The advent of microgrids in modern energy systems heralds a promising era of resilience,sustainability,and efficiency.Within the realm of grid-tied microgrids,the selection of an optimal optimization algorithm is crit...The advent of microgrids in modern energy systems heralds a promising era of resilience,sustainability,and efficiency.Within the realm of grid-tied microgrids,the selection of an optimal optimization algorithm is critical for effective energy management,particularly in economic dispatching.This study compares the performance of Particle Swarm Optimization(PSO)and Genetic Algorithms(GA)in microgrid energy management systems,implemented using MATLAB tools.Through a comprehensive review of the literature and sim-ulations conducted in MATLAB,the study analyzes performance metrics,convergence speed,and the overall efficacy of GA and PSO,with a focus on economic dispatching tasks.Notably,a significant distinction emerges between the cost curves generated by the two algo-rithms for microgrid operation,with the PSO algorithm consistently resulting in lower costs due to its effective economic dispatching capabilities.Specifically,the utilization of the PSO approach could potentially lead to substantial savings on the power bill,amounting to approximately$15.30 in this evaluation.Thefindings provide insights into the strengths and limitations of each algorithm within the complex dynamics of grid-tied microgrids,thereby assisting stakeholders and researchers in arriving at informed decisions.This study contributes to the discourse on sustainable energy management by offering actionable guidance for the advancement of grid-tied micro-grid technologies through MATLAB-implemented optimization algorithms.展开更多
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
Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for t...Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.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
In view of characteristics of particle swarm optimization ( PSO ) algorithm of fast convergence but easily falling into local optimum value , a novel improved particle swarm optimization algorithm is put forward , and...In view of characteristics of particle swarm optimization ( PSO ) algorithm of fast convergence but easily falling into local optimum value , a novel improved particle swarm optimization algorithm is put forward , and it is applicable to identify parameters of hydraulic pressure system model in strip rolling process.In order to maintain population diversity and enhance global optimization capability , the algorithm is firstly improved by means of decreasing its inertia weight linearly from the maximum to the minimum and then combined with chaotic characteristics of ergodicity , randomness and sensitivity to initial value.When the improved algorithm is used to identify parameters of hydraulic pressure system , the comparison of simulation curves and measured curves indicates that the identification results are reliable and close to actual situation.A new method was provided for hydraulic AGC system model identification.展开更多
Objective:To explore core acupoints and acupoint selection principles in acupuncture and moxibustion for obesity,from syndrome differentiation prescriptions of the acupuncture-moxibustion therapy in 808 obesity prescr...Objective:To explore core acupoints and acupoint selection principles in acupuncture and moxibustion for obesity,from syndrome differentiation prescriptions of the acupuncture-moxibustion therapy in 808 obesity prescriptions,by using node centrality and cluster analysis methods in complex network.Methods:Firstly,an acupoint network model is established,and acupoint nodes are assessed and calculated in multiple aspects by introducing the node centrality analysis idea of complex network,to excavate core acupoint nodes.Secondly,a cluster analysis is carried out on acupoint network by the cluster algorithm Q-PSO for complex network,to investigate the acupoint combination principles.Results:Zusanli(足三里ST36),Tianshu(天枢ST25),Fenglong(丰隆ST40),Zhongwan(中脘CV12)and Qihai(气海CV6),etc.,were included into the core acupoint Sanyinjiao(三阴交SP6)community.Zhigou(支沟TE6),Neiting(内庭ST44),Shangjuxu(上巨虚ST37),and Pishu(脾俞BL20)etc.,were included into the core acupoint Yinlingquan(阴陵泉SP9)community.Baihuanshu(白环俞BL30)and Zhiyang(至阳GV9)were included into the core acupoint Dachangshu(大肠俞BL25)community.Biguan(髀关ST31)was a single core community.Among all the acupoint nodes,SP6,ST25,SP9,ST36,CV6,Quchi(曲池L111),and Guanyuan(关元CV4)were of high degree centrality and eigenvector centrality,directly reflecting their importance in acupoint selection prescriptions.Conclusion:The Q-PSO algorithm is characterized with high precision and high efficiency,etc.The core acupoints and their combination principles explored by this algorithm are in accordance with clinical experiences.展开更多
An improved particle swarm optimization(PSO)algorithm based on dynamic inertia weight and adjustment coefficient is proposed in this paper.The expressions of inertia weight and adjustment coefficient are established b...An improved particle swarm optimization(PSO)algorithm based on dynamic inertia weight and adjustment coefficient is proposed in this paper.The expressions of inertia weight and adjustment coefficient are established based on inter-particle distance and iterations.The improved algorithm is applied to a novel two-stage photovoltaic(PV)converter.The later DC/AC circuit chooses a dual-DC-input multi-level dual-buck inverter.This converter has the advantages of no shoot-through problem and high efficiency.Finally,the validity and effectiveness of the algorithm and the converter are verified with experimental results.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
Without any prior information about related wireless transmitting nodes,joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task.Measuring the ...Without any prior information about related wireless transmitting nodes,joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task.Measuring the signal related data based on a group distributed sensor is an efficient way to infer the various characteristics of the signal sources.In this paper,we propose a particle swarm optimization to estimate multiple co-frequency"blind"source nodes,which is based on the received power data measured by the sensors.To distract the mix signals precisely,a genetic algorithm is applied,and it further improves the estimation performance of the system.The simulation results show the efficiency of the proposed algorithm.展开更多
The shop scheduling problem with limited buffers has broad applications in real-world production scenarios,so this research direction is of great practical significance.However,there is currently little research on th...The shop scheduling problem with limited buffers has broad applications in real-world production scenarios,so this research direction is of great practical significance.However,there is currently little research on the hybrid flow shop scheduling problem with limited buffers(LBHFSP).This paper deeply investigates the LBHFSP to optimize the goal of the total completion time.To better solve the LBHFSP,a multi-level subpopulation-based particle swarm optimization algorithm(MLPSO)is proposed,which is founded on the attributes of the LBHFSP and the shortcomings of the basic PSO(particle swarm optimization)algorithm.In MLPSO,firstly,considering the impact of the limited buffers on the process of subsequent operations,a specific circular decoding strategy is developed to accommodate the characteristics of limited buffers.Secondly,an initialization strategy based on blocking time is designed to enhance the quality and diversity of the initial population.Afterward,a multi-level subpopulation collaborative search is developed to prevent being trapped in a local optimum and improve the global exploration capability.Additionally,a local search strategy based on the first blocked job is designed to enhance the MLPSO algorithm’s exploitation capability.Lastly,numerous experiments are carried out to test the performance of the proposed MLPSO by comparing it with classical intelligent optimization and popular algorithms in recent years.The results confirm that the proposed MLPSO has an outstanding performance when compared to other algorithms when solving LBHFSP.展开更多
文摘The advent of microgrids in modern energy systems heralds a promising era of resilience,sustainability,and efficiency.Within the realm of grid-tied microgrids,the selection of an optimal optimization algorithm is critical for effective energy management,particularly in economic dispatching.This study compares the performance of Particle Swarm Optimization(PSO)and Genetic Algorithms(GA)in microgrid energy management systems,implemented using MATLAB tools.Through a comprehensive review of the literature and sim-ulations conducted in MATLAB,the study analyzes performance metrics,convergence speed,and the overall efficacy of GA and PSO,with a focus on economic dispatching tasks.Notably,a significant distinction emerges between the cost curves generated by the two algo-rithms for microgrid operation,with the PSO algorithm consistently resulting in lower costs due to its effective economic dispatching capabilities.Specifically,the utilization of the PSO approach could potentially lead to substantial savings on the power bill,amounting to approximately$15.30 in this evaluation.Thefindings provide insights into the strengths and limitations of each algorithm within the complex dynamics of grid-tied microgrids,thereby assisting stakeholders and researchers in arriving at informed decisions.This study contributes to the discourse on sustainable energy management by offering actionable guidance for the advancement of grid-tied micro-grid technologies through MATLAB-implemented optimization algorithms.
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the National Natural Science Foundation of China(Grant No.62101579).
文摘Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.
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Item Sponsored by National Natural Science Foundation of China ( 51075352 )
文摘In view of characteristics of particle swarm optimization ( PSO ) algorithm of fast convergence but easily falling into local optimum value , a novel improved particle swarm optimization algorithm is put forward , and it is applicable to identify parameters of hydraulic pressure system model in strip rolling process.In order to maintain population diversity and enhance global optimization capability , the algorithm is firstly improved by means of decreasing its inertia weight linearly from the maximum to the minimum and then combined with chaotic characteristics of ergodicity , randomness and sensitivity to initial value.When the improved algorithm is used to identify parameters of hydraulic pressure system , the comparison of simulation curves and measured curves indicates that the identification results are reliable and close to actual situation.A new method was provided for hydraulic AGC system model identification.
基金Supported by Hubei Health & Family Planning Commission Notice (No. [2017]20)Wuhan training project of the sixth batch of young and middle-aged medical talents, wuhan health & family planning commission (Wuhan Health & Family Planning Commission Notice No. [2018]116)Training project of the first batch of tanhualin famous doctors and students (Hubei TCM Hospital No. [2018]72)
文摘Objective:To explore core acupoints and acupoint selection principles in acupuncture and moxibustion for obesity,from syndrome differentiation prescriptions of the acupuncture-moxibustion therapy in 808 obesity prescriptions,by using node centrality and cluster analysis methods in complex network.Methods:Firstly,an acupoint network model is established,and acupoint nodes are assessed and calculated in multiple aspects by introducing the node centrality analysis idea of complex network,to excavate core acupoint nodes.Secondly,a cluster analysis is carried out on acupoint network by the cluster algorithm Q-PSO for complex network,to investigate the acupoint combination principles.Results:Zusanli(足三里ST36),Tianshu(天枢ST25),Fenglong(丰隆ST40),Zhongwan(中脘CV12)and Qihai(气海CV6),etc.,were included into the core acupoint Sanyinjiao(三阴交SP6)community.Zhigou(支沟TE6),Neiting(内庭ST44),Shangjuxu(上巨虚ST37),and Pishu(脾俞BL20)etc.,were included into the core acupoint Yinlingquan(阴陵泉SP9)community.Baihuanshu(白环俞BL30)and Zhiyang(至阳GV9)were included into the core acupoint Dachangshu(大肠俞BL25)community.Biguan(髀关ST31)was a single core community.Among all the acupoint nodes,SP6,ST25,SP9,ST36,CV6,Quchi(曲池L111),and Guanyuan(关元CV4)were of high degree centrality and eigenvector centrality,directly reflecting their importance in acupoint selection prescriptions.Conclusion:The Q-PSO algorithm is characterized with high precision and high efficiency,etc.The core acupoints and their combination principles explored by this algorithm are in accordance with clinical experiences.
文摘An improved particle swarm optimization(PSO)algorithm based on dynamic inertia weight and adjustment coefficient is proposed in this paper.The expressions of inertia weight and adjustment coefficient are established based on inter-particle distance and iterations.The improved algorithm is applied to a novel two-stage photovoltaic(PV)converter.The later DC/AC circuit chooses a dual-DC-input multi-level dual-buck inverter.This converter has the advantages of no shoot-through problem and high efficiency.Finally,the validity and effectiveness of the algorithm and the converter are verified with experimental results.
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.
文摘Without any prior information about related wireless transmitting nodes,joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task.Measuring the signal related data based on a group distributed sensor is an efficient way to infer the various characteristics of the signal sources.In this paper,we propose a particle swarm optimization to estimate multiple co-frequency"blind"source nodes,which is based on the received power data measured by the sensors.To distract the mix signals precisely,a genetic algorithm is applied,and it further improves the estimation performance of the system.The simulation results show the efficiency of the proposed algorithm.
基金supported in part by the National Natural Science Foundation of China under Grant No.52175490.
文摘The shop scheduling problem with limited buffers has broad applications in real-world production scenarios,so this research direction is of great practical significance.However,there is currently little research on the hybrid flow shop scheduling problem with limited buffers(LBHFSP).This paper deeply investigates the LBHFSP to optimize the goal of the total completion time.To better solve the LBHFSP,a multi-level subpopulation-based particle swarm optimization algorithm(MLPSO)is proposed,which is founded on the attributes of the LBHFSP and the shortcomings of the basic PSO(particle swarm optimization)algorithm.In MLPSO,firstly,considering the impact of the limited buffers on the process of subsequent operations,a specific circular decoding strategy is developed to accommodate the characteristics of limited buffers.Secondly,an initialization strategy based on blocking time is designed to enhance the quality and diversity of the initial population.Afterward,a multi-level subpopulation collaborative search is developed to prevent being trapped in a local optimum and improve the global exploration capability.Additionally,a local search strategy based on the first blocked job is designed to enhance the MLPSO algorithm’s exploitation capability.Lastly,numerous experiments are carried out to test the performance of the proposed MLPSO by comparing it with classical intelligent optimization and popular algorithms in recent years.The results confirm that the proposed MLPSO has an outstanding performance when compared to other algorithms when solving LBHFSP.
