This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow an...This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow and Eagle Optimization Algorithm (HS-SBOA) is proposed. Initially, the algorithm employs Iterative Mapping to generate an initial sparrow and eagle population, enhancing the diversity of the population during the global search phase. Subsequently, an adaptive weighting strategy is introduced during the exploration phase of the algorithm to achieve a balance between exploration and exploitation. Finally, to avoid the algorithm falling into local optima, a Cauchy mutation operation is applied to the current best individual. To validate the performance of the HS-SBOA algorithm, it was applied to the CEC2021 benchmark function set and three practical engineering problems, and compared with other optimization algorithms such as the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) to test the effectiveness of the improved algorithm. The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved strategies.展开更多
Copy number variation(CNV)is a remarkable manifestation of genomic structural variations that affect human health.However,CNV detection in low coverage and low purity data is one of the challenging issues.To fill this...Copy number variation(CNV)is a remarkable manifestation of genomic structural variations that affect human health.However,CNV detection in low coverage and low purity data is one of the challenging issues.To fill this gap,a hybrid algorithm combining an improved whale optimization algorithm(IwOA)and backpropagation(BP)neural networks(hereafter called IWOABP)is developed for CNV detection.First,to enhance the precision of detection,the detectable categories for the gain and loss are respectively expanded to two types,where gain is divided into tand_gain and inte_gain,and loss is divided into hemi_loss and homo_loss.Then,IWOA is introduced to tune the weights and bias values of BP neural network,which can improve the BP neural network abilities to jump out of the local optimums.Next,to ensure the population diversity and the uniform distribution of solutions,a pooling mechanism and a migration search strategy are designed.In addition,to balance the exploitation and exploration abilities,three position update strategies based on an adaptive inertia-weight are used.Finally,to evaluate the detection performance of IwOABP,seven state-of-the-art detection methods are chosen to make detailed comparisons with the proposed algorithm.The results show that IWOABP has outstanding performance in sensitivity,precision,and Fl-score using both simulated and real data.展开更多
In order to further analyze the micro-motion modulation signals generated by rotating components and extract micro-motion features,a modulation signal denoising algorithm based on improved variational mode decompositi...In order to further analyze the micro-motion modulation signals generated by rotating components and extract micro-motion features,a modulation signal denoising algorithm based on improved variational mode decomposition(VMD)is proposed.To improve the time-frequency performance,this method decomposes the data into narrowband signals and analyzes the internal energy and frequency variations within the signal.Genetic algorithms are used to adaptively optimize the mode number and bandwidth control parameters in the process of VMD.This approach aims to obtain the optimal parameter combination and perform mode decomposition on the micro-motion modulation signal.The optimal mode number and quadratic penalty factor for VMD are determined.Based on the optimal values of the mode number and quadratic penalty factor,the original signal is decomposed using VMD,resulting in optimal mode number intrinsic mode function(IMF)components.The effective modes are then reconstructed with the denoised modes,achieving signal denoising.Through experimental data verification,the proposed algorithm demonstrates effective denoising of modulation signals.In simulation data validation,the algorithm achieves the highest signal-to-noise ratio(SNR)and exhibits the best performance.展开更多
文摘This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow and Eagle Optimization Algorithm (HS-SBOA) is proposed. Initially, the algorithm employs Iterative Mapping to generate an initial sparrow and eagle population, enhancing the diversity of the population during the global search phase. Subsequently, an adaptive weighting strategy is introduced during the exploration phase of the algorithm to achieve a balance between exploration and exploitation. Finally, to avoid the algorithm falling into local optima, a Cauchy mutation operation is applied to the current best individual. To validate the performance of the HS-SBOA algorithm, it was applied to the CEC2021 benchmark function set and three practical engineering problems, and compared with other optimization algorithms such as the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) to test the effectiveness of the improved algorithm. The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved strategies.
基金supported by the National Science Foundation of China(Nos.62473331 and 62173216)Key projects of Yunnan Province Basic Research Program(No.202401AS070036)Yunnan Key Laboratory of Modern Analytical Mathematics and Applications(No.202302AN360007).
文摘Copy number variation(CNV)is a remarkable manifestation of genomic structural variations that affect human health.However,CNV detection in low coverage and low purity data is one of the challenging issues.To fill this gap,a hybrid algorithm combining an improved whale optimization algorithm(IwOA)and backpropagation(BP)neural networks(hereafter called IWOABP)is developed for CNV detection.First,to enhance the precision of detection,the detectable categories for the gain and loss are respectively expanded to two types,where gain is divided into tand_gain and inte_gain,and loss is divided into hemi_loss and homo_loss.Then,IWOA is introduced to tune the weights and bias values of BP neural network,which can improve the BP neural network abilities to jump out of the local optimums.Next,to ensure the population diversity and the uniform distribution of solutions,a pooling mechanism and a migration search strategy are designed.In addition,to balance the exploitation and exploration abilities,three position update strategies based on an adaptive inertia-weight are used.Finally,to evaluate the detection performance of IwOABP,seven state-of-the-art detection methods are chosen to make detailed comparisons with the proposed algorithm.The results show that IWOABP has outstanding performance in sensitivity,precision,and Fl-score using both simulated and real data.
文摘In order to further analyze the micro-motion modulation signals generated by rotating components and extract micro-motion features,a modulation signal denoising algorithm based on improved variational mode decomposition(VMD)is proposed.To improve the time-frequency performance,this method decomposes the data into narrowband signals and analyzes the internal energy and frequency variations within the signal.Genetic algorithms are used to adaptively optimize the mode number and bandwidth control parameters in the process of VMD.This approach aims to obtain the optimal parameter combination and perform mode decomposition on the micro-motion modulation signal.The optimal mode number and quadratic penalty factor for VMD are determined.Based on the optimal values of the mode number and quadratic penalty factor,the original signal is decomposed using VMD,resulting in optimal mode number intrinsic mode function(IMF)components.The effective modes are then reconstructed with the denoised modes,achieving signal denoising.Through experimental data verification,the proposed algorithm demonstrates effective denoising of modulation signals.In simulation data validation,the algorithm achieves the highest signal-to-noise ratio(SNR)and exhibits the best performance.
