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PID Steering Control Method of Agricultural Robot Based on Fusion of Particle Swarm Optimization and Genetic Algorithm 认领 引用 被引量：4
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作者 ZHAO Longlian ZHANG Jiachuang +2 位作者 LI Mei DONG Zhicheng LI Junhui 《农业机械学报》 EI CAS CSCD 北大核心 2026年第1期358-367,共10页
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion... Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots. 展开更多
关键词 agricultural robot steering PID control particle swarm optimization algorithm genetic algorithm
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Remanufacturing closed-loop supply chain network design based on genetic particle swarm optimization algorithm 认领 引用 被引量：11
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作者 周鲜成 赵志学 +1 位作者 周开军 贺彩虹 《Journal of Central South University》 SCIE EI CAS 2012年第2期482-487,共6页
As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of c... As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms. 展开更多
关键词 genetic particle swarm optimization closed-loop supply chain remanufacturing network design reverse logistics
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Robot stereo vision calibration method with genetic algorithm and particle swarm optimization 认领 引用 被引量：2
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作者 汪首坤 李德龙 +1 位作者 郭俊杰 王军政 《Journal of Beijing Institute of Technology》 EI CAS 2013年第2期213-221,共9页
Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a ... Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation. 展开更多
关键词 robot stereo vision camera calibration genetic algorithm （GA） particle swarm opti-mization （PSO） hybrid intelligent optimization
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Dynamic neighborhood genetic learning particle swarm optimization for high-power-density electric propulsion motor 认领 引用 被引量：4
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作者 Jinquan XU Huapeng LIN Hong GUO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第12期253-265,共13页
To maximize the power density of the electric propulsion motor in aerospace application,this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization(DNGL-PSO)for the motor design,which... To maximize the power density of the electric propulsion motor in aerospace application,this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization(DNGL-PSO)for the motor design,which can deal with the insufficient population diversity and non-global optimal solution issues.The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module.To improve the population diversity,the dynamic neighborhood strategy is first proposed,which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism.The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space,thus obtaining highquality exemplars.Meanwhile,when the global optimal solution cannot update its fitness value,the shuffling mechanism module is triggered to dynamically change the local neighborhood members.The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood.Then,the global learning based particle update approach is proposed,which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage.Finally,the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO.The simulation results show that the proposed DNGL-PSO has excellent adaptability,optimization efficiency and global optimization capability,while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%. 展开更多
关键词 Dynamic Neighborhood Genetic Learning Particle Swarm Optimization(DNGL-PSO) Permanent magnet synchronous motor Power density Efficiency of motor Electric propulsion motor
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Genetic algorithm and particle swarm optimization tuned fuzzy PID controller on direct torque control of dual star induction motor 认领 引用 被引量：21
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作者 BOUKHALFA Ghoulemallah BELKACEM Sebti +1 位作者 CHIKHI Abdesselem BENAGGOUNE Said 《Journal of Central South University》 SCIE EI CAS CSCD 2019年第7期1886-1896,共11页
This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different he... This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance. 展开更多
关键词 dual star induction motor drive direct torque control particle swarm optimization (PSO) fuzzy logic control genetic algorithms
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DOA and Power Estimation Using Genetic Algorithm and Fuzzy Discrete Particle Swarm Optimization 认领 引用 被引量：3
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作者 Jia-Zhou Liu Zhi-Qin Zhao +1 位作者 Zi-Yuan He Qing-Huo Liu 《Journal of Electronic Science and Technology》 CAS 2014年第1期71-75,共5页
Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival （DOA）. In this method, a gen... Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival （DOA）. In this method, a genetic algorithm （GA） and fuzzy discrete particle swarm optimization （FDPSO） are applied to optimize the direction of arrival and power parameters of the mode simultaneously. Firstly, the GA algorithm is applied to make the solution fall into the global searching. Secondly, the FDPSO method is utilized to narrow down the search field. In FDPSO, a chaotic factor and a crossover method are added to speed up the convergence. This approach has been demonstrated through some computational simulations. It is shown that the proposed algorithm can estimate both the DOA and the powers accurately. It is more efficient than some present methods, such as the Newton-like algorithm, Akaike information critical （AIC）, particle swarm optimization （PSO）, and genetic algorithm with particle swarm optimization （GA-PSO）. 展开更多
关键词 Direction of arrival genetic algorithm,particle swarm optimization.
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Optimization of Fairhurst-Cook Model for 2-D Wing Cracks Using Ant Colony Optimization (ACO), Particle Swarm Intelligence (PSO), and Genetic Algorithm (GA) 认领 引用 被引量：1
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作者 Mohammad Najjarpour Hossein Jalalifar 《Journal of Applied Mathematics and Physics》 2018年第8期1581-1595,共15页
The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the slid... The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack. 展开更多
关键词 Wing Crack Fairhorst-Cook Model Sensitivity Analysis Optimization Particle Swarm Intelligence (PSO) Ant Colony Optimization (ACO) Genetic Algorithm (GA)
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Particle Swarm Optimization Algorithm vs Genetic Algorithm to Develop Integrated Scheme for Obtaining Optimal Mechanical Structure and Adaptive Controller of a Robot 认领 引用
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作者 Rega Rajendra Dilip K. Pratihar 《Intelligent Control and Automation》 2011年第4期430-449,共20页
The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipula... The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected. 展开更多
关键词 Manipulator Optimal Structure Adaptive Controller Genetic Algorithm Neural Networks Particle Swarm Optimization
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Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights 认领 引用 被引量：14
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作者 Hai-tao Chen Wen-chuan Wang +1 位作者 Xiao-nan Chen Lin Qiu 《Water Science and Engineering》 EI CAS CSCD 2020年第2期136-144,共9页
Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algori... Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified. 展开更多
关键词 Particle swarm optimization Genetic algorithm Random inertia weight Multi-objective reservoir operation Reservoir group Panjiakou Reservoir
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A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization 认领 引用 被引量：9
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作者 Zhenyu Lei Shangce Gao +2 位作者 Zhiming Zhang Haichuan Yang Haotian Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第5期1168-1180,共13页
Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that red... Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems. 展开更多
关键词 Chaotic local search(CLS) evolutionary computation genetic learning particle swarm optimization(PSO) wake effect wind farm layout optimization(WFLO)
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Optimization of Vertical Well Placement by Using a Hybrid Particle Swarm Optimization 认领 引用 被引量：1
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作者 DONG Xiaojian WU Zhijian +2 位作者 DONG Chao CHEN Zhangxin WANG Hui 《Wuhan University Journal of Natural Sciences》 CAS 2011年第3期237-240,共4页
Locating wells is an important step in oil exploitation. This paper proposes a novel approach, which first combines particle swarm optimization, genetic algorithm, and a reservoir simulation evaluation tool to optimiz... Locating wells is an important step in oil exploitation. This paper proposes a novel approach, which first combines particle swarm optimization, genetic algorithm, and a reservoir simulation evaluation tool to optimize the locations of vertical wells. Simulation results show that the convergence efficiency of our approach outperforms traditional genetic algorithm and overcomes the disadvantage of particle swarm algorithm that would be easily trapped into best-at-local solution so that its optimization result has been significantly improved. 展开更多
关键词 well location optimization particle swarm optimization genetic algorithm （GA） global optimization
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Optimal Linear Phase Finite Impulse Response Band Pass Filter Design Using Craziness Based Particle Swarm Optimization Algorithm 认领 引用
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作者 SANGEETA Mandal SAKTI Prasad Ghoshal +1 位作者 RAJIB Kar DURBADAL Mandal 《Journal of Shanghai Jiaotong university(Science)》 EI 2011年第6期696-703,共8页
An efficient method is proposed for the design of finite impulse response(FIR) filter with arbitrary pass band edge,stop band edge frequencies and transition width.The proposed FIR band stop filter is designed using c... An efficient method is proposed for the design of finite impulse response(FIR) filter with arbitrary pass band edge,stop band edge frequencies and transition width.The proposed FIR band stop filter is designed using craziness based particle swarm optimization(CRPSO) approach.Given the filter specifications to be realized,the CRPSO algorithm generates a set of optimal filter coefficients and tries to meet the ideal frequency response characteristics.In this paper,for the given problem,the realizations of the optimal FIR band pass filters of different orders have been performed.The simulation results have been compared with those obtained by the well accepted evolutionary algorithms,such as Parks and McClellan algorithm(PMA),genetic algorithm(GA) and classical particle swarm optimization(PSO).Several numerical design examples justify that the proposed optimal filter design approach using CRPSO outperforms PMA and PSO,not only in the accuracy of the designed filter but also in the convergence speed and solution quality. 展开更多
关键词 finite impulse response(FIR) filter particle swarm optimization(PSO) craziness based particle swarm optimization(CRPSO) Parks and McClellan algorithm(PMA) genetic algorithm(GA) optimization
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An Overall Optimization Model Using Metaheuristic Algorithms for the CNN-Based IoT Attack Detection Problem 认领 引用 被引量：1
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作者 Le Thi Hong Van Le Duc Thuan +1 位作者 Pham Van Huong Nguyen Hieu Minh 《Computers, Materials & Continua》 SCIE EI 2026年第4期1934-1964,共31页
Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified... Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications. 展开更多
关键词 Genetic algorithm(GA) particle swarm optimization(PSO) multi-objective optimization convolutional neural network—CNN IoT attack detection metaheuristic optimization CNN configuration
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Brillouin scattering spectrum character extraction based on genetic algorithm and seeker optimization algorithm 认领 引用
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作者 Zhang Yanjun Jin Peijun +3 位作者 Fu Xinghu Hou Jiaoru Zhang Fangcao Xu Jinrui 《High Technology Letters》 EI CAS 2019年第4期401-407,共7页
A new hybrid optimization method based on genetic algorithm(GA)and seeker optimization algorithm(SOA)is presented in this paper.The hybrid algorithm optimizes SOA by using crossover and mutation operations in GA in or... A new hybrid optimization method based on genetic algorithm(GA)and seeker optimization algorithm(SOA)is presented in this paper.The hybrid algorithm optimizes SOA by using crossover and mutation operations in GA in order to improve the global search ability of SOA.Four algorithms,i.e.particle swarm optimization(PSO),SOA,GA and quantum-behaved particle swarm optimization(GA-QPSO)and GA-SOA are used to process the simulation and experimental data of Brillouin scattering spectrum(BSS)at different temperatures.The results show that GA-SOA improves the accuracy of extracting the center frequency shift and the minimum center frequency of Brillouin scattering spectrum compared with other three algorithms.The shift error is 0.203 MHz.Therefore,GA-SOA can be applied to the accurate extraction of BSS characteristics. 展开更多
关键词 Brillouin scattering spectrum(BSS) seeker optimization algorithm(SOA) genetic algorithm(GA) particle swarm optimization(PSO) Brillouin frequency shift(BFS)
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An Effective Non-Commutative Encryption Approach with Optimized Genetic Algorithm for Ensuring Data Protection in Cloud Computing 认领 引用 被引量：2
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作者 S.Jerald Nirmal Kumar S.Ravimaran M.M.Gowthul Alam 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第11期671-697,共27页
Nowadays,succeeding safe communication and protection-sensitive data from unauthorized access above public networks are the main worries in cloud servers.Hence,to secure both data and keys ensuring secured data storag... Nowadays,succeeding safe communication and protection-sensitive data from unauthorized access above public networks are the main worries in cloud servers.Hence,to secure both data and keys ensuring secured data storage and access,our proposed work designs a Novel Quantum Key Distribution(QKD)relying upon a non-commutative encryption framework.It makes use of a Novel Quantum Key Distribution approach,which guarantees high level secured data transmission.Along with this,a shared secret is generated using Diffie Hellman(DH)to certify secured key generation at reduced time complexity.Moreover,a non-commutative approach is used,which effectively allows the users to store and access the encrypted data into the cloud server.Also,to prevent data loss or corruption caused by the insiders in the cloud,Optimized Genetic Algorithm(OGA)is utilized,which effectively recovers the data and retrieve it if the missed data without loss.It is then followed with the decryption process as if requested by the user.Thus our proposed framework ensures authentication and paves way for secure data access,with enhanced performance and reduced complexities experienced with the prior works. 展开更多
关键词 Cloud computing quantum key distribution Diffie Hellman non-commutative approach genetic algorithm particle swarm optimization
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Genetic Based Approach for Optimal Power and Channel Allocation to Enhance D2D Underlaied Cellular Network Capacity in 5G 认领 引用 被引量：1
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作者 Ahmed.A.Rosas Mona Shokair M.I.Dessouky 《Computers, Materials & Continua》 SCIE EI 2022年第8期3751-3762,共12页
With the obvious throughput shortage in traditional cellular radio networks,Device-to-Device(D2D)communications has gained a lot of attention to improve the utilization,capacity and channel performance of nextgenerati... With the obvious throughput shortage in traditional cellular radio networks,Device-to-Device(D2D)communications has gained a lot of attention to improve the utilization,capacity and channel performance of nextgeneration networks.In this paper,we study a joint consideration of power and channel allocation based on genetic algorithm as a promising direction to expand the overall network capacity for D2D underlaied cellular networks.The genetic based algorithm targets allocating more suitable channels to D2D users and finding the optimal transmit powers for all D2D links and cellular users efficiently,aiming to maximize the overall system throughput of D2D underlaied cellular network with minimum interference level,while satisfying the required quality of service QoS of each user.The simulation results show that our proposed approach has an advantage in terms of maximizing the overall system utilization than fixed,random,BAT algorithm(BA)and Particle Swarm Optimization(PSO)based power allocation schemes. 展开更多
关键词 5G D2D communication spectrum allocation power allocation genetic algorithm optimization BAT-optimization particle swarm optimization
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An Optimized Multiclass Support Vector Machine for Recognizing EEG Signals based on a Lower-Limb Exoskeleton 认领 引用
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作者 Kamel K.Mohammed Heba M.Afify Aboul Ella Hassanien 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1605-1620,共16页
The lower-limb prosthesis is used to assist patients with dysfunction of motor dysfunction or aging through Brain-Machine Interface(BMI)based on Electroencephalography(EEG)signals to control cognitive tasks.This paper... The lower-limb prosthesis is used to assist patients with dysfunction of motor dysfunction or aging through Brain-Machine Interface(BMI)based on Electroencephalography(EEG)signals to control cognitive tasks.This paper presents a remarkable model to improve the estimation of the EEG signal and further help improve the control performance for the lower-limb prosthesis,and then improve the rehabilitation.It is based on an optimized Multiclass Support Vector Machine(MSVM)using Snake Optimizer(SO)to get the best possible parameter tuning for classifying different cognitive tasks to control of lower-limb exoskeleton.A public EEG dataset for a lower-limb exoskeleton using Motor Imagery(MI)during the control of the prosthesis and attention to gait(Att)on two surfaces,including flat(Experience)and non-flat(Slopes),has been used as benchmark data sets for this work.The results of the proposed model revealed the superiority of this technique in accuracy,compared with two optimization methods,including Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By comparing the outcomes of SO-MSVM with state of the arts,it achieved an accuracy of more than 85%for MI and Att metric,demonstrating intriguing results for solving the rehabilitation challenge.The devised technique could help people with neurological conditions who have trouble using manual controls. 展开更多
关键词 Electroencephalography(EEG) Brain-Machine Interface(BMI) Multiclass Support Vector Machine(MSVM) Genetic Algorithm(GA) Particle Swarm Optimization(PSO) and Snake Optimization(SO)
Planning of DC Electric Spring with Particle Swarm Optimization and Elitist Non-dominated Sorting Genetic Algorithm 认领 引用 被引量：3
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作者 Qingsong Wang Siwei Li +2 位作者 Hao Ding Ming Cheng Giuseppe Buja 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2024年第2期574-583,共10页
This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical... This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical load (NCL) and internal storage. It can offer higher power quality to critical load (CL), reduce power imbalance and relieve pressure on energy storage systems (RESs). In this paper, a planning method for parallel DCESs is proposed to maximize stability gain, economic benefits, and penetration of RESs. The planning model is a master optimization with sub-optimization to highlight the priority of objectives. Master optimization is used to improve stability of the network, and sub-optimization aims to improve economic benefit and allowable penetration of RESs. This issue is a multivariable nonlinear mixed integer problem, requiring huge calculations by using common solvers. Therefore, particle Swarm optimization (PSO) and Elitist non-dominated sorting genetic algorithm (NSGA-II) were used to solve this model. Considering uncertainty of RESs, this paper verifies effectiveness of the proposed planning method on IEEE 33-bus system based on deterministic scenarios obtained by scenario analysis. 展开更多
关键词 DC distribution network DC electric spring non-dominated sorting genetic algorithm particle swarm optimization renewable energy source
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Enhancing resource allocation in edge and fog-cloud computing with genetic algorithm and particle swarm optimization 认领 引用 被引量：1
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作者 Saad-Eddine Chafi Younes Balboul +2 位作者 Mohammed Fattah Said Mazer Moulhime El Bekkali 《Intelligent and Converged Networks》 EI 2023年第4期273-279,共7页
Evolutionary algorithms have gained significant attention from researchers as effective solutions for various optimization problems.Genetic Algorithm(GA)is widely popular due to its logical approach,broad applicabilit... Evolutionary algorithms have gained significant attention from researchers as effective solutions for various optimization problems.Genetic Algorithm(GA)is widely popular due to its logical approach,broad applicability,and ability to tackle complex issues encountered in engineering systems.However,GA is known for its high implementation cost and typically requires a large number of iterations.On the other hand,Particle Swarm Optimization(PSO)is a relatively new heuristic technique inspired by the collective behaviors of real organisms.Both GA and PSO algorithms are prominent heuristic optimization methods that belong to the population-based approaches family.While they are often seen as competitors,their efficiency heavily relies on the parameter values chosen and the specific optimization problem at hand.In this study,we aim to compare the runtime performance of GA and PSO algorithms within a cutting-edge edge and fog cloud architecture.Through extensive experiments and performance evaluations,the authors demonstrate the effectiveness of GA and PSO algorithms in improving resource allocation in edge and fog cloud computing scenarios using FogWorkflowSim simulator.The comparative analysis sheds light on the strengths and limitations of each algorithm,providing valuable insights for researchers and practitioners in the field. 展开更多
关键词 particle swarm optimization genetic algorithm performance evaluation edge and fog cloud FogWorkflowSim
Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Parameter Calibration in Hydrological Simulation 认领 引用
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作者 Xinyu Zhang Yang Li Genshen Chu 《Data Intelligence》 EI 2023年第4期904-922,共19页
Parameter calibration is an important part of hydrological simulation and affects the final simulation results.In this paper,we introduce heuristic optimization algorithms,genetic algorithm(GA)to cope with the complex... Parameter calibration is an important part of hydrological simulation and affects the final simulation results.In this paper,we introduce heuristic optimization algorithms,genetic algorithm(GA)to cope with the complexity of the parameter calibration problem,and use particle swarm optimization algorithm(PsO)as a comparison.For large-scale hydrological simulations,we use a multilevel parallel parameter calibration framework to make full use of processor resources,and accelerate the process of solving high-dimensional parameter calibration.Further,we test and apply the experiments on domestic supercomputers.The results of parameter calibration with GA and PSO can basically reach the ideal value of 0.65 and above,with PSO achieving a speedup of 58.52 on TianHe-2 supercomputer.The experimental results indicate that using a parallel implementation on multicore CPUs makes high-dimensional parameter calibration in large-scale hydrological simulation possible.Moreover,our comparison of the two algorithms shows that the GA obtains better calibration results,and the PSO has a more pronounced acceleration effect. 展开更多
关键词 Hydrologic simulation Parameter calibration Genetic algorithm Particle swarm optimization
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