This study introduces a novel algorithm known as the dung beetle optimization algorithm based on bounded reflection optimization andmulti-strategy fusion(BFDBO),which is designed to tackle the complexities associated ...This study introduces a novel algorithm known as the dung beetle optimization algorithm based on bounded reflection optimization andmulti-strategy fusion(BFDBO),which is designed to tackle the complexities associated with multi-UAV collaborative trajectory planning in intricate battlefield environments.Initially,a collaborative planning cost function for the multi-UAV system is formulated,thereby converting the trajectory planning challenge into an optimization problem.Building on the foundational dung beetle optimization(DBO)algorithm,BFDBO incorporates three significant innovations:a boundary reflection mechanism,an adaptive mixed exploration strategy,and a dynamic multi-scale mutation strategy.These enhancements are intended to optimize the equilibrium between local exploration and global exploitation,facilitating the discovery of globally optimal trajectories thatminimize the cost function.Numerical simulations utilizing the CEC2022 benchmark function indicate that all three enhancements of BFDBOpositively influence its performance,resulting in accelerated convergence and improved optimization accuracy relative to leading optimization algorithms.In two battlefield scenarios of varying complexities,BFDBO achieved a minimum of a 39% reduction in total trajectory planning costs when compared to DBO and three other highperformance variants,while also demonstrating superior average runtime.This evidence underscores the effectiveness and applicability of BFDBO in practical,real-world contexts.展开更多
This paper aims to address the problem of multi-UAV cooperative search for multiple targets in a mountainous environment,considering the constraints of UAV dynamics and prior environmental information.Firstly,using th...This paper aims to address the problem of multi-UAV cooperative search for multiple targets in a mountainous environment,considering the constraints of UAV dynamics and prior environmental information.Firstly,using the target probability distribution map,two strategies of information fusion and information diffusion are employed to solve the problem of environmental information inconsistency caused by different UAVs searching different areas,thereby improving the coordination of UAV groups.Secondly,the task region is decomposed into several high-value sub-regions by using data clustering method.Based on this,a hierarchical search strategy is proposed,which allows precise or rough search in different probability areas by adjusting the altitude of the aircraft,thereby improving the search efficiency.Third,the Elite Dung Beetle Optimization Algorithm(EDBOA)is proposed based on bionics by accurately simulating the social behavior of dung beetles to plan paths that satisfy the UAV dynamics constraints and adapt to the mountainous terrain,where the mountain is considered as an obstacle to be avoided.Finally,the objective function for path optimization is formulated by considering factors such as coverage within the task region,smoothness of the search path,and path length.The effectiveness and superiority of the proposed schemes are verified by the simulation.展开更多
Power prediction has been critical in large-scale wind power grid connections.However,traditional wind power prediction methods have long suffered from problems,for instance low prediction accuracy and poor reliabilit...Power prediction has been critical in large-scale wind power grid connections.However,traditional wind power prediction methods have long suffered from problems,for instance low prediction accuracy and poor reliability.For this purpose,a hybrid prediction model(VMD-LSTM-Attention)has been proposed,which integrates the variational modal decomposition(VMD),the long short-term memory(LSTM),and the attention mechanism(Attention),and has been optimized by improved dung beetle optimization algorithm(IDBO).Firstly,the algorithm's performance has been significantly enhanced through the implementation of three key strategies,namely the elite group strategy of the Logistic-Tent map,the nonlinear adjustment factor,and the adaptive T-distribution disturbance mechanism.Subsequently,IDBO has been applied to optimize the important parameters of VMD(decomposition layers and penalty factors)to ensure the best decomposition signal is obtained;Furthermore,the IDBO has been deployed to optimize the three key hyper-parameters of the LSTM,thereby improving its learning capability.Finally,an Attention mechanism has been incorporated to adaptively weight temporal features,thus increasing the model's ability to focus on key information.Comprehensive simulation experiments have demonstrated that the proposed model achieves higher prediction accuracy compared with VMD-LSTM,VMD-LSTM-Attention,and traditional prediction methods,and quantitative indexes verify the efectiveness of the algorithmic improvement as well as the excellence and precision of the model in wind power prediction.展开更多
基金funded by the National Defense Science and Technology Innovation project,grant number ZZKY20223103the Basic Frontier InnovationProject at the Engineering University of PAP,grant number WJY202429+2 种基金the Basic Frontier lnnovation Project at the Engineering University of PAP,grant number WJY202408the Graduate Student Funding Priority Project,grant number JYWJ2024B006Key project of National Social Science Foundation,grant number 2023-SKJJ-A-116.
文摘This study introduces a novel algorithm known as the dung beetle optimization algorithm based on bounded reflection optimization andmulti-strategy fusion(BFDBO),which is designed to tackle the complexities associated with multi-UAV collaborative trajectory planning in intricate battlefield environments.Initially,a collaborative planning cost function for the multi-UAV system is formulated,thereby converting the trajectory planning challenge into an optimization problem.Building on the foundational dung beetle optimization(DBO)algorithm,BFDBO incorporates three significant innovations:a boundary reflection mechanism,an adaptive mixed exploration strategy,and a dynamic multi-scale mutation strategy.These enhancements are intended to optimize the equilibrium between local exploration and global exploitation,facilitating the discovery of globally optimal trajectories thatminimize the cost function.Numerical simulations utilizing the CEC2022 benchmark function indicate that all three enhancements of BFDBOpositively influence its performance,resulting in accelerated convergence and improved optimization accuracy relative to leading optimization algorithms.In two battlefield scenarios of varying complexities,BFDBO achieved a minimum of a 39% reduction in total trajectory planning costs when compared to DBO and three other highperformance variants,while also demonstrating superior average runtime.This evidence underscores the effectiveness and applicability of BFDBO in practical,real-world contexts.
基金supported by the Natural Science Foundation of China(62273068)the Fundamental Research Funds for the Central Universities(3132023512)Dalian Science and Technology Innovation Fund(2019J12GX040).
文摘This paper aims to address the problem of multi-UAV cooperative search for multiple targets in a mountainous environment,considering the constraints of UAV dynamics and prior environmental information.Firstly,using the target probability distribution map,two strategies of information fusion and information diffusion are employed to solve the problem of environmental information inconsistency caused by different UAVs searching different areas,thereby improving the coordination of UAV groups.Secondly,the task region is decomposed into several high-value sub-regions by using data clustering method.Based on this,a hierarchical search strategy is proposed,which allows precise or rough search in different probability areas by adjusting the altitude of the aircraft,thereby improving the search efficiency.Third,the Elite Dung Beetle Optimization Algorithm(EDBOA)is proposed based on bionics by accurately simulating the social behavior of dung beetles to plan paths that satisfy the UAV dynamics constraints and adapt to the mountainous terrain,where the mountain is considered as an obstacle to be avoided.Finally,the objective function for path optimization is formulated by considering factors such as coverage within the task region,smoothness of the search path,and path length.The effectiveness and superiority of the proposed schemes are verified by the simulation.
基金the Open Fund of Guangxi Key Laboratory of Building New Energy and Energy Saving(Project Number:Guike Energy 17-J-21-3).
文摘Power prediction has been critical in large-scale wind power grid connections.However,traditional wind power prediction methods have long suffered from problems,for instance low prediction accuracy and poor reliability.For this purpose,a hybrid prediction model(VMD-LSTM-Attention)has been proposed,which integrates the variational modal decomposition(VMD),the long short-term memory(LSTM),and the attention mechanism(Attention),and has been optimized by improved dung beetle optimization algorithm(IDBO).Firstly,the algorithm's performance has been significantly enhanced through the implementation of three key strategies,namely the elite group strategy of the Logistic-Tent map,the nonlinear adjustment factor,and the adaptive T-distribution disturbance mechanism.Subsequently,IDBO has been applied to optimize the important parameters of VMD(decomposition layers and penalty factors)to ensure the best decomposition signal is obtained;Furthermore,the IDBO has been deployed to optimize the three key hyper-parameters of the LSTM,thereby improving its learning capability.Finally,an Attention mechanism has been incorporated to adaptively weight temporal features,thus increasing the model's ability to focus on key information.Comprehensive simulation experiments have demonstrated that the proposed model achieves higher prediction accuracy compared with VMD-LSTM,VMD-LSTM-Attention,and traditional prediction methods,and quantitative indexes verify the efectiveness of the algorithmic improvement as well as the excellence and precision of the model in wind power prediction.
