.This significantly reduces dimensional redundancy,enhances printing efficiency,and optimizes material usage.In this study,a quadratic regression prediction model for weld bead geometry was developed using the response surface methodology(RSM),with predictions generated through several machine learning models.These models included the back-propagation neural network(BPNN),support vector regression(SVR),multi-output support vector regression(MOSVR),extreme learning machine(ELM),and a differential evolution-optimized MOSVR(DE-MOSVR)model.Grid search and cross-validation techniques were utilized to identify the optimal parameters for each model to achieve the best predictive performance.A comparison of these models was conducted,followed by an evaluation of their generalization capabilities using an additional 20 sets of test data.The most accurate predictive model was selected based on a comprehensive assessment.The results showed that the DE-MOSVR model outperformed the others,achieving mean squared error,root mean squared error,mean absolute error,and R^(2) values for width(height)predictions of 0.0411(0.0041),0.2028(0.0639),0.1671(0.0550),and 0.9434(0.9433),respectively.It demonstrated the smallest deviation in the validation set,with mean deviations of 1.97% and 1.68%,respectively.The model we developed was validated through the production of prototype parts,providing valuable reference and guidance for predicting and modeling weld bead morphology in the Wire-fed LA-DED process.展开更多
Adaptive Cross-Generation Differential Evolution(ACGDE)is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms(EAs).However,its conv...Adaptive Cross-Generation Differential Evolution(ACGDE)is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms(EAs).However,its convergence and diversity are not satisfactory compared with the latest algorithms.In order to adapt to the current environment,ACGDE requires improvements in many aspects,such as its initialization and mutant operator.In this paper,an enhanced version is proposed,namely SIACGDE.It incorporates a strengthened initialization strategy and optimized parameters in contrast to its predecessor.These improvements make the direction of crossgeneration mutation more clearly and the ability of searching more efficiently.The experiments show that the new algorithm has better diversity and improves convergence to a certain extent.At the same time,SIACGDE outperforms other state-of-the-art algorithms on four metrics of 24 test problems.展开更多
One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil.In this study,the main purpose is to develop a novel hybrid Machine Learning(ML...One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil.In this study,the main purpose is to develop a novel hybrid Machine Learning(ML)model(ANFIS-DE),which used Differential Evolution(DE)algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System(ANFIS),for estimating soil Compression coefficient(Cc)from other geotechnical parameters namelyWater Content,Void Ratio,SpecificGravity,Liquid Limit,Plastic Limit,Clay content and Depth of Soil Samples.Validation of the predictive capability of the novel model was carried out using statistical indices:Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Correlation Coefficient(R).In addition,two popular ML models namely Reduced Error Pruning Trees(REPTree)and Decision Stump(Dstump)were used for comparison.Results showed that the performance of the novel model ANFIS-DE is the best(R=0.825,MAE=0.064 and RMSE=0.094)in comparison to other models such as REPTree(R=0.7802,MAE=0.068 and RMSE=0.0988)andDstump(R=0.7325,MAE=0.0785 and RMSE=0.1036).Therefore,the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc,which can be employed in the design and construction of civil engineering structures.展开更多
Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses thatmust be conducted.Building a surrogatemodel to approx...Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses thatmust be conducted.Building a surrogatemodel to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem.However,most existing surrogate models have been designed based on regression techniques.This paper proposes a novel method,called CaDE,which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution(DE)optimization.The proposed method is separated into two stages.During the first optimization stage,the original DE is implemented as usual,but all individuals produced in this phase are stored as inputs of the training data.Based on design constraints verification,these individuals are labeled as“safe”or“unsafe”and their labels are saved as outputs of the training data.When collecting enough data,an AdaBoost model is trained to evaluate the safety state of structures.This model is then used in the second stage to preliminarily assess new individuals,and unpromising ones are rejected without checking design constraints.This method reduces unnecessary structural analyses,thereby shortens the optimization process.Five benchmark truss sizing optimization problems are solved using the proposed method to demonstrate its effectiveness.The obtained results show that the CaDE finds good optimal designs with less structural analyses in comparison with the original DE and four other DE variants.The reduction rate of five examples ranges from 18 to over 50%.Moreover,the proposed method is applied to a real-size transmission tower design problem to exhibit its applicability in practice.展开更多
Optimizing wind energy harvesting performance remains a significant challenge.Machine learning(ML)offers a promising approach for addressing this challenge.This study proposes an ML-based approach using the radial bas...Optimizing wind energy harvesting performance remains a significant challenge.Machine learning(ML)offers a promising approach for addressing this challenge.This study proposes an ML-based approach using the radial basis function neural network(RBFNN)and differential evolution(DE)to predict and optimize the structural parameters(the diameter of the spherical bluff body D,the total spring stiffness k,and the length of the piezoelectric cantilever beam L)of the wind energy harvester(WEH).The RBFNN model is trained with theoretical data and validated with wind tunnel experimental results,achieving the coefficient-of-determination scores R2of 97.8%and 90.3%for predicting the average output power Pavgand aero-electro-mechanical efficiencyηaem,respectively.The DE algorithm is used to identify the optimal parameter combinations for wind speeds U ranging from 2.5 m/s to 6.5 m/s.The maximum Pavgis achieved when D=57.5 mm,k=28.8 N/m,L=112.1 mm,and U=4.6 m/s,while the maximumηaemis achieved when D=52.7 mm,k=29.2 N/m,L=89.2 mm,and U=4.7 m/s.Compared with that of the non-optimized structure,the WEH performance is improved by 28.6%in P_(avg)and 19.1%inη_(aem).展开更多
The mutation operations and related control parameters play important roles in the performance of the differential evolution algorithm.Learning optimal policies for these strategies and parameters through reinforcemen...The mutation operations and related control parameters play important roles in the performance of the differential evolution algorithm.Learning optimal policies for these strategies and parameters through reinforcement learning is a hot topic.However,most of the current studies focus on either mutation strategy selection or the control parameters alone while the others keep fixed or self-adaptive,resulting in deteriorated performances.To address this gap,this paper proposes a framework for the joint adaptation of mutation strategies and related control parameters based on deep reinforcement learning.In this method,the distributed proximal policy optimization algorithm is employed to train the agents to dynamically select the optimal combination of mutation strategies and control parameters.To enhance the agent’s learning of the optimal policy,information derived from fitness landscape analysis is incorporated into the state representations.The training is conducted on the black-box optimization benchmark test problems,which are capable of generating large-scale test instances.Numerical results on the new problems from CEC2013 and CEC2017 test suites,and the real-world application of rover trajectory planning demonstrate that the proposed approach achieves competitive performance compared to state-of-the-art methods.The adaptation behavior and the contribution of learning are also thoroughly analyzed.展开更多
This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions,by combining the advantages of two powerful population-based metaheuristics different...This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions,by combining the advantages of two powerful population-based metaheuristics differential evolution(DE)and particle swarm optimization(PSO).In the hybrid denoted by DEPSO,each individual in one generation chooses its evolution method,DE or PSO,in a statistical learning way.The choice depends on the relative success ratio of the two methods in a previous learning period.The proposed DEPSO is compared with its PSO and DE parents,two advanced DE variants one of which is suggested by the originators of DE,two advanced PSO variants one of which is acknowledged as a recent standard by PSO community,and also a previous DEPSO.Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.展开更多
Nonlinear equations systems(NESs)arise in a wide range of domains.Solving NESs requires the algorithm to locate multiple roots simultaneously.To deal with NESs efficiently,this study presents an enhanced reinforcement...Nonlinear equations systems(NESs)arise in a wide range of domains.Solving NESs requires the algorithm to locate multiple roots simultaneously.To deal with NESs efficiently,this study presents an enhanced reinforcement learning based differential evolution with the following major characteristics:(1)the design of state function uses the information on the fitness alternation action;(2)different neighborhood sizes and mutation strategies are combined as optional actions;and(3)the unbalanced assignment method is adopted to change the reward value to select the optimal actions.To evaluate the performance of our approach,30 NESs test problems and 18 test instances with different features are selected as the test suite.The experimental results indicate that the proposed approach can improve the performance in solving NESs,and outperform several state-of-the-art methods.展开更多
This paper presents opposition-based differential evolution to determine the optimal hourly schedule of power generation in a hydrothermal system.Differential evolution(DE)is a population-based stochastic parallel sea...This paper presents opposition-based differential evolution to determine the optimal hourly schedule of power generation in a hydrothermal system.Differential evolution(DE)is a population-based stochastic parallel search evolutionary algorithm.Opposition-based differential evolution has been used here to improve the effectiveness and quality of the solution.The proposed opposition-based differential evolution(ODE)employs opposition-based learning(OBL)for population initialization and also for generation jumping.The effectiveness of the proposed method has been verified on two test problems,two fixed head hydrothermal test systems and three hydrothermal multi-reservoir cascaded hydroelectric test systems having prohibited operating zones and thermal units with valve point loading.The results of the proposed approach are compared with those obtained by other evolutionary methods.It is found that the proposed opposition-based differential evolution based approach is able to provide better solution.展开更多
The Differential Evolution(DE)algorithm,which is an efficient optimization algorithm,has been used to solve various optimization problems.In this paper,adaptive dimensional learning with a tolerance framework for DE i...The Differential Evolution(DE)algorithm,which is an efficient optimization algorithm,has been used to solve various optimization problems.In this paper,adaptive dimensional learning with a tolerance framework for DE is proposed.The population is divided into an elite subpopulation,an ordinary subpopulation,and an inferior subpopulation according to the fitness values.The ordinary and elite subpopulations are used to maintain the current evolution state and to guide the evolution direction of the population,respectively.The inferior subpopulation learns from the elite subpopulation through the dimensional learning strategy.If the global optimum is not improved in a specified number of iterations,a tolerance mechanism is applied.Under the tolerance mechanism,the inferior and elite subpopulations implement the restart strategy and the reverse dimensional learning strategy,respectively.In addition,the individual status and algorithm status are used to adaptively adjust the control parameters.To evaluate the performance of the proposed algorithm,six state-of-the-art DE algorithm variants are compared on the benchmark functions.The results of the simulation show that the proposed algorithm outperforms other variant algorithms regarding function convergence rate and solution accuracy.展开更多
To address complex single objective global optimization problems,a new Level-Based Learning Differential Evolution(LBLDE)is developed in this study.In this approach,the whole population is sorted from the best to the ...To address complex single objective global optimization problems,a new Level-Based Learning Differential Evolution(LBLDE)is developed in this study.In this approach,the whole population is sorted from the best to the worst at the beginning of each generation.Then,the population is partitioned into multiple levels,and different levels are used to exert different functions.In each level,a control parameter is used to select excellent exemplars from upper levels for learning.In this case,the poorer individuals can choose more learning exemplars to improve their exploration ability,and excellent individuals can directly learn from the several best individuals to improve the quality of solutions.To accelerate the convergence speed,a difference vector selection method based on the level is developed.Furthermore,specific crossover rates are assigned to individuals at the lowest level to guarantee that the population can continue to update during the later evolutionary process.A comprehensive experiment is organized and conducted to obtain a deep insight into LBLDE and demonstrates the superiority of LBLDE in comparison with seven peer DE variants.展开更多
基金Supported by Defense Industrial Technology Development Program of China(Grant No.JCKY2021605B015).
文摘Welding deformation adversely affects the quality and precision of structural components,and traditional methods require significant material resources and time.Machine learning has demonstrated exceptional ac-curacy and efficiency in solving complex problems.Thus,the use of machine learning to predict welding de-formations is a novel approach.In this study,laser welding experiments were conducted on a TC4 titanium alloy to establish a welding deformation dataset.The deep neural network(DNN)and convolutional neural network(CNN)models were designed and constructed,with average prediction errors of 0.85 mm and 0.94 mm on the validation set,respectively.To further optimize the network parameters,a differential evolution algorithm was employed through mutation,crossover,and selection.The results indicated that after optimization,the pre-diction errors of the DNN and CNN models reduced to 0.75 mm and 0.85 mm,respectively.These represent accuracy improvements of 14.8%and 9.6%,respectively.The optimized models exhibited superior predictive performances for the validation set.
文摘To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine(ELM).Input weights and hidden layer biases were generated randomly in traditional ELM.To improve the estimation accuracy of ELM,the differential evolution algorithm was used to optimize these parameters in feasible solution spaces.First,incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests.Then,the ELM based on differential evolution algorithm(DE-ELM model)was used for a lithium-ion battery SOH estimation.At last,four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model.Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.
基金This paper is dedicated to Professor lan R. Petersen on the occasion of his 60th birthday. This work was supported by the National Natural Science Foundation of China （Nos. 61374092, 61432008）, the National Key Research and Development Program of China （No. 2016YFD0702100） and the Australian Research Council＇s Discovery Projects funding scheme under Project DP130101658.
文摘Learning control has been recognized as a powerful approach in quantum information technology. In this paper, we extend the application of differential evolution （DE） to design optimal control for various quantum systems. Various DE methods are introduced and analyzed, and EMSDE featuring in equally mixed strategies is employed for quantum control. Two classes of quantum control problems, including control of four-level open quantum ensembles and quantum superconducting systems, are investigated to demonstrate the performance of EMSDE for learning control of quantum systems. Numerical results verify the effectiveness of the FMSDE method for various quantum systems and show the potential for complex quantum control problems.
文摘Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated.It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction.Early detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this condition.However,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per patient.To overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for training.Developing this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in Tehran.To address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority class.Additionally,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training phase.The effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance metrics.Overall,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.
基金supported by Natural Science Foundation of Shandong Province(Grant No.ZR202212010161)Natural Science Foundation of Qingdao(Grant No.23-2-1-83-zyyd-jch)+1 种基金Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515110116)the National Natural Science Foundation of China(Grant No.52405359).
文摘Wire-fed laser-arc directed energy deposition(Wire-fed LA-DED)Technol.improves production speed while maintaining high quality and is particularly suited for manufacturing large,complex aluminum or titanium alloy components.The geometry of the weld bead(height and width)is influenced by multiple intricate parameters and variables during the manufacturing process.Accurately predicting the weld bead shape enables precise control over the surface flatness of the part,helping to prevent defects such as lack of fusion.This significantly reduces dimensional redundancy,enhances printing efficiency,and optimizes material usage.In this study,a quadratic regression prediction model for weld bead geometry was developed using the response surface methodology(RSM),with predictions generated through several machine learning models.These models included the back-propagation neural network(BPNN),support vector regression(SVR),multi-output support vector regression(MOSVR),extreme learning machine(ELM),and a differential evolution-optimized MOSVR(DE-MOSVR)model.Grid search and cross-validation techniques were utilized to identify the optimal parameters for each model to achieve the best predictive performance.A comparison of these models was conducted,followed by an evaluation of their generalization capabilities using an additional 20 sets of test data.The most accurate predictive model was selected based on a comprehensive assessment.The results showed that the DE-MOSVR model outperformed the others,achieving mean squared error,root mean squared error,mean absolute error,and R^(2) values for width(height)predictions of 0.0411(0.0041),0.2028(0.0639),0.1671(0.0550),and 0.9434(0.9433),respectively.It demonstrated the smallest deviation in the validation set,with mean deviations of 1.97% and 1.68%,respectively.The model we developed was validated through the production of prototype parts,providing valuable reference and guidance for predicting and modeling weld bead morphology in the Wire-fed LA-DED process.
文摘Adaptive Cross-Generation Differential Evolution(ACGDE)is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms(EAs).However,its convergence and diversity are not satisfactory compared with the latest algorithms.In order to adapt to the current environment,ACGDE requires improvements in many aspects,such as its initialization and mutant operator.In this paper,an enhanced version is proposed,namely SIACGDE.It incorporates a strengthened initialization strategy and optimized parameters in contrast to its predecessor.These improvements make the direction of crossgeneration mutation more clearly and the ability of searching more efficiently.The experiments show that the new algorithm has better diversity and improves convergence to a certain extent.At the same time,SIACGDE outperforms other state-of-the-art algorithms on four metrics of 24 test problems.
基金Ministry of Education and Training of Vietnam,Grant No.B2020-GHA-03the University of Transport and Communications,Hanoi,Vietnam.
文摘One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil.In this study,the main purpose is to develop a novel hybrid Machine Learning(ML)model(ANFIS-DE),which used Differential Evolution(DE)algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System(ANFIS),for estimating soil Compression coefficient(Cc)from other geotechnical parameters namelyWater Content,Void Ratio,SpecificGravity,Liquid Limit,Plastic Limit,Clay content and Depth of Soil Samples.Validation of the predictive capability of the novel model was carried out using statistical indices:Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Correlation Coefficient(R).In addition,two popular ML models namely Reduced Error Pruning Trees(REPTree)and Decision Stump(Dstump)were used for comparison.Results showed that the performance of the novel model ANFIS-DE is the best(R=0.825,MAE=0.064 and RMSE=0.094)in comparison to other models such as REPTree(R=0.7802,MAE=0.068 and RMSE=0.0988)andDstump(R=0.7325,MAE=0.0785 and RMSE=0.1036).Therefore,the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc,which can be employed in the design and construction of civil engineering structures.
基金funded by Hanoi University of Civil Engineering(HUCE)in Project Code 35-2021/KHXD-TD.
文摘Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses thatmust be conducted.Building a surrogatemodel to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem.However,most existing surrogate models have been designed based on regression techniques.This paper proposes a novel method,called CaDE,which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution(DE)optimization.The proposed method is separated into two stages.During the first optimization stage,the original DE is implemented as usual,but all individuals produced in this phase are stored as inputs of the training data.Based on design constraints verification,these individuals are labeled as“safe”or“unsafe”and their labels are saved as outputs of the training data.When collecting enough data,an AdaBoost model is trained to evaluate the safety state of structures.This model is then used in the second stage to preliminarily assess new individuals,and unpromising ones are rejected without checking design constraints.This method reduces unnecessary structural analyses,thereby shortens the optimization process.Five benchmark truss sizing optimization problems are solved using the proposed method to demonstrate its effectiveness.The obtained results show that the CaDE finds good optimal designs with less structural analyses in comparison with the original DE and four other DE variants.The reduction rate of five examples ranges from 18 to over 50%.Moreover,the proposed method is applied to a real-size transmission tower design problem to exhibit its applicability in practice.
基金Project supported by the National Key R&D Program of China(No.2021YFF0501001)the National Natural Science Foundation of China(Nos.52308315,51922046,and 52192661)+3 种基金the Research Funds of Huazhong University of Science and Technology(No.2023JCYJ014)the China Postdoctoral Science Foundation(No.2023M731206)the Research Funds of China Railway Siyuan Survey and Design Group Co.Ltd.(Nos.KY2023014S,KY2023126S,2021K085,2020K006,and 2020K172)the Autonomous Innovation Fund of Hubei Province of China(No.5003242027)。
文摘Optimizing wind energy harvesting performance remains a significant challenge.Machine learning(ML)offers a promising approach for addressing this challenge.This study proposes an ML-based approach using the radial basis function neural network(RBFNN)and differential evolution(DE)to predict and optimize the structural parameters(the diameter of the spherical bluff body D,the total spring stiffness k,and the length of the piezoelectric cantilever beam L)of the wind energy harvester(WEH).The RBFNN model is trained with theoretical data and validated with wind tunnel experimental results,achieving the coefficient-of-determination scores R2of 97.8%and 90.3%for predicting the average output power Pavgand aero-electro-mechanical efficiencyηaem,respectively.The DE algorithm is used to identify the optimal parameter combinations for wind speeds U ranging from 2.5 m/s to 6.5 m/s.The maximum Pavgis achieved when D=57.5 mm,k=28.8 N/m,L=112.1 mm,and U=4.6 m/s,while the maximumηaemis achieved when D=52.7 mm,k=29.2 N/m,L=89.2 mm,and U=4.7 m/s.Compared with that of the non-optimized structure,the WEH performance is improved by 28.6%in P_(avg)and 19.1%inη_(aem).
基金supported by the National Natural Science Foundation of China(No.52105244)the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems(No.GZKF-202425)+1 种基金the Entrepreneurship and Innovation Support Plan of Chongqing for Returned Overseas Scholars(No.cx2023085)the Independent Research Project-Key Program from the State Key Laboratory of Mechanical Transmission for Advanced Equipment(No.SKLMT-ZZKT-2024Z09).
文摘The mutation operations and related control parameters play important roles in the performance of the differential evolution algorithm.Learning optimal policies for these strategies and parameters through reinforcement learning is a hot topic.However,most of the current studies focus on either mutation strategy selection or the control parameters alone while the others keep fixed or self-adaptive,resulting in deteriorated performances.To address this gap,this paper proposes a framework for the joint adaptation of mutation strategies and related control parameters based on deep reinforcement learning.In this method,the distributed proximal policy optimization algorithm is employed to train the agents to dynamically select the optimal combination of mutation strategies and control parameters.To enhance the agent’s learning of the optimal policy,information derived from fitness landscape analysis is incorporated into the state representations.The training is conducted on the black-box optimization benchmark test problems,which are capable of generating large-scale test instances.Numerical results on the new problems from CEC2013 and CEC2017 test suites,and the real-world application of rover trajectory planning demonstrate that the proposed approach achieves competitive performance compared to state-of-the-art methods.The adaptation behavior and the contribution of learning are also thoroughly analyzed.
基金Supported by the National Natural Science Foundation of China(Grant No.60374069)the Foundation of the Key Laboratory of Complex Systems and Intelligent Science,Institute of Automation,Chinese Academy of Sciences(Grant No.20060104)
文摘This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions,by combining the advantages of two powerful population-based metaheuristics differential evolution(DE)and particle swarm optimization(PSO).In the hybrid denoted by DEPSO,each individual in one generation chooses its evolution method,DE or PSO,in a statistical learning way.The choice depends on the relative success ratio of the two methods in a previous learning period.The proposed DEPSO is compared with its PSO and DE parents,two advanced DE variants one of which is suggested by the originators of DE,two advanced PSO variants one of which is acknowledged as a recent standard by PSO community,and also a previous DEPSO.Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.
基金This work was partly supported by the Natural Science Foundation of Guangxi Province(No.2020JJA170038)Special Talent Project of Guangxi Science and Technology Base(No.GuiKe AD21220119)the High-Level Talents Research Project of Beibu Gulf(No.2020KYQD06)。
文摘Nonlinear equations systems(NESs)arise in a wide range of domains.Solving NESs requires the algorithm to locate multiple roots simultaneously.To deal with NESs efficiently,this study presents an enhanced reinforcement learning based differential evolution with the following major characteristics:(1)the design of state function uses the information on the fitness alternation action;(2)different neighborhood sizes and mutation strategies are combined as optional actions;and(3)the unbalanced assignment method is adopted to change the reward value to select the optimal actions.To evaluate the performance of our approach,30 NESs test problems and 18 test instances with different features are selected as the test suite.The experimental results indicate that the proposed approach can improve the performance in solving NESs,and outperform several state-of-the-art methods.
文摘This paper presents opposition-based differential evolution to determine the optimal hourly schedule of power generation in a hydrothermal system.Differential evolution(DE)is a population-based stochastic parallel search evolutionary algorithm.Opposition-based differential evolution has been used here to improve the effectiveness and quality of the solution.The proposed opposition-based differential evolution(ODE)employs opposition-based learning(OBL)for population initialization and also for generation jumping.The effectiveness of the proposed method has been verified on two test problems,two fixed head hydrothermal test systems and three hydrothermal multi-reservoir cascaded hydroelectric test systems having prohibited operating zones and thermal units with valve point loading.The results of the proposed approach are compared with those obtained by other evolutionary methods.It is found that the proposed opposition-based differential evolution based approach is able to provide better solution.
基金This work was supported by the National Natural Science Foundation of China(Nos.61903089 and 62066019)the Natural Science Foundation of Jiangxi Province(Nos.20202BABL202020 and 20202BAB202014)the National Key Research and Development Program of China(No.2020YFB1713700).
文摘The Differential Evolution(DE)algorithm,which is an efficient optimization algorithm,has been used to solve various optimization problems.In this paper,adaptive dimensional learning with a tolerance framework for DE is proposed.The population is divided into an elite subpopulation,an ordinary subpopulation,and an inferior subpopulation according to the fitness values.The ordinary and elite subpopulations are used to maintain the current evolution state and to guide the evolution direction of the population,respectively.The inferior subpopulation learns from the elite subpopulation through the dimensional learning strategy.If the global optimum is not improved in a specified number of iterations,a tolerance mechanism is applied.Under the tolerance mechanism,the inferior and elite subpopulations implement the restart strategy and the reverse dimensional learning strategy,respectively.In addition,the individual status and algorithm status are used to adaptively adjust the control parameters.To evaluate the performance of the proposed algorithm,six state-of-the-art DE algorithm variants are compared on the benchmark functions.The results of the simulation show that the proposed algorithm outperforms other variant algorithms regarding function convergence rate and solution accuracy.
基金This work was supported in part by the National Natural Science Fund for Outstanding Young Scholars of China(No.61922072)the National Natural Science Foundation of China(Nos.61876169,61276238,61806179,and 61976237)Key Research and Development and Promotion Projects in Henan Province(No.192102210098).
文摘To address complex single objective global optimization problems,a new Level-Based Learning Differential Evolution(LBLDE)is developed in this study.In this approach,the whole population is sorted from the best to the worst at the beginning of each generation.Then,the population is partitioned into multiple levels,and different levels are used to exert different functions.In each level,a control parameter is used to select excellent exemplars from upper levels for learning.In this case,the poorer individuals can choose more learning exemplars to improve their exploration ability,and excellent individuals can directly learn from the several best individuals to improve the quality of solutions.To accelerate the convergence speed,a difference vector selection method based on the level is developed.Furthermore,specific crossover rates are assigned to individuals at the lowest level to guarantee that the population can continue to update during the later evolutionary process.A comprehensive experiment is organized and conducted to obtain a deep insight into LBLDE and demonstrates the superiority of LBLDE in comparison with seven peer DE variants.
