Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability...Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.展开更多
hyperbolic model for the diffusion of proteins through the blood-cerebro spinal fluid(CSF)barrier revolutionized clinical neurochemistry thirty years ago.The regression curves were informally parametrized based on phy...hyperbolic model for the diffusion of proteins through the blood-cerebro spinal fluid(CSF)barrier revolutionized clinical neurochemistry thirty years ago.The regression curves were informally parametrized based on physiolog-ically-driven constraints.The current paper readdresses this issue with nu-merical optimization for unconstrained non-linear regression,implementing the Levenberg-Marquardt Algorithm(LMA).Astonishingly similar estimates are obtained,which reconfirms the concepts of H.Reiber proposed in 1990s.The LMA is discussed in the context of other optimization algorithms.展开更多
For density inversion of gravity anomaly data, once the inversion method is determined, the main factors affecting the inversion result are the inversion parameters and subdivision scheme. A set of reasonable inversio...For density inversion of gravity anomaly data, once the inversion method is determined, the main factors affecting the inversion result are the inversion parameters and subdivision scheme. A set of reasonable inversion parameters and subdivision scheme can, not only improve the inversion process efficiency, but also ensure inversion result accuracy. The gravity inversion method based on correlation searching and the golden section algorithm is an effective potential field inversion method. It can be used to invert 2D and 3D physical properties with potential data observed on flat or rough surfaces. In this paper, we introduce in detail the density inversion principles based on correlation searching and the golden section algorithm. Considering that the gold section algorithm is not globally optimized. we present a heuristic method to ensure the inversion result is globally optimized. With a series of model tests, we systematically compare and analyze the inversion result efficiency and accuracy with different parameters. Based on the model test results, we conclude the selection principles for each inversion parameter with which the inversion accuracy can be obviously improved.展开更多
This study sets up two new merit functions,which are minimized for the detection of real eigenvalue and complex eigenvalue to address nonlinear eigenvalue problems.For each eigen-parameter the vector variable is solve...This study sets up two new merit functions,which are minimized for the detection of real eigenvalue and complex eigenvalue to address nonlinear eigenvalue problems.For each eigen-parameter the vector variable is solved from a nonhomogeneous linear system obtained by reducing the number of eigen-equation one less,where one of the nonzero components of the eigenvector is normalized to the unit and moves the column containing that component to the right-hand side as a nonzero input vector.1D and 2D golden section search algorithms are employed to minimize the merit functions to locate real and complex eigenvalues.Simultaneously,the real and complex eigenvectors can be computed very accurately.A simpler approach to the nonlinear eigenvalue problems is proposed,which implements a normalization condition for the uniqueness of the eigenvector into the eigenequation directly.The real eigenvalues can be computed by the fictitious time integration method(FTIM),which saves computational costs compared to the one-dimensional golden section search algorithm(1D GSSA).The simpler method is also combined with the Newton iterationmethod,which is convergent very fast.All the proposed methods are easily programmed to compute the eigenvalue and eigenvector with high accuracy and efficiency.展开更多
As in the building of deep buried long tunnels,there are complicated conditions such as great deformation,high stress,multi-variables,high non-linearity and so on,the algorithm for structure optimization and its appli...As in the building of deep buried long tunnels,there are complicated conditions such as great deformation,high stress,multi-variables,high non-linearity and so on,the algorithm for structure optimization and its application in tunnel engineering are still in the starting stage. Along with the rapid development of highways across the country,it has become a very urgent task to be tackled to carry out the optimization design of the structure of the section of the tunnel to lessen excavation workload and to reinforce the support. Artificial intelligence demonstrates an extremely strong capability of identifying,expressing and disposing such kind of multiple variables and complicated non-linear relations. In this paper,a comprehensive consideration of the strategy of the selection and updating of the concentration and adaptability of the immune algorithm is made to replace the selection mode in the original genetic algorithm which depends simply on the adaptability value. Such an algorithm has the advantages of both the immune algorithm and the genetic algorithm,thus serving the purpose of not only enhancing the individual adaptability but maintaining the individual diversity as well. By use of the identifying function of the antigen memory,the global search capability of the immune genetic algorithm is raised,thereby avoiding the occurrence of the premature phenomenon. By optimizing the structure of the section of the Huayuan tunnel,the current excavation area and support design are adjusted. A conclusion with applicable value is arrived at. At a higher computational speed and a higher efficiency,the current method is verified to have advantages in the optimization computation of the tunnel project. This also suggests that the application of the immune genetic algorithm has a practical significance to the stability assessment and informationization design of the wall rock of the tunnel.展开更多
A new fuzzy optimization neural network model is proposed based on the Levenberg-Marquardt (LM) algorithm on account of the disadvantages of slow convergence of traditional fuzzy optimization neural network model. In ...A new fuzzy optimization neural network model is proposed based on the Levenberg-Marquardt (LM) algorithm on account of the disadvantages of slow convergence of traditional fuzzy optimization neural network model. In this new model,the gradient descent algorithm is replaced by the LM algorithm to obtain the minimum of output errors during network training,which changes the weights adjusting equations of the network and increases the training speed. Moreover,to avoid the results yielding to local minimum,the transfer function is also revised to sigmoid function. A case study is utilized to validate this new model,and the results reveal that the new model fast training speed and better forecasting capability.展开更多
To improve the thermal efficiency and reduce nitrogen oxides （NOx ） emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other relat...To improve the thermal efficiency and reduce nitrogen oxides （NOx ） emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other related parameters, dynamic radial basis function （RBF） artificial neural network （ANN） models for forecasting unburned carbon in fly ash and NO, emissions in flue gas ware developed in this paper, together with a multi-objective optimization system utilizing particle swarm optimization and Powell （PSO-Powell） algorithm. To validate the proposed approach, a series of field tests were conducted in a 350 MW power plant. The results indicate that PSO-Powell algorithm can improve the capability to search optimization solution of PSO algorithm, and the effectiveness of system. Its prospective application in the optimization of a pulverized coal （ PC ） fired boiler is presented as well.展开更多
The permanent magnet eddy current coupler(PMEC)solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems.It provides torque to ...The permanent magnet eddy current coupler(PMEC)solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems.It provides torque to the load and generates heat and losses,reducing its energy transfer efficiency.This issue has become an obstacle for PMEC to develop toward a higher power.This paper aims to improve the overall performance of PMEC through multi-objective optimization methods.Firstly,a PMEC modeling method based on the Levenberg-Marquardt back propagation(LMBP)neural network is proposed,aiming at the characteristics of the complex input-output relationship and the strong nonlinearity of PMEC.Then,a novel competition mechanism-based multi-objective particle swarm optimization algorithm(NCMOPSO)is proposed to find the optimal structural parameters of PMEC.Chaotic search and mutation strategies are used to improve the original algorithm,which improves the shortcomings of multi-objective particle swarm optimization(MOPSO),which is too fast to converge into a global optimum,and balances the convergence and diversity of the algorithm.In order to verify the superiority and applicability of the proposed algorithm,it is compared with several popular multi-objective optimization algorithms.Applying them to the optimization model of PMEC,the results show that the proposed algorithm has better comprehensive performance.Finally,a finite element simulation model is established using the optimal structural parameters obtained by the proposed algorithm to verify the optimization results.Compared with the prototype,the optimized PMEC has reduced eddy current losses by 1.7812 kW,increased output torque by 658.5 N·m,and decreased costs by 13%,improving energy transfer efficiency.展开更多
From the end of 2019 until now,the Coronavirus Disease 2019(COVID-19)has been rampaging around the world,posing a great threat to people's lives and health,as well as a serious impact on economic development.Consi...From the end of 2019 until now,the Coronavirus Disease 2019(COVID-19)has been rampaging around the world,posing a great threat to people's lives and health,as well as a serious impact on economic development.Considering the severely infectious nature of COVID-19,the diagnosis of COVID-19 has become crucial.Identification through the use of Computed Tomography(CT)images is an efficient and quick means.Therefore,scientific researchers have proposed numerous segmentation methods to improve the diagnosis of CT images.In this paper,we propose a reinforcement learning-based golden jackal optimization algorithm,which is named QLGJO,to segment CT images in furtherance of the diagnosis of COVID-19.Reinforcement learning is combined for the first time with meta-heuristics in segmentation problem.This strategy can effectively overcome the disadvantage that the original algorithm tends to fall into local optimum.In addition,one hybrid model and three different mutation strategies were applied to the update part of the algorithm in order to enrich the diversity of the population.Two experiments were carried out to test the performance of the proposed algorithm.First,compare QLGJO with other advanced meta-heuristics using the IEEE CEC2022 benchmark functions.Secondly,QLGJO was experimentally evaluated on CT images of COVID-19 using the Otsu method and compared with several well-known meta-heuristics.It is shown that QLGJO is very competitive in benchmark function and image segmentation experiments compared with other advanced meta-heuristics.Furthermore,the source code of the QLGJO is publicly available at https://github.com/Vang-z/QLGJO.展开更多
In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on th...In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on the optimization theory to identifying fire image characteristics. First the optimization of BP neural network adopting Levenberg-Marquardt algorithm with the property of quadratic convergence is discussed, and then a new system of fire image identification is devised. Plenty of experiments and field tests have proved that this system can detect the early-stage fire flame quickly and reliably.展开更多
Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of...Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.展开更多
Golden eagle optimizer(GEO)is a recently introduced nature-inspired metaheuristic algorithm,which simulates the spiral hunting behavior of golden eagles in nature.Regrettably,the GEO suffers from the challenges of low...Golden eagle optimizer(GEO)is a recently introduced nature-inspired metaheuristic algorithm,which simulates the spiral hunting behavior of golden eagles in nature.Regrettably,the GEO suffers from the challenges of low diversity,slow iteration speed,and stagnation in local optimization when dealing with complicated optimization problems.To ameliorate these deficiencies,an improved hybrid GEO called IGEO,combined with Lévy flight,sine cosine algorithm and differential evolution(DE)strategy,is developed in this paper.The Lévy flight strategy is introduced into the initial stage to increase the diversity of the golden eagle population and make the initial population more abundant;meanwhile,the sine-cosine function can enhance the exploration ability of GEO and decrease the possibility of GEO falling into the local optima.Furthermore,the DE strategy is used in the exploration and exploitation stage to improve accuracy and convergence speed of GEO.Finally,the superiority of the presented IGEO are comprehensively verified by comparing GEO and several state-of-the-art algorithms using(1)the CEC 2017 and CEC 2019 benchmark functions and(2)5 real-world engineering problems respectively.The comparison results demonstrate that the proposed IGEO is a powerful and attractive alternative for solving engineering optimization problems.展开更多
As one of the most important steps in the design of bearing-less rotor systems,the design of flexible beam has received much research attention.Because of the very complex working environment of helicopter,the flexibl...As one of the most important steps in the design of bearing-less rotor systems,the design of flexible beam has received much research attention.Because of the very complex working environment of helicopter,the flexible beam should satisfy both the strength and dynamic requirements.However,traditional optimization research focused only on either the strength or dynamical characteristics.To sufficiently improve the performance of the flexible beam,both aspects must be considered.This paper proposes a two-stage optimization method based on the Hamilton variational principle:Variational asymptotic beam section analysis(VABS)program and genetic algorithm(GA).Consequently,a two-part analysis model based on the Hamilton variational principle and VABS is established to calculate section characteristics and structural dynamics characteristics,respectively.Subsequently,the two parts are combined to establish a two-stage optimization process and search with GA to obtain the best dynamic characteristics combinations.Based on the primary optimization results,the section characteristics of the flexible beam are further optimized using GA.The optimization results show that the torsional stiffness decreases by 36.1%compared with the full 0°laying scheme without optimization and the dynamic requirements are achieved.The natural frequencies of flapping and torsion meet the requirements(0.5 away from the passing frequencies of the blade,0.25 away from the excitation force frequency,and the flapping and torsion frequencies keep a corresponding distance).The results indicate that the optimization method can significantly improve the performance of the flexible beam.展开更多
In Underwater Acoustic Sensor Network(UASN),routing and propagation delay is affected in each node by various water column environmental factors such as temperature,salinity,depth,gases,divergent and rotational wind.H...In Underwater Acoustic Sensor Network(UASN),routing and propagation delay is affected in each node by various water column environmental factors such as temperature,salinity,depth,gases,divergent and rotational wind.High sound velocity increases the transmission rate of the packets and the high dissolved gases in the water increases the sound velocity.High dissolved gases and sound velocity environment in the water column provides high transmission rates among UASN nodes.In this paper,the Modified Mackenzie Sound equation calculates the sound velocity in each node for energy-efficient routing.Golden Ratio Optimization Method(GROM)and Gaussian Process Regression(GPR)predicts propagation delay of each node in UASN using temperature,salinity,depth,dissolved gases dataset.Dissolved gases,rotational and divergent winds,and stress plays a major problem in UASN,which increases propagation delay and energy consumption.Predicted values from GPR and GROM leads to node selection and Corona Virus Optimization Algorithm(CVOA)routing is performed on the selected nodes.The proposed GPR-CVOA and GROM-CVOA algorithm solves the problem of propagation delay and consumes less energy in nodes,based on appropriate tolerant delays in transmitting packets among nodes during high rotational and divergent winds.From simulation results,CVOA Algorithm performs better than traditional DF and LION algorithms.展开更多
巷道断面成形是煤矿掘进过程中的重要工序,但目前的巷道断面成形作业多为人工控制掘进机进行往复式截割,制约了煤矿掘进工作面的智能化发展。为此,针对断面成形轨迹规划未考虑煤岩特征、优化目标单一的问题,提出了一种基于改进灰狼优化(...巷道断面成形是煤矿掘进过程中的重要工序,但目前的巷道断面成形作业多为人工控制掘进机进行往复式截割,制约了煤矿掘进工作面的智能化发展。为此,针对断面成形轨迹规划未考虑煤岩特征、优化目标单一的问题,提出了一种基于改进灰狼优化(grey wolf optimizer, GWO)算法的掘进机断面成形轨迹规划方法。首先,根据夹矸位置将待截割断面环境分为4种情况,对相应断面进行栅格化处理并建立栅格地图,同时采用二值膨胀法对不规则夹矸进行膨胀化处理。然后,对GWO算法进行了改进,以提升其寻优性能和收敛速度。接着,开展了仿真实验,利用改进GWO算法实现了4种环境下掘进机断面成形轨迹的规划。最后,利用掘进机样机开展了断面截割实验。仿真结果表明:相较于传统的GWO算法,改进GWO算法的收敛速度更快且收敛精度更高;在4种断面环境下,基于改进GWO算法规划的断面成形轨迹长度最短,欠挖面积最小,转向次数最少,更容易实现高精度、高效率的轨迹跟踪控制,保证了巷道断面的成形质量。实验结果表明,基于改进GWO算法规划的断面成形轨迹既能提高掘进机的截割效率,又能满足巷道断面成形的质量要求。研究结果可为煤矿井下智能掘进技术的发展提供新的思路和方法。展开更多
文摘Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.
文摘hyperbolic model for the diffusion of proteins through the blood-cerebro spinal fluid(CSF)barrier revolutionized clinical neurochemistry thirty years ago.The regression curves were informally parametrized based on physiolog-ically-driven constraints.The current paper readdresses this issue with nu-merical optimization for unconstrained non-linear regression,implementing the Levenberg-Marquardt Algorithm(LMA).Astonishingly similar estimates are obtained,which reconfirms the concepts of H.Reiber proposed in 1990s.The LMA is discussed in the context of other optimization algorithms.
基金supported by Specialized Research Fund for the Doctoral Program of Higher Education of China(20110022120004)the Fundamental Research Funds for the Central Universities
文摘For density inversion of gravity anomaly data, once the inversion method is determined, the main factors affecting the inversion result are the inversion parameters and subdivision scheme. A set of reasonable inversion parameters and subdivision scheme can, not only improve the inversion process efficiency, but also ensure inversion result accuracy. The gravity inversion method based on correlation searching and the golden section algorithm is an effective potential field inversion method. It can be used to invert 2D and 3D physical properties with potential data observed on flat or rough surfaces. In this paper, we introduce in detail the density inversion principles based on correlation searching and the golden section algorithm. Considering that the gold section algorithm is not globally optimized. we present a heuristic method to ensure the inversion result is globally optimized. With a series of model tests, we systematically compare and analyze the inversion result efficiency and accuracy with different parameters. Based on the model test results, we conclude the selection principles for each inversion parameter with which the inversion accuracy can be obviously improved.
基金the National Science and Tech-nology Council,Taiwan for their financial support(Grant Number NSTC 111-2221-E-019-048).
文摘This study sets up two new merit functions,which are minimized for the detection of real eigenvalue and complex eigenvalue to address nonlinear eigenvalue problems.For each eigen-parameter the vector variable is solved from a nonhomogeneous linear system obtained by reducing the number of eigen-equation one less,where one of the nonzero components of the eigenvector is normalized to the unit and moves the column containing that component to the right-hand side as a nonzero input vector.1D and 2D golden section search algorithms are employed to minimize the merit functions to locate real and complex eigenvalues.Simultaneously,the real and complex eigenvectors can be computed very accurately.A simpler approach to the nonlinear eigenvalue problems is proposed,which implements a normalization condition for the uniqueness of the eigenvector into the eigenequation directly.The real eigenvalues can be computed by the fictitious time integration method(FTIM),which saves computational costs compared to the one-dimensional golden section search algorithm(1D GSSA).The simpler method is also combined with the Newton iterationmethod,which is convergent very fast.All the proposed methods are easily programmed to compute the eigenvalue and eigenvector with high accuracy and efficiency.
基金supported by the National Natural Science Foundation of China (No.50808090)
文摘As in the building of deep buried long tunnels,there are complicated conditions such as great deformation,high stress,multi-variables,high non-linearity and so on,the algorithm for structure optimization and its application in tunnel engineering are still in the starting stage. Along with the rapid development of highways across the country,it has become a very urgent task to be tackled to carry out the optimization design of the structure of the section of the tunnel to lessen excavation workload and to reinforce the support. Artificial intelligence demonstrates an extremely strong capability of identifying,expressing and disposing such kind of multiple variables and complicated non-linear relations. In this paper,a comprehensive consideration of the strategy of the selection and updating of the concentration and adaptability of the immune algorithm is made to replace the selection mode in the original genetic algorithm which depends simply on the adaptability value. Such an algorithm has the advantages of both the immune algorithm and the genetic algorithm,thus serving the purpose of not only enhancing the individual adaptability but maintaining the individual diversity as well. By use of the identifying function of the antigen memory,the global search capability of the immune genetic algorithm is raised,thereby avoiding the occurrence of the premature phenomenon. By optimizing the structure of the section of the Huayuan tunnel,the current excavation area and support design are adjusted. A conclusion with applicable value is arrived at. At a higher computational speed and a higher efficiency,the current method is verified to have advantages in the optimization computation of the tunnel project. This also suggests that the application of the immune genetic algorithm has a practical significance to the stability assessment and informationization design of the wall rock of the tunnel.
基金Sponsored by the National Natural Science Foundation of China (Grant No. 50579095)Ertan Hydropower Development Company, LTD.
文摘A new fuzzy optimization neural network model is proposed based on the Levenberg-Marquardt (LM) algorithm on account of the disadvantages of slow convergence of traditional fuzzy optimization neural network model. In this new model,the gradient descent algorithm is replaced by the LM algorithm to obtain the minimum of output errors during network training,which changes the weights adjusting equations of the network and increases the training speed. Moreover,to avoid the results yielding to local minimum,the transfer function is also revised to sigmoid function. A case study is utilized to validate this new model,and the results reveal that the new model fast training speed and better forecasting capability.
文摘To improve the thermal efficiency and reduce nitrogen oxides （NOx ） emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other related parameters, dynamic radial basis function （RBF） artificial neural network （ANN） models for forecasting unburned carbon in fly ash and NO, emissions in flue gas ware developed in this paper, together with a multi-objective optimization system utilizing particle swarm optimization and Powell （PSO-Powell） algorithm. To validate the proposed approach, a series of field tests were conducted in a 350 MW power plant. The results indicate that PSO-Powell algorithm can improve the capability to search optimization solution of PSO algorithm, and the effectiveness of system. Its prospective application in the optimization of a pulverized coal （ PC ） fired boiler is presented as well.
基金supported by the National Natural Science Foundation of China under Grant 52077027.
文摘The permanent magnet eddy current coupler(PMEC)solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems.It provides torque to the load and generates heat and losses,reducing its energy transfer efficiency.This issue has become an obstacle for PMEC to develop toward a higher power.This paper aims to improve the overall performance of PMEC through multi-objective optimization methods.Firstly,a PMEC modeling method based on the Levenberg-Marquardt back propagation(LMBP)neural network is proposed,aiming at the characteristics of the complex input-output relationship and the strong nonlinearity of PMEC.Then,a novel competition mechanism-based multi-objective particle swarm optimization algorithm(NCMOPSO)is proposed to find the optimal structural parameters of PMEC.Chaotic search and mutation strategies are used to improve the original algorithm,which improves the shortcomings of multi-objective particle swarm optimization(MOPSO),which is too fast to converge into a global optimum,and balances the convergence and diversity of the algorithm.In order to verify the superiority and applicability of the proposed algorithm,it is compared with several popular multi-objective optimization algorithms.Applying them to the optimization model of PMEC,the results show that the proposed algorithm has better comprehensive performance.Finally,a finite element simulation model is established using the optimal structural parameters obtained by the proposed algorithm to verify the optimization results.Compared with the prototype,the optimized PMEC has reduced eddy current losses by 1.7812 kW,increased output torque by 658.5 N·m,and decreased costs by 13%,improving energy transfer efficiency.
基金supported by the National Natural Science Foundation of China[grant numbers 21466008]the Guangxi Natural Science Foundation,China[grant numbers 2019GXNSFAA185017]+1 种基金the Scientific Research Project of Guangxi Minzu University[grant numbers 2021MDKJ004]the Innovation Project of Guangxi Graduate Education[grant numbers YCSW2022255].
文摘From the end of 2019 until now,the Coronavirus Disease 2019(COVID-19)has been rampaging around the world,posing a great threat to people's lives and health,as well as a serious impact on economic development.Considering the severely infectious nature of COVID-19,the diagnosis of COVID-19 has become crucial.Identification through the use of Computed Tomography(CT)images is an efficient and quick means.Therefore,scientific researchers have proposed numerous segmentation methods to improve the diagnosis of CT images.In this paper,we propose a reinforcement learning-based golden jackal optimization algorithm,which is named QLGJO,to segment CT images in furtherance of the diagnosis of COVID-19.Reinforcement learning is combined for the first time with meta-heuristics in segmentation problem.This strategy can effectively overcome the disadvantage that the original algorithm tends to fall into local optimum.In addition,one hybrid model and three different mutation strategies were applied to the update part of the algorithm in order to enrich the diversity of the population.Two experiments were carried out to test the performance of the proposed algorithm.First,compare QLGJO with other advanced meta-heuristics using the IEEE CEC2022 benchmark functions.Secondly,QLGJO was experimentally evaluated on CT images of COVID-19 using the Otsu method and compared with several well-known meta-heuristics.It is shown that QLGJO is very competitive in benchmark function and image segmentation experiments compared with other advanced meta-heuristics.Furthermore,the source code of the QLGJO is publicly available at https://github.com/Vang-z/QLGJO.
文摘In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on the optimization theory to identifying fire image characteristics. First the optimization of BP neural network adopting Levenberg-Marquardt algorithm with the property of quadratic convergence is discussed, and then a new system of fire image identification is devised. Plenty of experiments and field tests have proved that this system can detect the early-stage fire flame quickly and reliably.
基金National Natural Science Foundation of China(No.61371024)Aviation Science Fund of China(No.2013ZD53051)+2 种基金Aerospace Technology Support Fund of Chinathe Industry-Academy-Research Project of AVIC,China(No.cxy2013XGD14)the Open Research Project of Guangdong Key Laboratory of Popular High Performance Computers/Shenzhen Key Laboratory of Service Computing and Applications,China
文摘Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.
基金National Natural Science Foundation of China(Grant No.51875454).
文摘Golden eagle optimizer(GEO)is a recently introduced nature-inspired metaheuristic algorithm,which simulates the spiral hunting behavior of golden eagles in nature.Regrettably,the GEO suffers from the challenges of low diversity,slow iteration speed,and stagnation in local optimization when dealing with complicated optimization problems.To ameliorate these deficiencies,an improved hybrid GEO called IGEO,combined with Lévy flight,sine cosine algorithm and differential evolution(DE)strategy,is developed in this paper.The Lévy flight strategy is introduced into the initial stage to increase the diversity of the golden eagle population and make the initial population more abundant;meanwhile,the sine-cosine function can enhance the exploration ability of GEO and decrease the possibility of GEO falling into the local optima.Furthermore,the DE strategy is used in the exploration and exploitation stage to improve accuracy and convergence speed of GEO.Finally,the superiority of the presented IGEO are comprehensively verified by comparing GEO and several state-of-the-art algorithms using(1)the CEC 2017 and CEC 2019 benchmark functions and(2)5 real-world engineering problems respectively.The comparison results demonstrate that the proposed IGEO is a powerful and attractive alternative for solving engineering optimization problems.
基金supported by the Foundation of National Key Laboratory of Rotorcraft Aeromechanics,Nanjing University of Aeronautics and Astronautics(No.614222004030917)。
文摘As one of the most important steps in the design of bearing-less rotor systems,the design of flexible beam has received much research attention.Because of the very complex working environment of helicopter,the flexible beam should satisfy both the strength and dynamic requirements.However,traditional optimization research focused only on either the strength or dynamical characteristics.To sufficiently improve the performance of the flexible beam,both aspects must be considered.This paper proposes a two-stage optimization method based on the Hamilton variational principle:Variational asymptotic beam section analysis(VABS)program and genetic algorithm(GA).Consequently,a two-part analysis model based on the Hamilton variational principle and VABS is established to calculate section characteristics and structural dynamics characteristics,respectively.Subsequently,the two parts are combined to establish a two-stage optimization process and search with GA to obtain the best dynamic characteristics combinations.Based on the primary optimization results,the section characteristics of the flexible beam are further optimized using GA.The optimization results show that the torsional stiffness decreases by 36.1%compared with the full 0°laying scheme without optimization and the dynamic requirements are achieved.The natural frequencies of flapping and torsion meet the requirements(0.5 away from the passing frequencies of the blade,0.25 away from the excitation force frequency,and the flapping and torsion frequencies keep a corresponding distance).The results indicate that the optimization method can significantly improve the performance of the flexible beam.
文摘In Underwater Acoustic Sensor Network(UASN),routing and propagation delay is affected in each node by various water column environmental factors such as temperature,salinity,depth,gases,divergent and rotational wind.High sound velocity increases the transmission rate of the packets and the high dissolved gases in the water increases the sound velocity.High dissolved gases and sound velocity environment in the water column provides high transmission rates among UASN nodes.In this paper,the Modified Mackenzie Sound equation calculates the sound velocity in each node for energy-efficient routing.Golden Ratio Optimization Method(GROM)and Gaussian Process Regression(GPR)predicts propagation delay of each node in UASN using temperature,salinity,depth,dissolved gases dataset.Dissolved gases,rotational and divergent winds,and stress plays a major problem in UASN,which increases propagation delay and energy consumption.Predicted values from GPR and GROM leads to node selection and Corona Virus Optimization Algorithm(CVOA)routing is performed on the selected nodes.The proposed GPR-CVOA and GROM-CVOA algorithm solves the problem of propagation delay and consumes less energy in nodes,based on appropriate tolerant delays in transmitting packets among nodes during high rotational and divergent winds.From simulation results,CVOA Algorithm performs better than traditional DF and LION algorithms.
文摘巷道断面成形是煤矿掘进过程中的重要工序,但目前的巷道断面成形作业多为人工控制掘进机进行往复式截割,制约了煤矿掘进工作面的智能化发展。为此,针对断面成形轨迹规划未考虑煤岩特征、优化目标单一的问题,提出了一种基于改进灰狼优化(grey wolf optimizer, GWO)算法的掘进机断面成形轨迹规划方法。首先,根据夹矸位置将待截割断面环境分为4种情况,对相应断面进行栅格化处理并建立栅格地图,同时采用二值膨胀法对不规则夹矸进行膨胀化处理。然后,对GWO算法进行了改进,以提升其寻优性能和收敛速度。接着,开展了仿真实验,利用改进GWO算法实现了4种环境下掘进机断面成形轨迹的规划。最后,利用掘进机样机开展了断面截割实验。仿真结果表明:相较于传统的GWO算法,改进GWO算法的收敛速度更快且收敛精度更高;在4种断面环境下,基于改进GWO算法规划的断面成形轨迹长度最短,欠挖面积最小,转向次数最少,更容易实现高精度、高效率的轨迹跟踪控制,保证了巷道断面的成形质量。实验结果表明,基于改进GWO算法规划的断面成形轨迹既能提高掘进机的截割效率,又能满足巷道断面成形的质量要求。研究结果可为煤矿井下智能掘进技术的发展提供新的思路和方法。
