A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm(AM-SSA),called AMSSAElman-AdaBoost,is proposed for predicting the existing metro tunnel deformation induced by adjacent ...A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm(AM-SSA),called AMSSAElman-AdaBoost,is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground.The novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and exploration.In AM-SSA,firstly,the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow,enhancing the global search ability.Then the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered,expanding the local search ability.Finally,the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local optimal.In addition,it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original SSA.To demonstrate the effectiveness and reliability of AM-SSA,23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions(CEC2005),are employed as the numerical examples and investigated in comparison with some wellknown optimization algorithms.The statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search spaces.By utilizing the AdaBoost algorithm,multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction output.Additionally,the on-site monitoring data acquired from a deep excavation project in Ningbo,China,were selected as the training and testing sample.Meanwhile,the predictive outcomes are compared with those of other different optimization and machine learning techniques.In the end,the obtained results in this real-world geotechnical engineering field reveal the feasibility of the proposed hybrid algorithm model,illustrating its power and superiority in terms of computational efficiency,accuracy,stability,and robustness.More critically,by observing data in real time on daily basis,the structural safety associated with metro tunnels could be supervised,which enables decision-makers to take concrete control and protection measures.展开更多
The validity of the ant colony algorithm has been demonstrated as a powerful tool solving the optimization. An ant colony optimization algorithm based on mutation and dynamic pheromone updating in this paper was appli...The validity of the ant colony algorithm has been demonstrated as a powerful tool solving the optimization. An ant colony optimization algorithm based on mutation and dynamic pheromone updating in this paper was applied to settle job shop scheduling problem. Result of computer simulation shows that this method is effective.展开更多
To address the issue of accurately extracting fault characteristic information of railway freight car bearings under noisy conditions,this paper proposes a fault diagnosis method based on Adaptive Chirp Mode Decomposi...To address the issue of accurately extracting fault characteristic information of railway freight car bearings under noisy conditions,this paper proposes a fault diagnosis method based on Adaptive Chirp Mode Decomposition(ACMD)and an optimized Maximum Correlation Kurtosis Deconvolution(MCKD)using a Sparrow Search Algorithm Combining Sine-Cosine and Cauchy Mutation(SCSSA).Firstly,ACMD is used to decompose and reconstruct the original fault signal to obtain several Intrinsic Mode Functions(IMFs).Then,the IMFs are filtered according to the Gini coefficient indicator,with the IMF having the largest Gini coefficient selected as the optimal component.Secondly,the SCSSA is employed to iteratively optimize the filter length L,fault signal period T,and displacement parameter M in the MCKD algorithm,determining the optimal parameter combination for MCKD.This avoids the limitations of manual settings and enhances the accuracy of fault diagnosis.The optimized MCKD is then applied to the optimal component,and deconvolution is performed using maximum correlation kurtosis as the criterion to extract fault characteristic information through its envelope spectrum.To verify the effectiveness and generalizability of the proposed method,simulations,experimental signals from the Case Western Reserve University Bearing Center,and actual measured signals from railway freight car bearing 353130B are used to analyze inner ring faults.The experimental results demonstrate that the method can accurately extract fault characteristic information of railway freight car bearings under noise interference and identify the fault type.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52125803).
文摘A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm(AM-SSA),called AMSSAElman-AdaBoost,is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground.The novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and exploration.In AM-SSA,firstly,the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow,enhancing the global search ability.Then the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered,expanding the local search ability.Finally,the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local optimal.In addition,it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original SSA.To demonstrate the effectiveness and reliability of AM-SSA,23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions(CEC2005),are employed as the numerical examples and investigated in comparison with some wellknown optimization algorithms.The statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search spaces.By utilizing the AdaBoost algorithm,multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction output.Additionally,the on-site monitoring data acquired from a deep excavation project in Ningbo,China,were selected as the training and testing sample.Meanwhile,the predictive outcomes are compared with those of other different optimization and machine learning techniques.In the end,the obtained results in this real-world geotechnical engineering field reveal the feasibility of the proposed hybrid algorithm model,illustrating its power and superiority in terms of computational efficiency,accuracy,stability,and robustness.More critically,by observing data in real time on daily basis,the structural safety associated with metro tunnels could be supervised,which enables decision-makers to take concrete control and protection measures.
文摘The validity of the ant colony algorithm has been demonstrated as a powerful tool solving the optimization. An ant colony optimization algorithm based on mutation and dynamic pheromone updating in this paper was applied to settle job shop scheduling problem. Result of computer simulation shows that this method is effective.
文摘To address the issue of accurately extracting fault characteristic information of railway freight car bearings under noisy conditions,this paper proposes a fault diagnosis method based on Adaptive Chirp Mode Decomposition(ACMD)and an optimized Maximum Correlation Kurtosis Deconvolution(MCKD)using a Sparrow Search Algorithm Combining Sine-Cosine and Cauchy Mutation(SCSSA).Firstly,ACMD is used to decompose and reconstruct the original fault signal to obtain several Intrinsic Mode Functions(IMFs).Then,the IMFs are filtered according to the Gini coefficient indicator,with the IMF having the largest Gini coefficient selected as the optimal component.Secondly,the SCSSA is employed to iteratively optimize the filter length L,fault signal period T,and displacement parameter M in the MCKD algorithm,determining the optimal parameter combination for MCKD.This avoids the limitations of manual settings and enhances the accuracy of fault diagnosis.The optimized MCKD is then applied to the optimal component,and deconvolution is performed using maximum correlation kurtosis as the criterion to extract fault characteristic information through its envelope spectrum.To verify the effectiveness and generalizability of the proposed method,simulations,experimental signals from the Case Western Reserve University Bearing Center,and actual measured signals from railway freight car bearing 353130B are used to analyze inner ring faults.The experimental results demonstrate that the method can accurately extract fault characteristic information of railway freight car bearings under noise interference and identify the fault type.
