Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical ...Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information, a new multimodality medical image fusion method(NSST-PAPCNNLatLRR) is proposed in this paper. Firstly, the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST. Then, the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients. The improved PAPCNN model was based on the automatic setting of the parameters, and the optimal method was configured for the time decay factor αe. The experimental results show that, in comparison with the five mainstream fusion algorithms, the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images, and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in six objective indexes.展开更多
Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image ...Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods,a new multimodal medical image fusion method is proposed.This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients,then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients,and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients.Finally,based on the automatic setting of parameters,the optimization method configuration of the time decay factorαe is carried out.The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images,and at the same time,it has achieved great improvement in visual quality and objective evaluation indicators.展开更多
Edge is the key information in the process of image smoothing. Some edges, especially the weak edges, are difficult to maintain, which result in the local area being over-smoothed. For the protection of weak edges, we...Edge is the key information in the process of image smoothing. Some edges, especially the weak edges, are difficult to maintain, which result in the local area being over-smoothed. For the protection of weak edges, we propose an image smoothing algorithm based on global sparse structure and parameter adaptation. The algorithm decomposes the image into high frequency and low frequency part based on global sparse structure. The low frequency part contains less texture information which is relatively easy to smoothen. The high frequency part is more sensitive to edge information so it is more suitable for the selection of smoothing parameters. To reduce the computational complexity and improve the effect, we propose a bicubic polynomial fitting method to fit all the sample values into a surface. Finally, we use Alternating Direction Method of Multipliers (ADMM) to unify the whole algorithm and obtain the smoothed results by iterative optimization. Compared with traditional methods and deep learning methods, as well as the application tasks of edge extraction, image abstraction, pseudo-boundary removal, and image enhancement, it shows that our algorithm can preserve the local weak edge of the image more effectively, and the visual effect of smoothed results is better.展开更多
As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly evident.However,the challenge lies in identifying the right parameters and strategies for th...As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly evident.However,the challenge lies in identifying the right parameters and strategies for these algorithms.In this paper,we introduce the adaptive multi-strategy Rabbit Algorithm(RA).RA is inspired by the social interactions of rabbits,incorporating elements such as exploration,exploitation,and adaptation to address optimization challenges.It employs three distinct subgroups,comprising male,female,and child rabbits,to execute a multi-strategy search.Key parameters,including distance factor,balance factor,and learning factor,strike a balance between precision and computational efficiency.We offer practical recommendations for fine-tuning five essential RA parameters,making them versatile and independent.RA is capable of autonomously selecting adaptive parameter settings and mutation strategies,enabling it to successfully tackle a range of 17 CEC05 benchmark functions with dimensions scaling up to 5000.The results underscore RA’s superior performance in large-scale optimization tasks,surpassing other state-of-the-art metaheuristics in convergence speed,computational precision,and scalability.Finally,RA has demonstrated its proficiency in solving complicated optimization problems in real-world engineering by completing 10 problems in CEC2020.展开更多
The probability hypothesis density(PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledg...The probability hypothesis density(PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation(APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter(PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches.展开更多
By the improvement of Riks’and Crisfield’s arc-length method,the adaptiveparameter incremental method is preasted for predicting the snapping response ofstructures. Its justification is fulfilled. Finally,the effect...By the improvement of Riks’and Crisfield’s arc-length method,the adaptiveparameter incremental method is preasted for predicting the snapping response ofstructures. Its justification is fulfilled. Finally,the effectiveness of this method isdemonstrated by solving the snapping response of spherical caps subjected to centrallydistributed pressures.展开更多
Digital images have been applied to various areas such as evidence in courts.However,it always suffers from noise by criminals.This type of computer network security has become a hot issue that can’t be ignored.In th...Digital images have been applied to various areas such as evidence in courts.However,it always suffers from noise by criminals.This type of computer network security has become a hot issue that can’t be ignored.In this paper,we focus on noise removal so as to provide guarantees for computer network security.Firstly,we introduce a well-known denoising method called Expected Patch Log Likelihood(EPLL)with Gaussian Mixture Model as its prior.This method achieves exciting results in noise removal.However,there remain problems to be solved such as preserving the edge and meaningful details in image denoising,cause it considers a constant as regularization parameter so that we denoise with the same strength on the whole image.This leads to a problem that edges and meaningful details may be oversmoothed.Under the consideration of preserving edges of the image,we introduce a new adaptive parameter selection based on EPLL by the use of the image gradient and variance,which varies with different regions of the image.Moreover,we add a gradient fidelity term to relieve staircase effect and preserve more details.The experiment shows that our proposed method proves the effectiveness not only in vision but also on quantitative evaluation.展开更多
In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLS...In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.展开更多
Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local s...Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local search abilities, and therefore the performance of PSO. In this work, an adaptive parallel PSO algorithm, which is based on the dynamic exchange of control parameters between adjacent swarms, has been developed. The proposed PSO algorithm enables us to adaptively optimize inertia factors, learning factors and swarm activity. By performing simulations of a search for the global minimum of a benchmark multimodal function, we have found that the proposed PSO successfully provides appropriate control parameter values, and thus good global optimization performance.展开更多
The merits of compressed air energy storage(CAES)include large power generation capacity,long service life,and environmental safety.When a CAES plant is switched to the grid-connected mode and participates in grid reg...The merits of compressed air energy storage(CAES)include large power generation capacity,long service life,and environmental safety.When a CAES plant is switched to the grid-connected mode and participates in grid regulation,using the traditional control mode with low accuracy can result in excess grid-connected impulse current and junction voltage.This occurs because the CAES output voltage does not match the frequency,amplitude,and phase of the power grid voltage.Therefore,an adaptive linear active disturbance-rejection control(A-LADRC)strategy was proposed.Based on the LADRC strategy,which is more accurate than the traditional proportional integral controller,the proposed controller is enhanced to allow adaptive adjustment of bandwidth parameters,resulting in improved accuracy and response speed.The problem of large impulse current when CAES is switched to the grid-connected mode is addressed,and the frequency fluctuation is reduced.Finally,the effectiveness of the proposed strategy in reducing the impact of CAES on the grid connection was verified using a hardware-in-the-loop simulation platform.The influence of the k value in the adaptive-adjustment formula on the A-LADRC was analyzed through simulation.The anti-interference performance of the control was verified by increasing and decreasing the load during the presynchronization process.展开更多
To tackle the path planning problem,this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution(TPADE).This algorithm draws inspiration from group behavior to implement a...To tackle the path planning problem,this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution(TPADE).This algorithm draws inspiration from group behavior to implement a two-stage scaling factor variation strategy.In the initial phase,it adapts according to environmental complexity.In the following phase,it combines individual and global experiences to fine-tune the orientation factor,effectively improving its global search capability.Furthermore,this study developed a new population update method,ensuring that well-adapted individuals are retained,which enhances population diversity.In benchmark function tests across different dimensions,the proposed algorithm consistently demonstrates superior convergence accuracy and speed.This study also tested the TPADE algorithm in path planning simulations.The experimental results reveal that the TPADE algorithm outperforms existing algorithms by achieving path lengths of 28.527138 and 31.963990 in simple and complex map environments,respectively.These findings indicate that the proposed algorithm is more adaptive and efficient in path planning.展开更多
In modern vehicles, electronic throttle(ET) has been widely utilized to control the airflow into gasoline engine. To solve the control difficulties with an ET, such as strong nonlinearity,unknown model parameters and ...In modern vehicles, electronic throttle(ET) has been widely utilized to control the airflow into gasoline engine. To solve the control difficulties with an ET, such as strong nonlinearity,unknown model parameters and input saturation constraints,an adaptive sliding-mode tracking control strategy for an ET is presented. Compared with the existing control strategies for an ET, input saturation constraints and parameter uncertainties are adequately considered in the proposed control strategy. At first, the nonlinear dynamic model for control of an ET is described. According to the dynamical model, the nonlinear adaptive sliding-mode tracking control method is presented,where parameter adaptive laws and auxiliary design system are employed. Parameter adaptive law is given to estimate the unknown parameter with an ET. An auxiliary system is designed,and its state is utilized in the tracking control method to handle the input saturation. Stability proof and analysis of the adaptive sliding-mode control method is performed by using Lyapunov stability theory. Finally, the reliability and feasibility of the proposed control strategy are evaluated by computer simulation.Simulation research shows that the proposed sliding-mode control strategy can provide good control performance for an ET.展开更多
Since the problems of branch loss and fracture in retinal blood vessel segmentation algorithms,an image segmentation method is proposed based on improved pulse coupled neural network(PCNN)and gray wolf optimization al...Since the problems of branch loss and fracture in retinal blood vessel segmentation algorithms,an image segmentation method is proposed based on improved pulse coupled neural network(PCNN)and gray wolf optimization algorithm(GWO).Simplifying the neuron input domain and neuron connection domain of the PCNN network,increasing the gradient information factor in the internal activity items,reducing the model parameters,enhancing the pulse issuing ability,and the optimal parameters of the network are automatically obtained based on multiple feature evaluation criteria and the GWO algorithm.The test in the public data set drive shows that the sensitivity,accuracy,precision,and specificity of the algorithm are 0.799549,0.962789,0.889163,and 0.986552,respectively.The accuracy and specificity are better than the classical segmentation algorithm.It solved the influence of low illumination,optic disc highlight,and foveal shadow on vascular segmentation,and showed excellent performance of vessel connectivity and terminal sensitivity.展开更多
Biological neurons can be excited to maintain certain firing patterns following different external stimuli,and similar changes in electrical activities can be reproduced in some neural circuits by applying an external...Biological neurons can be excited to maintain certain firing patterns following different external stimuli,and similar changes in electrical activities can be reproduced in some neural circuits by applying an external voltage.Generic neural circuits are composed of capacitors,induction coils,resistors,and nonlinear resistors,and continuous energy exchange between the capacitive and inductive components is crucial for preserving output voltages.Incorporating nonlinear elements causes interactions between the charge flow across the capacitor and the induced electromotive force on the inductor.It is a challenge to explore the occurrence of nonlinear oscillation and coherence resonance in a neural circuit without using a capacitor and nonlinear resistor,and it considers the case lack of electric field energy.In this paper,a simple neural circuit is proposed that combines two inductors,one magnetic flux-controlled memristor(MFCM),and three resistors,with two constant voltage sources in the branch circuits used as reverse potentials in the ion channels.The field energy has an exact form,and it is stored in the circuit components as a magnetic field.Scale transformation is applied on the circuit equations and field energy function to obtain equivalent dimensionless forms of the memristive neuron and Hamilton energy.The reference values for the physical time and capacitance are represented by an appropriate combination of resistance and inductance,because the capacitance value is unavailable.The memristive neuron without capacitive effect still shows similar firing patterns,and coherence resonance is induced under noisy excitation.The emergence of coherence resonance can be predicted by calculating the distribution of the average energy<H>versus noise intensity,and the value for<H>reaches a maximum under coherence resonance.Finally,an adaptive law for parameter growth under energy control is proposed to control mode transitions in the electrical activity.The methodology and results of this work offer insights into the oscillatory mechanism of neural circuits,and showcase how magnetic field control can be used to manage neural activations.展开更多
The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter stra...The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.展开更多
The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powe...The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.展开更多
The search efficiency of differential evolution （DE） algorithm is greatly impacted by its control parameters. Although many adaptation/self-adaptation techniques can automatically find suitable control parameters fo...The search efficiency of differential evolution （DE） algorithm is greatly impacted by its control parameters. Although many adaptation/self-adaptation techniques can automatically find suitable control parameters for the DE, most techniques are based on pop- ulation information which may be misleading in solving complex optimization problems. Therefore, a self-adaptive DE （i.e., JADE） using two-phase parameter control scheme （TPC-JADE） is proposed to enhance the performance of DE in the current study. In the TPC-JADE, an adaptation technique is utilized to generate the control parameters in the early population evolution, and a well-known empirical guideline is used to update the control parameters in the later evolution stages. The TPC-JADE is compared with four state-of-the-art DE variants on two famous test suites （i.e., IEEE CEC2005 and IEEE CEC2015）. Results indicate that the overall performance of the TPC-JADE is better than that of the other compared algorithms. In addition, the proposed algorithm is utilized to obtain optimal nutrient and inducer feeding for the Lee-Ramirez bioreactor. Experimental results show that the TPC-JADE can perform well on an actual dynamic optimization problem.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 challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adapti...One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adaptive differential evolution(CTbADE)algorithm is presented in this research paper.The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima.A more diverse population improves the global searching capability and helps to escape from the local optima problem.Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems.An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper.The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm’s exploration and exploitation capability.A comprehensive test suite of standard benchmark problems with different natures,i.e.,unimodal/multimodal and separable/non-separable,was used to test the convergence power of the proposed CTbADE algorithm.Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness,solution quality,and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms,such as jDE,CoDE,and EPSDE algorithms.This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computationalmodels with a high number of parameters to adjust during the problem-solving process.展开更多
In this paper,a third-generation dry gas-to-ethylbenzene process in a factory of PetroChina is considered.For the gradual catalyst deactivation in the alkylation reactor,a model is established with the parameters esti...In this paper,a third-generation dry gas-to-ethylbenzene process in a factory of PetroChina is considered.For the gradual catalyst deactivation in the alkylation reactor,a model is established with the parameters estimated from the reaction rate equation of alkylation based on the on-site data and those from laboratory analysis. The real-time dynamic simulation of the alkylation process is carried out,in which the module accuracy is ensured by using OPC(Object linking and embedding for Process Control)technique and adaptive correction of model parameters.Both the current and future operation temperature can be predicted.展开更多
基金funded by the National Natural Science Foundation of China,grant number 61302188.
文摘Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information, a new multimodality medical image fusion method(NSST-PAPCNNLatLRR) is proposed in this paper. Firstly, the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST. Then, the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients. The improved PAPCNN model was based on the automatic setting of the parameters, and the optimal method was configured for the time decay factor αe. The experimental results show that, in comparison with the five mainstream fusion algorithms, the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images, and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in six objective indexes.
文摘Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods,a new multimodal medical image fusion method is proposed.This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients,then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients,and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients.Finally,based on the automatic setting of parameters,the optimization method configuration of the time decay factorαe is carried out.The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images,and at the same time,it has achieved great improvement in visual quality and objective evaluation indicators.
文摘Edge is the key information in the process of image smoothing. Some edges, especially the weak edges, are difficult to maintain, which result in the local area being over-smoothed. For the protection of weak edges, we propose an image smoothing algorithm based on global sparse structure and parameter adaptation. The algorithm decomposes the image into high frequency and low frequency part based on global sparse structure. The low frequency part contains less texture information which is relatively easy to smoothen. The high frequency part is more sensitive to edge information so it is more suitable for the selection of smoothing parameters. To reduce the computational complexity and improve the effect, we propose a bicubic polynomial fitting method to fit all the sample values into a surface. Finally, we use Alternating Direction Method of Multipliers (ADMM) to unify the whole algorithm and obtain the smoothed results by iterative optimization. Compared with traditional methods and deep learning methods, as well as the application tasks of edge extraction, image abstraction, pseudo-boundary removal, and image enhancement, it shows that our algorithm can preserve the local weak edge of the image more effectively, and the visual effect of smoothed results is better.
文摘As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly evident.However,the challenge lies in identifying the right parameters and strategies for these algorithms.In this paper,we introduce the adaptive multi-strategy Rabbit Algorithm(RA).RA is inspired by the social interactions of rabbits,incorporating elements such as exploration,exploitation,and adaptation to address optimization challenges.It employs three distinct subgroups,comprising male,female,and child rabbits,to execute a multi-strategy search.Key parameters,including distance factor,balance factor,and learning factor,strike a balance between precision and computational efficiency.We offer practical recommendations for fine-tuning five essential RA parameters,making them versatile and independent.RA is capable of autonomously selecting adaptive parameter settings and mutation strategies,enabling it to successfully tackle a range of 17 CEC05 benchmark functions with dimensions scaling up to 5000.The results underscore RA’s superior performance in large-scale optimization tasks,surpassing other state-of-the-art metaheuristics in convergence speed,computational precision,and scalability.Finally,RA has demonstrated its proficiency in solving complicated optimization problems in real-world engineering by completing 10 problems in CEC2020.
基金supported by the National Natural Science Foundation of China (Nos. 61305017, 61304264)the Natural Science Foundation of Jiangsu Province (No. BK20130154)
文摘The probability hypothesis density(PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation(APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter(PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches.
文摘By the improvement of Riks’and Crisfield’s arc-length method,the adaptiveparameter incremental method is preasted for predicting the snapping response ofstructures. Its justification is fulfilled. Finally,the effectiveness of this method isdemonstrated by solving the snapping response of spherical caps subjected to centrallydistributed pressures.
基金This paper is partly supported by the National Natural Science Foundation of China(GRANT No.61672293).
文摘Digital images have been applied to various areas such as evidence in courts.However,it always suffers from noise by criminals.This type of computer network security has become a hot issue that can’t be ignored.In this paper,we focus on noise removal so as to provide guarantees for computer network security.Firstly,we introduce a well-known denoising method called Expected Patch Log Likelihood(EPLL)with Gaussian Mixture Model as its prior.This method achieves exciting results in noise removal.However,there remain problems to be solved such as preserving the edge and meaningful details in image denoising,cause it considers a constant as regularization parameter so that we denoise with the same strength on the whole image.This leads to a problem that edges and meaningful details may be oversmoothed.Under the consideration of preserving edges of the image,we introduce a new adaptive parameter selection based on EPLL by the use of the image gradient and variance,which varies with different regions of the image.Moreover,we add a gradient fidelity term to relieve staircase effect and preserve more details.The experiment shows that our proposed method proves the effectiveness not only in vision but also on quantitative evaluation.
基金National Natural Science Foundation of China,Grant No.52375264.
文摘In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.
文摘Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local search abilities, and therefore the performance of PSO. In this work, an adaptive parallel PSO algorithm, which is based on the dynamic exchange of control parameters between adjacent swarms, has been developed. The proposed PSO algorithm enables us to adaptively optimize inertia factors, learning factors and swarm activity. By performing simulations of a search for the global minimum of a benchmark multimodal function, we have found that the proposed PSO successfully provides appropriate control parameter values, and thus good global optimization performance.
基金supported by National Natural Science Foundation of China(Project No.52077079).
文摘The merits of compressed air energy storage(CAES)include large power generation capacity,long service life,and environmental safety.When a CAES plant is switched to the grid-connected mode and participates in grid regulation,using the traditional control mode with low accuracy can result in excess grid-connected impulse current and junction voltage.This occurs because the CAES output voltage does not match the frequency,amplitude,and phase of the power grid voltage.Therefore,an adaptive linear active disturbance-rejection control(A-LADRC)strategy was proposed.Based on the LADRC strategy,which is more accurate than the traditional proportional integral controller,the proposed controller is enhanced to allow adaptive adjustment of bandwidth parameters,resulting in improved accuracy and response speed.The problem of large impulse current when CAES is switched to the grid-connected mode is addressed,and the frequency fluctuation is reduced.Finally,the effectiveness of the proposed strategy in reducing the impact of CAES on the grid connection was verified using a hardware-in-the-loop simulation platform.The influence of the k value in the adaptive-adjustment formula on the A-LADRC was analyzed through simulation.The anti-interference performance of the control was verified by increasing and decreasing the load during the presynchronization process.
基金The National Natural Science Foundation of China(No.62272239,62303214)Jiangsu Agricultural Science and Tech-nology Independent Innovation Fund(No.SJ222051).
文摘To tackle the path planning problem,this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution(TPADE).This algorithm draws inspiration from group behavior to implement a two-stage scaling factor variation strategy.In the initial phase,it adapts according to environmental complexity.In the following phase,it combines individual and global experiences to fine-tune the orientation factor,effectively improving its global search capability.Furthermore,this study developed a new population update method,ensuring that well-adapted individuals are retained,which enhances population diversity.In benchmark function tests across different dimensions,the proposed algorithm consistently demonstrates superior convergence accuracy and speed.This study also tested the TPADE algorithm in path planning simulations.The experimental results reveal that the TPADE algorithm outperforms existing algorithms by achieving path lengths of 28.527138 and 31.963990 in simple and complex map environments,respectively.These findings indicate that the proposed algorithm is more adaptive and efficient in path planning.
基金partially supported by the National Natural Science Foundation of China(61773189)Natural Science Fundamental of Liaoning Province(20170540443)the Program for Liaoning Innovative Research Team in University(LT2016006)
文摘In modern vehicles, electronic throttle(ET) has been widely utilized to control the airflow into gasoline engine. To solve the control difficulties with an ET, such as strong nonlinearity,unknown model parameters and input saturation constraints,an adaptive sliding-mode tracking control strategy for an ET is presented. Compared with the existing control strategies for an ET, input saturation constraints and parameter uncertainties are adequately considered in the proposed control strategy. At first, the nonlinear dynamic model for control of an ET is described. According to the dynamical model, the nonlinear adaptive sliding-mode tracking control method is presented,where parameter adaptive laws and auxiliary design system are employed. Parameter adaptive law is given to estimate the unknown parameter with an ET. An auxiliary system is designed,and its state is utilized in the tracking control method to handle the input saturation. Stability proof and analysis of the adaptive sliding-mode control method is performed by using Lyapunov stability theory. Finally, the reliability and feasibility of the proposed control strategy are evaluated by computer simulation.Simulation research shows that the proposed sliding-mode control strategy can provide good control performance for an ET.
基金The 2019 Guangdong Province General College Youth Innovation Talent Project(2019GKQNCX009)。
文摘Since the problems of branch loss and fracture in retinal blood vessel segmentation algorithms,an image segmentation method is proposed based on improved pulse coupled neural network(PCNN)and gray wolf optimization algorithm(GWO).Simplifying the neuron input domain and neuron connection domain of the PCNN network,increasing the gradient information factor in the internal activity items,reducing the model parameters,enhancing the pulse issuing ability,and the optimal parameters of the network are automatically obtained based on multiple feature evaluation criteria and the GWO algorithm.The test in the public data set drive shows that the sensitivity,accuracy,precision,and specificity of the algorithm are 0.799549,0.962789,0.889163,and 0.986552,respectively.The accuracy and specificity are better than the classical segmentation algorithm.It solved the influence of low illumination,optic disc highlight,and foveal shadow on vascular segmentation,and showed excellent performance of vessel connectivity and terminal sensitivity.
基金supported by the National Natural Science Foundation of China(No.62361037).
文摘Biological neurons can be excited to maintain certain firing patterns following different external stimuli,and similar changes in electrical activities can be reproduced in some neural circuits by applying an external voltage.Generic neural circuits are composed of capacitors,induction coils,resistors,and nonlinear resistors,and continuous energy exchange between the capacitive and inductive components is crucial for preserving output voltages.Incorporating nonlinear elements causes interactions between the charge flow across the capacitor and the induced electromotive force on the inductor.It is a challenge to explore the occurrence of nonlinear oscillation and coherence resonance in a neural circuit without using a capacitor and nonlinear resistor,and it considers the case lack of electric field energy.In this paper,a simple neural circuit is proposed that combines two inductors,one magnetic flux-controlled memristor(MFCM),and three resistors,with two constant voltage sources in the branch circuits used as reverse potentials in the ion channels.The field energy has an exact form,and it is stored in the circuit components as a magnetic field.Scale transformation is applied on the circuit equations and field energy function to obtain equivalent dimensionless forms of the memristive neuron and Hamilton energy.The reference values for the physical time and capacitance are represented by an appropriate combination of resistance and inductance,because the capacitance value is unavailable.The memristive neuron without capacitive effect still shows similar firing patterns,and coherence resonance is induced under noisy excitation.The emergence of coherence resonance can be predicted by calculating the distribution of the average energy<H>versus noise intensity,and the value for<H>reaches a maximum under coherence resonance.Finally,an adaptive law for parameter growth under energy control is proposed to control mode transitions in the electrical activity.The methodology and results of this work offer insights into the oscillatory mechanism of neural circuits,and showcase how magnetic field control can be used to manage neural activations.
基金funded by the NationalKey Research and Development Program of China under Grant No.11974373.
文摘The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.
基金supported by the National Natural Science Foundation of China(61271250)
文摘The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.
基金supported by National Natural Science Foundation of China(Nos.61603244 and 41505001)Fundamental Research Funds for the Central Universities(No.222201717006)
文摘The search efficiency of differential evolution （DE） algorithm is greatly impacted by its control parameters. Although many adaptation/self-adaptation techniques can automatically find suitable control parameters for the DE, most techniques are based on pop- ulation information which may be misleading in solving complex optimization problems. Therefore, a self-adaptive DE （i.e., JADE） using two-phase parameter control scheme （TPC-JADE） is proposed to enhance the performance of DE in the current study. In the TPC-JADE, an adaptation technique is utilized to generate the control parameters in the early population evolution, and a well-known empirical guideline is used to update the control parameters in the later evolution stages. The TPC-JADE is compared with four state-of-the-art DE variants on two famous test suites （i.e., IEEE CEC2005 and IEEE CEC2015）. Results indicate that the overall performance of the TPC-JADE is better than that of the other compared algorithms. In addition, the proposed algorithm is utilized to obtain optimal nutrient and inducer feeding for the Lee-Ramirez bioreactor. Experimental results show that the TPC-JADE can perform well on an actual dynamic optimization problem.
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This work was supported by the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,which funded this research work through project number 959.
文摘One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adaptive differential evolution(CTbADE)algorithm is presented in this research paper.The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima.A more diverse population improves the global searching capability and helps to escape from the local optima problem.Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems.An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper.The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm’s exploration and exploitation capability.A comprehensive test suite of standard benchmark problems with different natures,i.e.,unimodal/multimodal and separable/non-separable,was used to test the convergence power of the proposed CTbADE algorithm.Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness,solution quality,and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms,such as jDE,CoDE,and EPSDE algorithms.This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computationalmodels with a high number of parameters to adjust during the problem-solving process.
文摘In this paper,a third-generation dry gas-to-ethylbenzene process in a factory of PetroChina is considered.For the gradual catalyst deactivation in the alkylation reactor,a model is established with the parameters estimated from the reaction rate equation of alkylation based on the on-site data and those from laboratory analysis. The real-time dynamic simulation of the alkylation process is carried out,in which the module accuracy is ensured by using OPC(Object linking and embedding for Process Control)technique and adaptive correction of model parameters.Both the current and future operation temperature can be predicted.
