Abstract Objective To develop a new technique for assessing the risk of birth defects, which are a major cause of infant mortality and disability in many parts of the world. Methods The region of interest in this stud...Abstract Objective To develop a new technique for assessing the risk of birth defects, which are a major cause of infant mortality and disability in many parts of the world. Methods The region of interest in this study was Heshun County, the county in China with the highest rate of neural tube defects （NTDs）. A hybrid particle swarm optimization/ant colony optimization （PSO/ACO） algorithm was used to quantify the probability of NTDs occurring at villages with no births. The hybrid PSO/ACO algorithm is a form of artificial intelligence adapted for hierarchical classification. It is a powerful technique for modeling complex problems involving impacts of causes. Results The algorithm was easy to apply, with the accuracy of the results being 69.5%＋7.02% at the 95% confidence level. Conclusion The proposed method is simple to apply, has acceptable fault tolerance, and greatly enhances the accuracy of calculations.展开更多
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
Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines A...Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.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
基金supported by National Natural Science Foundation of China(No.41101431)the fourth installment special funding of China Postdoctoral Science Foundation(No.201104003)+1 种基金China Postdoctoral Science Foundation(No.20100470004)the State Key Funds of Social Science Project(Research on Disability Prevention Measurement in China,No.09&ZD072)
文摘Abstract Objective To develop a new technique for assessing the risk of birth defects, which are a major cause of infant mortality and disability in many parts of the world. Methods The region of interest in this study was Heshun County, the county in China with the highest rate of neural tube defects （NTDs）. A hybrid particle swarm optimization/ant colony optimization （PSO/ACO） algorithm was used to quantify the probability of NTDs occurring at villages with no births. The hybrid PSO/ACO algorithm is a form of artificial intelligence adapted for hierarchical classification. It is a powerful technique for modeling complex problems involving impacts of causes. Results The algorithm was easy to apply, with the accuracy of the results being 69.5%＋7.02% at the 95% confidence level. Conclusion The proposed method is simple to apply, has acceptable fault tolerance, and greatly enhances the accuracy of calculations.
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.
文摘Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.
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.
