To solve the problem of multi-platform collaborative use in anti-ship missile (ASM) path planning, this paper pro-posed multi-operator real-time constraints particle swarm opti-mization (MRC-PSO) algorithm. MRC-PSO al...To solve the problem of multi-platform collaborative use in anti-ship missile (ASM) path planning, this paper pro-posed multi-operator real-time constraints particle swarm opti-mization (MRC-PSO) algorithm. MRC-PSO algorithm utilizes a semi-rasterization environment modeling technique and inte-grates the geometric gradient law of ASMs which distinguishes itself from other collaborative path planning algorithms by fully considering the coupling between collaborative paths. Then, MRC-PSO algorithm conducts chunked stepwise recursive evo-lution of particles while incorporating circumvent, coordination, and smoothing operators which facilitates local selection opti-mization of paths, gradually reducing algorithmic space, accele-rating convergence, and enhances path cooperativity. Simula-tion experiments comparing the MRC-PSO algorithm with the PSO algorithm, genetic algorithm and operational area cluster real-time restriction (OACRR)-PSO algorithm, which demon-strate that the MRC-PSO algorithm has a faster convergence speed, and the average number of iterations is reduced by approximately 75%. It also proves that it is equally effective in resolving complex scenarios involving multiple obstacles. More-over it effectively addresses the problem of path crossing and can better satisfy the requirements of multi-platform collabora-tive path planning. The experiments are conducted in three col-laborative operation modes, namely, three-to-two, three-to-three, and four-to-two, and the outcomes demonstrate that the algorithm possesses strong universality.展开更多
Aiming at the problem that the trajectory tracking performance of redundant manipulator corresponding to the target position is difficult to optimize,the trajectory tracking method of redundant manipulator based on PS...Aiming at the problem that the trajectory tracking performance of redundant manipulator corresponding to the target position is difficult to optimize,the trajectory tracking method of redundant manipulator based on PSO algorithm optimization is studied.The kinematic diagram of redundant manipulator is created,to derive the equation of motion trajectory of redundant manipulator end.Pseudo inverse Jacobi matrix is used to solve the problem of manipulator redundancy.Based on the tracking ellipse of redundant manipulator,the tracking shape of redundant manipulator is determined with the overall tracking index as the second index,and the optimization method of tracking index is proposed.The redundant manipulator contour is located by active contour model,on this basis,combined with particle swarm optimization algorithm,the point coordinates on the circumference with the relevant joint point as the center and joint length as the radius are selected as the algorithm particles for iteration,and the optimal tracking results of the overall redundant manipulator trajectory are obtained.The experimental results show that under the proposed method,the tracking error of the redundant manipulator is low,and the error jump range is small.It shows that this method has high tracking accuracy and reliability.展开更多
Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is pro...Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.展开更多
Multibody system dynamics provides a strong tool for the estimation of dynamic performances and the optimization of multisystem robot design. It can be described with differential algebraic equations(DAEs). In this pa...Multibody system dynamics provides a strong tool for the estimation of dynamic performances and the optimization of multisystem robot design. It can be described with differential algebraic equations(DAEs). In this paper, a particle swarm optimization(PSO) method is introduced to solve and control a symplectic multibody system for the first time. It is first combined with the symplectic method to solve problems in uncontrolled and controlled robotic arm systems. It is shown that the results conserve the energy and keep the constraints of the chaotic motion, which demonstrates the efficiency, accuracy, and time-saving ability of the method. To make the system move along the pre-planned path, which is a functional extremum problem, a double-PSO-based instantaneous optimal control is introduced. Examples are performed to test the effectiveness of the double-PSO-based instantaneous optimal control. The results show that the method has high accuracy, a fast convergence speed, and a wide range of applications.All the above verify the immense potential applications of the PSO method in multibody system dynamics.展开更多
Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the car...Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the cargo oil tank(COT) under various kinds of constraints in the preliminary design stage.A non-linear programming model is built to simulate the optimization design,in which the requirements and rules for COTD are used as the constraints.Considering the distance between the inner shell and hull,a fuzzy constraint is used to express the feasibility degree of the double-hull configuration.In terms of the characteristic of COTD,the PSO algorithm is improved to solve this problem.A bivariate extremum strategy is presented to deal with the fuzzy constraint,by which the maximum and minimum cargo capacities are obtained simultaneously.Finally,the simulation demonstrates the feasibility and effectiveness of the proposed approach.展开更多
Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be ma...Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model.In addition,the neural network has the ability of generalization and mapping,it can consider the noise,transmission channel inconsistency and other factors of the objective environment.Therefore,this paper utilizes Back Propagation(BP)neural network as the basic framework of underwater DOA estimation.Furthermore,in order to improve the performance of DOA estimation of BP neural network,the following three improvements are proposed.(1)Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process,PSO-BP-NN based on optimized particle swarm optimization(PSO)algorithm is proposed.(2)The Higher-order cumulant of the received signal is utilized to establish the training model.(3)A BP neural network training method for arbitrary number of sources is proposed.Finally,the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm.展开更多
In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath f...In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.展开更多
We proposed and demonstrated the ultra-compact 1310/1550 nm wavelength multiplexer/demultiplexer assisted by subwavelength grating(SWG)using particle swarm optimization(PSO)algorithm in silicon-on-insulator(SOI)platfo...We proposed and demonstrated the ultra-compact 1310/1550 nm wavelength multiplexer/demultiplexer assisted by subwavelength grating(SWG)using particle swarm optimization(PSO)algorithm in silicon-on-insulator(SOI)platform.Through the self-imaging effect of multimode interference(MMI)coupler,the demultiplexing function for 1310 nm and 1550 nm wavelengths is implemented.After that,three parallel SWG-based slots are inserted into the MMI section so that the effective refractive index of the modes can be engineered and thus the beat length can be adjusted.Importantly,these three SWG slots significantly reduce the length of the device,which is much shorter than the length of traditional MMI-based wavelength demultiplexers.Ultimately,by using the PSO algorithm,the equivalent refractive index and width of the SWG in a certain range are optimized to achieve the best performance of the wavelength demultiplexer.It has been verified that the device footprint is only 2×30.68μm^(2),and 1 dB bandwidths of larger than 120 nm are acquired at 1310 nm and 1550 nm wavelengths.Meanwhile,the transmitted spectrum shows that the insertion loss(IL)values are below 0.47 dB at both wavelengths when the extinction ratio(ER)values are above 12.65 dB.This inverse design approach has been proved to be efficient in increasing bandwidth and reducing device length.展开更多
Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a produc...Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model(FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm(GA), particle swarm optimization(PSO) algorithm and differential mutation BA(DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms.展开更多
Polyvinyl chloride （PVC） polymerizing process is a typical complicated industrial process with the characteristics of large inertia, big time delay and nonlinearity. Firstly, for the general nonlinear and discrete t...Polyvinyl chloride （PVC） polymerizing process is a typical complicated industrial process with the characteristics of large inertia, big time delay and nonlinearity. Firstly, for the general nonlinear and discrete time system, a design scheme of model-free adaptive （MFA） controller is given. Then, particle swarm optimization （PSO） algorithm is applied to optimizing and setting the key parameters for controller tuning. After that, the MFA controller is used to control the system of polymerizing temperature. Finally, simulation results are given to show that the MAC strategy based on PSO obtains a good controlling performance index.展开更多
To consider multi-objective optimization problem with the number of feed array elements and sidelobe level of large antenna array, multi-objective cross entropy(CE) algorithm is proposed by combining fuzzy c-mean clus...To consider multi-objective optimization problem with the number of feed array elements and sidelobe level of large antenna array, multi-objective cross entropy(CE) algorithm is proposed by combining fuzzy c-mean clustering algorithm with traditional cross entropy algorithm, and specific program flow of the algorithm is given.Using the algorithm, large thinned array(200 elements) given sidelobe level(-10,-19 and-30 d B) problem is solved successfully. Compared with the traditional statistical algorithms, the optimization results of the algorithm validate that the number of feed array elements reduces by 51%, 11% and 6% respectively. In addition, compared with the particle swarm optimization(PSO) algorithm, the number of feed array elements from the algorithm is more similar, but the algorithm is more efficient.展开更多
Hyperparameter optimization is considered as one of the most challenges in deep learning and dominates the precision of model in a certain.Recent proposals tried to solve this issue through the particle swarm optimiza...Hyperparameter optimization is considered as one of the most challenges in deep learning and dominates the precision of model in a certain.Recent proposals tried to solve this issue through the particle swarm optimization(PSO),but its native defect may result in the local optima trapped and convergence difficulty.In this paper,the genetic operations are introduced to the PSO,which makes the best hyperparameter combination scheme for specific network architecture be located easier.Spe-cifically,to prevent the troubles caused by the different data types and value scopes,a mixed coding method is used to ensure the effectiveness of particles.Moreover,the crossover and mutation opera-tions are added to the process of particles updating,to increase the diversity of particles and avoid local optima in searching.Verified with three benchmark datasets,MNIST,Fashion-MNIST,and CIFAR10,it is demonstrated that the proposed scheme can achieve accuracies of 99.58%,93.39%,and 78.96%,respectively,improving the accuracy by about 0.1%,0.5%,and 2%,respectively,compared with that of the PSO.展开更多
This paper discusses a kind of optimal method used for searching flat panel display （FPD） scanning matrix. The method adopts bionic algorithm： genetic algorithm （GA） and particle swarm optimization （PSO） algori...This paper discusses a kind of optimal method used for searching flat panel display （FPD） scanning matrix. The method adopts bionic algorithm： genetic algorithm （GA） and particle swarm optimization （PSO） algorithm. The method using single GA is more time-consuming, and the search efficiency is low in later evolution; the PSO algorithm is easily falling into the local optimal solution and appears the premature convergent phenomenon. Hence, a hybrid approach of GAPSO is found to optimize the search for high grayscale weights scanning matrix. Finally in the acceptable time, it finds a weight scanning matrix （WSM） of 256 gray scales with Matlab, whose scanning efficiency reaches 94.73% and the linearity is very good.展开更多
The development of society is inseparable from the use of traditional burning energy.However,people’s excessive exploitation of fossil energy has led to the gradual shortage of fossil energy.It is essential to find N...The development of society is inseparable from the use of traditional burning energy.However,people’s excessive exploitation of fossil energy has led to the gradual shortage of fossil energy.It is essential to find New Energy(NE)and develop a new energy industry.The natural ecosystem has the characteristics of stable development.With the development of Artificial Intelligence(AI),the structure of the natural ecosystem has been applied to the NE industry,forming an NE industry ecological integration system.This paper uses Particle Swarm Optimization(PSO)algorithm to optimize the structure and resources of the NE industry,so that the NE industry has the capability of sustainable development.The traditional NE industry and the NE innovation industry ecological integration system based on PSO algorithm are compared.The experimental results show that in the NE vehicle industry,the average economic benefits of the traditional NE industry and the NE innovation industry ecosystem based on PSO algorithm are 63.6%and 77.2%,respectively.In the NE power generation industry,the average economic benefits of the traditional NE industry and the NE innovation industry ecosystem based on PSO algorithm are 67.6%and 80.4%,respectively.Therefore,in the context of AI,the application of PSO algorithm to the ecological integration system of NE industry could improve the economic benefits of NE industry.展开更多
Schizophrenia is a severe mental illness responsible for many of the world’s disabilities.It significantly impacts human society;thus,rapid,and efficient identification is required.This research aims to diagnose schi...Schizophrenia is a severe mental illness responsible for many of the world’s disabilities.It significantly impacts human society;thus,rapid,and efficient identification is required.This research aims to diagnose schizophrenia directly from a high-resolution camera,which can capture the subtle micro facial expressions that are difficult to spot with the help of the naked eye.In a clinical study by a team of experts at Bahawal Victoria Hospital(BVH),Bahawalpur,Pakistan,there were 300 people with schizophrenia and 299 healthy subjects.Videos of these participants have been captured and converted into their frames using the OpenFace tool.Additionally,pose,gaze,Action Units(AUs),and land-marked features have been extracted in the Comma Separated Values(CSV)file.Aligned faces have been used to detect schizophrenia by the proposed and the pre-trained Convolutional Neural Network(CNN)models,i.e.,VGG16,Mobile Net,Efficient Net,Google Net,and ResNet50.Moreover,Vision transformer,Swim transformer,big transformer,and vision transformer without attention have also been used to train the models on customized dataset.CSV files have been used to train a model using logistic regression,decision trees,random forest,gradient boosting,and support vector machine classifiers.Moreover,the parameters of the proposed CNN architecture have been optimized using the Particle Swarm Optimization algorithm.The experimental results showed a validation accuracy of 99.6%for the proposed CNN model.The results demonstrated that the reported method is superior to the previous methodologies.The model can be deployed in a real-time environment.展开更多
A metasurface-loaded 1×2 patch array antenna assisted by a deep-learning optimization method is proposed to realize port and radiation pattern decoupling simultaneously to enhance the isolation among elements in ...A metasurface-loaded 1×2 patch array antenna assisted by a deep-learning optimization method is proposed to realize port and radiation pattern decoupling simultaneously to enhance the isolation among elements in multi-input multi-output (MIMO) systems. The deep-learning-assisted optimization method uses an artificial neural network (ANN) and a particle swarm optimization (PSO) algorithm to seek the optimal structure of the antenna to achieve port decoupling with undistorted radiation patterns. The ANN is trained to describe the nonlinear relationship between the geometric parameters and the responses of the antenna. The PSO algorithm, guided by the cost function and number of iterations, is used to optimize the structure of the antenna according to the cost function combined with the trained ANN. Finally, by constraining the cost function, we obtain a 1×2 patch array antenna with a metasurface fixed above by studs, which achieves port and radiation pattern decoupling simultaneously. To validate the principle and design method, we designed, fabricated, and measured an antenna prototype with dimensions of 0.88λ_(0)×0.47λ_(0)×0.21λ_(0) (λ_(0) is the wavelength in free space at the center frequency). The measured fractional bandwidth is 8% (4.8–5.2 GHz). The isolation of the two-element patch antenna increases from 7.6 dB to 24.3 dB with an envelope correlation coefficient (ECC) of <0.0005 at 0.35λ_(0). Moreover, the H-plane radiation pattern of each element is consistent and symmetric in the broadside direction. These characteristics make the proposed antenna suitable for MIMO antenna systems with close spacing.展开更多
Purpose:The increase in plug-in electric vehicles(PEVs)is likely to see a noteworthy impact on the distribution system due to high electric power consumption during charging and uncertainty in charging behavior.To add...Purpose:The increase in plug-in electric vehicles(PEVs)is likely to see a noteworthy impact on the distribution system due to high electric power consumption during charging and uncertainty in charging behavior.To address this problem,the present work mainly focuses on optimal integration of distributed generators(DG)into radial distribution systems in the presence of PEV loads with their charging behavior under daily load pattern including load models by considering the daily(24 h)power loss and voltage improvement of the system as objectives for better system performance.Design/methodology/approach:To achieve the desired outcomes,an efficient weighted factor multi-objective function is modeled.Particle Swarm Optimization(PSO)and Butterfly Optimization(BO)algorithms are selected and implemented to minimize the objectives of the system.A repetitive backward-forward sweep-based load flow has been introduced to calculate the daily power loss and bus voltages of the radial distribution system.The simulations are carried out using MATLAB software.Findings:The simulation outcomes reveal that the proposed approach definitely improved the system performance in all aspects.Among PSO and BO,BO is comparatively successful in achieving the desired objectives.Originality/value:The main contribution of this paper is the formulation of the multi-objective function that can address daily active power loss and voltage deviation under 24-h load pattern including grouping of residential,industrial and commercial loads.Introduction of repetitive backward-forward sweep-based load flow and the modeling of PEV load with two different charging scenarios.展开更多
The execution of the gaits generated with the help of a gait planner is a crucial task in biped locomotion. This task is to be achieved with the help of a suitable torque based controller to ensure smooth walk of the ...The execution of the gaits generated with the help of a gait planner is a crucial task in biped locomotion. This task is to be achieved with the help of a suitable torque based controller to ensure smooth walk of the biped robot. It is important to note that the success of the developed proportion integration differentiation （PID） controller depends on the selected gains of the controller. In the present study, an attempt is made to tune the gains of the PID controller for the biped robot ascending and descending the stair case and sloping surface with the help of two non-traditional optimization algorithms, namely modified chaotic invasive weed optimization （MCIWO） and particle swarm optimization （PSO） algorithms. Once the optimal PID controllers are developed, a simulation study has been conducted in computer for obtaining the optimal tuning parameters of the controller of the biped robot. Finally, the optimal gait angles obtained by using the best controller are fed to the real biped robot and found that the biped robot has successfully negotiated the said terrains.展开更多
In the present paper,the distribution feeder reconfiguration in the presence of distributed generation resources(DGR)and energy storage systems(ESS)is solved in the dynamic form.Since studies on the reconfiguration pr...In the present paper,the distribution feeder reconfiguration in the presence of distributed generation resources(DGR)and energy storage systems(ESS)is solved in the dynamic form.Since studies on the reconfiguration problem have ignored the grid security and reliability,the non-distributed energy index along with the energy loss and voltage stability indices has been assumed as the objective functions of the given problem.To achieve the mentioned benefits,there are several practical plans in the distribution network.One of these applications is the network rearrangement plan,which is the simplest and least expensive way to add equipment to the network.Besides,by adding the DGRs to the distribution grid,the radial mode of the grid and the one-sided passage of power are eliminated,and the ordinary and simple grid is replaced with a complex grid.In this paper,an improved particle clustering algorithm is used to solve the distribution network rearrangement problem with the presence of distributed generation sources.The PQ model and the PV model are both considered,and for this purpose,a model based on the compensation technique is used to model the PV busbars.The proposed developed model has particularly improved the local and global search of this algorithm.The reconfiguration problem is discussed and investigated considering different scenarios in a standard 33-bus grid as a well-known power system in different scenarios in the presence and absence of the DGRs.Then,the obtained results are compared.展开更多
In order to overcome the drawbacks of standard particle swarm optimization(PSO)algorithm,such as prematurity and easily trapping in local optimum,a modified PSO algorithm is proposed,in which special techniques,as glo...In order to overcome the drawbacks of standard particle swarm optimization(PSO)algorithm,such as prematurity and easily trapping in local optimum,a modified PSO algorithm is proposed,in which special techniques,as global best perturbation and inertia weight jump threshold are adopted.The convergence speed and accuracy of the algo-rithm are improved.The test by some benchmark problems shows that the proposed algorithm achieves relatively higher performance.Thereafter,the applications of the modified PSO in the radiation pattern synthesis of antenna arrays are presented.展开更多
基金supported by Hunan Provincial Natural Science Foundation(2024JJ5173,2023JJ50047)Hunan Provincial Department of Education Scientific Research Project(23A0494)Hunan Provincial Innovation Foundation for Postgraduate(CX20231221).
文摘To solve the problem of multi-platform collaborative use in anti-ship missile (ASM) path planning, this paper pro-posed multi-operator real-time constraints particle swarm opti-mization (MRC-PSO) algorithm. MRC-PSO algorithm utilizes a semi-rasterization environment modeling technique and inte-grates the geometric gradient law of ASMs which distinguishes itself from other collaborative path planning algorithms by fully considering the coupling between collaborative paths. Then, MRC-PSO algorithm conducts chunked stepwise recursive evo-lution of particles while incorporating circumvent, coordination, and smoothing operators which facilitates local selection opti-mization of paths, gradually reducing algorithmic space, accele-rating convergence, and enhances path cooperativity. Simula-tion experiments comparing the MRC-PSO algorithm with the PSO algorithm, genetic algorithm and operational area cluster real-time restriction (OACRR)-PSO algorithm, which demon-strate that the MRC-PSO algorithm has a faster convergence speed, and the average number of iterations is reduced by approximately 75%. It also proves that it is equally effective in resolving complex scenarios involving multiple obstacles. More-over it effectively addresses the problem of path crossing and can better satisfy the requirements of multi-platform collabora-tive path planning. The experiments are conducted in three col-laborative operation modes, namely, three-to-two, three-to-three, and four-to-two, and the outcomes demonstrate that the algorithm possesses strong universality.
基金This work has been supported by the Ningbo National Natural Science Foundation(2019A610124)General Project of Education Department of Zhejiang Province(Y201737089).
文摘Aiming at the problem that the trajectory tracking performance of redundant manipulator corresponding to the target position is difficult to optimize,the trajectory tracking method of redundant manipulator based on PSO algorithm optimization is studied.The kinematic diagram of redundant manipulator is created,to derive the equation of motion trajectory of redundant manipulator end.Pseudo inverse Jacobi matrix is used to solve the problem of manipulator redundancy.Based on the tracking ellipse of redundant manipulator,the tracking shape of redundant manipulator is determined with the overall tracking index as the second index,and the optimization method of tracking index is proposed.The redundant manipulator contour is located by active contour model,on this basis,combined with particle swarm optimization algorithm,the point coordinates on the circumference with the relevant joint point as the center and joint length as the radius are selected as the algorithm particles for iteration,and the optimal tracking results of the overall redundant manipulator trajectory are obtained.The experimental results show that under the proposed method,the tracking error of the redundant manipulator is low,and the error jump range is small.It shows that this method has high tracking accuracy and reliability.
基金National Natural Science Foundations of China(Nos.61222303,21276078)National High-Tech Research and Development Program of China(No.2012AA040307)+1 种基金New Century Excellent Researcher Award Program from Ministry of Education of China(No.NCET10-0885)the Fundamental Research Funds for the Central Universities and Shanghai Leading Academic Discipline Project,China(No.B504)
文摘Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.
基金Project supported by the National Natural Science Foundation of China(Nos.91648101 and11672233)the Northwestern Polytechnical University(NPU)Foundation for Fundamental Research(No.3102017AX008)the National Training Program of Innovation and Entrepreneurship for Undergraduates(No.S201710699033)
文摘Multibody system dynamics provides a strong tool for the estimation of dynamic performances and the optimization of multisystem robot design. It can be described with differential algebraic equations(DAEs). In this paper, a particle swarm optimization(PSO) method is introduced to solve and control a symplectic multibody system for the first time. It is first combined with the symplectic method to solve problems in uncontrolled and controlled robotic arm systems. It is shown that the results conserve the energy and keep the constraints of the chaotic motion, which demonstrates the efficiency, accuracy, and time-saving ability of the method. To make the system move along the pre-planned path, which is a functional extremum problem, a double-PSO-based instantaneous optimal control is introduced. Examples are performed to test the effectiveness of the double-PSO-based instantaneous optimal control. The results show that the method has high accuracy, a fast convergence speed, and a wide range of applications.All the above verify the immense potential applications of the PSO method in multibody system dynamics.
基金the National Special Fund for Agro-scientific Research in the Public Interest(No.201003024)
文摘Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the cargo oil tank(COT) under various kinds of constraints in the preliminary design stage.A non-linear programming model is built to simulate the optimization design,in which the requirements and rules for COTD are used as the constraints.Considering the distance between the inner shell and hull,a fuzzy constraint is used to express the feasibility degree of the double-hull configuration.In terms of the characteristic of COTD,the PSO algorithm is improved to solve this problem.A bivariate extremum strategy is presented to deal with the fuzzy constraint,by which the maximum and minimum cargo capacities are obtained simultaneously.Finally,the simulation demonstrates the feasibility and effectiveness of the proposed approach.
基金Strategic Priority Research Program of Chinese Academy of Sciences,Grant No.XDA28040000,XDA28120000Natural Science Foundation of Shandong Province,Grant No.ZR2021MF094+2 种基金Key R&D Plan of Shandong Province,Grant No.2020CXGC010804Central Leading Local Science and Technology Development Special Fund Project,Grant No.YDZX2021122Science&Technology Specific Projects in Agricultural High-tech Industrial Demonstration Area of the Yellow River Delta,Grant No.2022SZX11。
文摘Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model.In addition,the neural network has the ability of generalization and mapping,it can consider the noise,transmission channel inconsistency and other factors of the objective environment.Therefore,this paper utilizes Back Propagation(BP)neural network as the basic framework of underwater DOA estimation.Furthermore,in order to improve the performance of DOA estimation of BP neural network,the following three improvements are proposed.(1)Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process,PSO-BP-NN based on optimized particle swarm optimization(PSO)algorithm is proposed.(2)The Higher-order cumulant of the received signal is utilized to establish the training model.(3)A BP neural network training method for arbitrary number of sources is proposed.Finally,the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm.
基金Project(50175034) supported by the National Natural Science Foundation of China
文摘In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.
基金supported by the National Natural Science Foundation of China(No.61505160)the Innovation Capability Support Program of Shaanxi(No.2018KJXX-042)+2 种基金the Natural Science Basic Research Program of Shaanxi(No.2019JM-084)the State Key Laboratory of Transient Optics and Photonics(No.SKLST202108)the Graduate Innovation and Practical Ability Training Project of Xi’an Shiyou University(No.YCS22213190)。
文摘We proposed and demonstrated the ultra-compact 1310/1550 nm wavelength multiplexer/demultiplexer assisted by subwavelength grating(SWG)using particle swarm optimization(PSO)algorithm in silicon-on-insulator(SOI)platform.Through the self-imaging effect of multimode interference(MMI)coupler,the demultiplexing function for 1310 nm and 1550 nm wavelengths is implemented.After that,three parallel SWG-based slots are inserted into the MMI section so that the effective refractive index of the modes can be engineered and thus the beat length can be adjusted.Importantly,these three SWG slots significantly reduce the length of the device,which is much shorter than the length of traditional MMI-based wavelength demultiplexers.Ultimately,by using the PSO algorithm,the equivalent refractive index and width of the SWG in a certain range are optimized to achieve the best performance of the wavelength demultiplexer.It has been verified that the device footprint is only 2×30.68μm^(2),and 1 dB bandwidths of larger than 120 nm are acquired at 1310 nm and 1550 nm wavelengths.Meanwhile,the transmitted spectrum shows that the insertion loss(IL)values are below 0.47 dB at both wavelengths when the extinction ratio(ER)values are above 12.65 dB.This inverse design approach has been proved to be efficient in increasing bandwidth and reducing device length.
文摘Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model(FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm(GA), particle swarm optimization(PSO) algorithm and differential mutation BA(DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms.
基金supported by University of Science and Technology Liaoning,National Financial Security and System Equipment Engineering Research Center(No.USTLKFGJ201502)
文摘Polyvinyl chloride （PVC） polymerizing process is a typical complicated industrial process with the characteristics of large inertia, big time delay and nonlinearity. Firstly, for the general nonlinear and discrete time system, a design scheme of model-free adaptive （MFA） controller is given. Then, particle swarm optimization （PSO） algorithm is applied to optimizing and setting the key parameters for controller tuning. After that, the MFA controller is used to control the system of polymerizing temperature. Finally, simulation results are given to show that the MAC strategy based on PSO obtains a good controlling performance index.
基金the National Natural Science Foundation of China(No.51474100)the Youth Science Fund of Heilongjiang Province in China(No.QC2010023)the Youth Outstanding Ability Program in Heilongjiang University of Science and Technology
文摘To consider multi-objective optimization problem with the number of feed array elements and sidelobe level of large antenna array, multi-objective cross entropy(CE) algorithm is proposed by combining fuzzy c-mean clustering algorithm with traditional cross entropy algorithm, and specific program flow of the algorithm is given.Using the algorithm, large thinned array(200 elements) given sidelobe level(-10,-19 and-30 d B) problem is solved successfully. Compared with the traditional statistical algorithms, the optimization results of the algorithm validate that the number of feed array elements reduces by 51%, 11% and 6% respectively. In addition, compared with the particle swarm optimization(PSO) algorithm, the number of feed array elements from the algorithm is more similar, but the algorithm is more efficient.
基金the National Key Research and Development Program of China(No.2022ZD0119003)the National Natural Science Foundation of China(No.61834005).
文摘Hyperparameter optimization is considered as one of the most challenges in deep learning and dominates the precision of model in a certain.Recent proposals tried to solve this issue through the particle swarm optimization(PSO),but its native defect may result in the local optima trapped and convergence difficulty.In this paper,the genetic operations are introduced to the PSO,which makes the best hyperparameter combination scheme for specific network architecture be located easier.Spe-cifically,to prevent the troubles caused by the different data types and value scopes,a mixed coding method is used to ensure the effectiveness of particles.Moreover,the crossover and mutation opera-tions are added to the process of particles updating,to increase the diversity of particles and avoid local optima in searching.Verified with three benchmark datasets,MNIST,Fashion-MNIST,and CIFAR10,it is demonstrated that the proposed scheme can achieve accuracies of 99.58%,93.39%,and 78.96%,respectively,improving the accuracy by about 0.1%,0.5%,and 2%,respectively,compared with that of the PSO.
基金supported by the Innovation Foundation of Shanghai University(Grant No.SHUCX112371)
文摘This paper discusses a kind of optimal method used for searching flat panel display （FPD） scanning matrix. The method adopts bionic algorithm： genetic algorithm （GA） and particle swarm optimization （PSO） algorithm. The method using single GA is more time-consuming, and the search efficiency is low in later evolution; the PSO algorithm is easily falling into the local optimal solution and appears the premature convergent phenomenon. Hence, a hybrid approach of GAPSO is found to optimize the search for high grayscale weights scanning matrix. Finally in the acceptable time, it finds a weight scanning matrix （WSM） of 256 gray scales with Matlab, whose scanning efficiency reaches 94.73% and the linearity is very good.
基金supported by the Science and Technology Projects in Guangzhou,Youth Science Fund Projects(No.202201010477).
文摘The development of society is inseparable from the use of traditional burning energy.However,people’s excessive exploitation of fossil energy has led to the gradual shortage of fossil energy.It is essential to find New Energy(NE)and develop a new energy industry.The natural ecosystem has the characteristics of stable development.With the development of Artificial Intelligence(AI),the structure of the natural ecosystem has been applied to the NE industry,forming an NE industry ecological integration system.This paper uses Particle Swarm Optimization(PSO)algorithm to optimize the structure and resources of the NE industry,so that the NE industry has the capability of sustainable development.The traditional NE industry and the NE innovation industry ecological integration system based on PSO algorithm are compared.The experimental results show that in the NE vehicle industry,the average economic benefits of the traditional NE industry and the NE innovation industry ecosystem based on PSO algorithm are 63.6%and 77.2%,respectively.In the NE power generation industry,the average economic benefits of the traditional NE industry and the NE innovation industry ecosystem based on PSO algorithm are 67.6%and 80.4%,respectively.Therefore,in the context of AI,the application of PSO algorithm to the ecological integration system of NE industry could improve the economic benefits of NE industry.
文摘Schizophrenia is a severe mental illness responsible for many of the world’s disabilities.It significantly impacts human society;thus,rapid,and efficient identification is required.This research aims to diagnose schizophrenia directly from a high-resolution camera,which can capture the subtle micro facial expressions that are difficult to spot with the help of the naked eye.In a clinical study by a team of experts at Bahawal Victoria Hospital(BVH),Bahawalpur,Pakistan,there were 300 people with schizophrenia and 299 healthy subjects.Videos of these participants have been captured and converted into their frames using the OpenFace tool.Additionally,pose,gaze,Action Units(AUs),and land-marked features have been extracted in the Comma Separated Values(CSV)file.Aligned faces have been used to detect schizophrenia by the proposed and the pre-trained Convolutional Neural Network(CNN)models,i.e.,VGG16,Mobile Net,Efficient Net,Google Net,and ResNet50.Moreover,Vision transformer,Swim transformer,big transformer,and vision transformer without attention have also been used to train the models on customized dataset.CSV files have been used to train a model using logistic regression,decision trees,random forest,gradient boosting,and support vector machine classifiers.Moreover,the parameters of the proposed CNN architecture have been optimized using the Particle Swarm Optimization algorithm.The experimental results showed a validation accuracy of 99.6%for the proposed CNN model.The results demonstrated that the reported method is superior to the previous methodologies.The model can be deployed in a real-time environment.
基金supported by the National Natural Science Foundation of China(Nos.62071256 and 62071263)。
文摘A metasurface-loaded 1×2 patch array antenna assisted by a deep-learning optimization method is proposed to realize port and radiation pattern decoupling simultaneously to enhance the isolation among elements in multi-input multi-output (MIMO) systems. The deep-learning-assisted optimization method uses an artificial neural network (ANN) and a particle swarm optimization (PSO) algorithm to seek the optimal structure of the antenna to achieve port decoupling with undistorted radiation patterns. The ANN is trained to describe the nonlinear relationship between the geometric parameters and the responses of the antenna. The PSO algorithm, guided by the cost function and number of iterations, is used to optimize the structure of the antenna according to the cost function combined with the trained ANN. Finally, by constraining the cost function, we obtain a 1×2 patch array antenna with a metasurface fixed above by studs, which achieves port and radiation pattern decoupling simultaneously. To validate the principle and design method, we designed, fabricated, and measured an antenna prototype with dimensions of 0.88λ_(0)×0.47λ_(0)×0.21λ_(0) (λ_(0) is the wavelength in free space at the center frequency). The measured fractional bandwidth is 8% (4.8–5.2 GHz). The isolation of the two-element patch antenna increases from 7.6 dB to 24.3 dB with an envelope correlation coefficient (ECC) of <0.0005 at 0.35λ_(0). Moreover, the H-plane radiation pattern of each element is consistent and symmetric in the broadside direction. These characteristics make the proposed antenna suitable for MIMO antenna systems with close spacing.
基金Proposal Number:EEQ-2016-000263,Financially supported by Department of Science and Technology(DST),Science and Engineering Research Board(SERB),Govt.of India,New Delhi,India.
文摘Purpose:The increase in plug-in electric vehicles(PEVs)is likely to see a noteworthy impact on the distribution system due to high electric power consumption during charging and uncertainty in charging behavior.To address this problem,the present work mainly focuses on optimal integration of distributed generators(DG)into radial distribution systems in the presence of PEV loads with their charging behavior under daily load pattern including load models by considering the daily(24 h)power loss and voltage improvement of the system as objectives for better system performance.Design/methodology/approach:To achieve the desired outcomes,an efficient weighted factor multi-objective function is modeled.Particle Swarm Optimization(PSO)and Butterfly Optimization(BO)algorithms are selected and implemented to minimize the objectives of the system.A repetitive backward-forward sweep-based load flow has been introduced to calculate the daily power loss and bus voltages of the radial distribution system.The simulations are carried out using MATLAB software.Findings:The simulation outcomes reveal that the proposed approach definitely improved the system performance in all aspects.Among PSO and BO,BO is comparatively successful in achieving the desired objectives.Originality/value:The main contribution of this paper is the formulation of the multi-objective function that can address daily active power loss and voltage deviation under 24-h load pattern including grouping of residential,industrial and commercial loads.Introduction of repetitive backward-forward sweep-based load flow and the modeling of PEV load with two different charging scenarios.
文摘The execution of the gaits generated with the help of a gait planner is a crucial task in biped locomotion. This task is to be achieved with the help of a suitable torque based controller to ensure smooth walk of the biped robot. It is important to note that the success of the developed proportion integration differentiation （PID） controller depends on the selected gains of the controller. In the present study, an attempt is made to tune the gains of the PID controller for the biped robot ascending and descending the stair case and sloping surface with the help of two non-traditional optimization algorithms, namely modified chaotic invasive weed optimization （MCIWO） and particle swarm optimization （PSO） algorithms. Once the optimal PID controllers are developed, a simulation study has been conducted in computer for obtaining the optimal tuning parameters of the controller of the biped robot. Finally, the optimal gait angles obtained by using the best controller are fed to the real biped robot and found that the biped robot has successfully negotiated the said terrains.
基金supported by The Training Plan of Young Backbone Teachers in Colleges and Universities of Henan Province(2018GGJS175:Research on Intelligent Power Management System).
文摘In the present paper,the distribution feeder reconfiguration in the presence of distributed generation resources(DGR)and energy storage systems(ESS)is solved in the dynamic form.Since studies on the reconfiguration problem have ignored the grid security and reliability,the non-distributed energy index along with the energy loss and voltage stability indices has been assumed as the objective functions of the given problem.To achieve the mentioned benefits,there are several practical plans in the distribution network.One of these applications is the network rearrangement plan,which is the simplest and least expensive way to add equipment to the network.Besides,by adding the DGRs to the distribution grid,the radial mode of the grid and the one-sided passage of power are eliminated,and the ordinary and simple grid is replaced with a complex grid.In this paper,an improved particle clustering algorithm is used to solve the distribution network rearrangement problem with the presence of distributed generation sources.The PQ model and the PV model are both considered,and for this purpose,a model based on the compensation technique is used to model the PV busbars.The proposed developed model has particularly improved the local and global search of this algorithm.The reconfiguration problem is discussed and investigated considering different scenarios in a standard 33-bus grid as a well-known power system in different scenarios in the presence and absence of the DGRs.Then,the obtained results are compared.
文摘In order to overcome the drawbacks of standard particle swarm optimization(PSO)algorithm,such as prematurity and easily trapping in local optimum,a modified PSO algorithm is proposed,in which special techniques,as global best perturbation and inertia weight jump threshold are adopted.The convergence speed and accuracy of the algo-rithm are improved.The test by some benchmark problems shows that the proposed algorithm achieves relatively higher performance.Thereafter,the applications of the modified PSO in the radiation pattern synthesis of antenna arrays are presented.
