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
The dung beetle optimizer(DBO)is a metaheuristic algorithm with fast convergence and powerful search capabilities,which has shown excellent performance in solving various optimization problems.However,it suffers from ...The dung beetle optimizer(DBO)is a metaheuristic algorithm with fast convergence and powerful search capabilities,which has shown excellent performance in solving various optimization problems.However,it suffers from the problems of easily falling into local optimal solutions and poor convergence accuracy when dealing with large-scale complex optimization problems.Therefore,we propose an adaptive DBO(ADBO)based on an elastic annealing mechanism to address these issues.First,the convergence factor is adjusted in a nonlinear decreasing manner to balance the requirements of global exploration and local exploitation,thus improving the convergence speed and search quality.Second,a greedy difference optimization strategy is introduced to increase population diversity,improve the global search capability,and avoid premature convergence.Finally,the elastic annealing mechanism is used to perturb the randomly selected individuals,helping the algorithm escape local optima and thereby improve solution quality and algorithm stability.The experimental results on the CEC 2017 and CEC 2022 benchmark function sets and MCNC benchmark circuits verify the effectiveness,superiority,and universality of ADBO.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
Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China,as well as an important source of modern biomass energy and new materials.Hemicellulose is an important component in corn s...Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China,as well as an important source of modern biomass energy and new materials.Hemicellulose is an important component in corn stalks,and it is very important to determine its content in corn stalks.In this paper,the feasibility of near-infrared spectroscopy(NIRS)combined with chemometrics for rapid detection of hemicellulose content in corn stalks was studied.In order to improve the accuracy of NIRS detection,a new intelligent optimization algorithm,dung beetle optimizer(DBO),was applied to select characteristic wavelengths of NIRS.Its modeling performance was compared with that based on characteristic wavelength selection using genetic algorithm(GA)and binary particle swarm optimization(BPSO),and it was found that the characteristic wavelength selection performance of DBO was excellent,and the regression accuracy of hemicellulose quantitative detection model established by its preferred characteristic wavelengths was better than the above two intelligent optimization algorithms.展开更多
含分布式电源(distributed generation,DG)的双极直流配电系统是未来配电网发展的重要形态之一,但由于DG接入方式、数量、容量、位置以及系统正负极负荷不平衡对系统静暂态电压稳定性影响不同,目前相关研究尚缺乏对此问题的分析。该文...含分布式电源(distributed generation,DG)的双极直流配电系统是未来配电网发展的重要形态之一,但由于DG接入方式、数量、容量、位置以及系统正负极负荷不平衡对系统静暂态电压稳定性影响不同,目前相关研究尚缺乏对此问题的分析。该文首先将DG等效为受控电流源,推导分析了DG接入方式、容量及负荷不平衡度对系统静态下电压不平衡度的影响;其次,基于单极故障下光伏型DG与交流电网暂态放电情况,推导分析了DG接入方式、位置、容量与系统暂态电压稳定性的关系;再者,基于多目标蜣螂优化算法提出以系统静暂态电压稳定性与DG接入成本为目标的DG接入方案规划方法,采用熵权逼近理想解排序法(technique for order preference by similarity to ideal solution,TOPSIS)法筛选出DG接入的最佳折中方案。最后在Matlab/Simulink仿真平台搭建改进IEEE14、IEEE33双极直流配电系统验证该文所提优化方法的普适性和有效性。展开更多
随着各种新型雷达的出现或战时预留模式的采用,真实的战场电磁环境将越加复杂,大概率会出现种类未知且参数突变的雷达调制信号,对现有的调制方式识别算法带来严峻挑战。对此,分析雷达调制方式“未知”对识别结果的影响机理,将开集差分...随着各种新型雷达的出现或战时预留模式的采用,真实的战场电磁环境将越加复杂,大概率会出现种类未知且参数突变的雷达调制信号,对现有的调制方式识别算法带来严峻挑战。对此,分析雷达调制方式“未知”对识别结果的影响机理,将开集差分分布对齐(distribution alignment with open set difference,DAOD)算法引入雷达调制方式识别领域,设计具体应用的技术方案,并针对DAOD算法所需参数依靠先验知识或者试探选取问题,利用蜣螂优化(dung beetle optimizer,DBO)算法进行参数优化。仿真结果表明:在单个雷达调制方式未知情形下,精确度Accuracy和F-measure分值的平均值分别可达91.34%和95.11%;在多个雷达调制方式未知情形下,Accuracy和F-measure的平均值分别可达91.37%、93.69%;与DAOD算法相比,上述结果分别提升了3.77%、1.83%、21.17%和12.06%。因此,DBO-DAOD算法可有效提升未知雷达调制方式的识别率。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Project supported by the National Natural Science Foundation of China(No.62102130)the Central Government Guides Local Science and Technology Development Fund Project of China(No.226Z0201G)+4 种基金the Natural Science Foundation of Hebei Province of China(Nos.F2020204003 and F2024204001)the Hebei Youth Talents Support Project of China(No.BJ2019008)the Science and Technology Research Projects of Higher Education Institutions in Hebei Province of China(No.QN2024138)the Basic Scientific Research Funds Research Project of Hebei Provincial Colleges and Universities of China(No.KY2022073)the Hebei Province Higher Education Institution Scientific Research Project of China(No.QN2025192)。
文摘The dung beetle optimizer(DBO)is a metaheuristic algorithm with fast convergence and powerful search capabilities,which has shown excellent performance in solving various optimization problems.However,it suffers from the problems of easily falling into local optimal solutions and poor convergence accuracy when dealing with large-scale complex optimization problems.Therefore,we propose an adaptive DBO(ADBO)based on an elastic annealing mechanism to address these issues.First,the convergence factor is adjusted in a nonlinear decreasing manner to balance the requirements of global exploration and local exploitation,thus improving the convergence speed and search quality.Second,a greedy difference optimization strategy is introduced to increase population diversity,improve the global search capability,and avoid premature convergence.Finally,the elastic annealing mechanism is used to perturb the randomly selected individuals,helping the algorithm escape local optima and thereby improve solution quality and algorithm stability.The experimental results on the CEC 2017 and CEC 2022 benchmark function sets and MCNC benchmark circuits verify the effectiveness,superiority,and universality of ADBO.
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Supported by San Heng San Zong Project of Heilongjiang Bayi Agricultural University(ZRCPY202314).
文摘Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China,as well as an important source of modern biomass energy and new materials.Hemicellulose is an important component in corn stalks,and it is very important to determine its content in corn stalks.In this paper,the feasibility of near-infrared spectroscopy(NIRS)combined with chemometrics for rapid detection of hemicellulose content in corn stalks was studied.In order to improve the accuracy of NIRS detection,a new intelligent optimization algorithm,dung beetle optimizer(DBO),was applied to select characteristic wavelengths of NIRS.Its modeling performance was compared with that based on characteristic wavelength selection using genetic algorithm(GA)and binary particle swarm optimization(BPSO),and it was found that the characteristic wavelength selection performance of DBO was excellent,and the regression accuracy of hemicellulose quantitative detection model established by its preferred characteristic wavelengths was better than the above two intelligent optimization algorithms.
文摘含分布式电源(distributed generation,DG)的双极直流配电系统是未来配电网发展的重要形态之一,但由于DG接入方式、数量、容量、位置以及系统正负极负荷不平衡对系统静暂态电压稳定性影响不同,目前相关研究尚缺乏对此问题的分析。该文首先将DG等效为受控电流源,推导分析了DG接入方式、容量及负荷不平衡度对系统静态下电压不平衡度的影响;其次,基于单极故障下光伏型DG与交流电网暂态放电情况,推导分析了DG接入方式、位置、容量与系统暂态电压稳定性的关系;再者,基于多目标蜣螂优化算法提出以系统静暂态电压稳定性与DG接入成本为目标的DG接入方案规划方法,采用熵权逼近理想解排序法(technique for order preference by similarity to ideal solution,TOPSIS)法筛选出DG接入的最佳折中方案。最后在Matlab/Simulink仿真平台搭建改进IEEE14、IEEE33双极直流配电系统验证该文所提优化方法的普适性和有效性。
文摘随着各种新型雷达的出现或战时预留模式的采用,真实的战场电磁环境将越加复杂,大概率会出现种类未知且参数突变的雷达调制信号,对现有的调制方式识别算法带来严峻挑战。对此,分析雷达调制方式“未知”对识别结果的影响机理,将开集差分分布对齐(distribution alignment with open set difference,DAOD)算法引入雷达调制方式识别领域,设计具体应用的技术方案,并针对DAOD算法所需参数依靠先验知识或者试探选取问题,利用蜣螂优化(dung beetle optimizer,DBO)算法进行参数优化。仿真结果表明:在单个雷达调制方式未知情形下,精确度Accuracy和F-measure分值的平均值分别可达91.34%和95.11%;在多个雷达调制方式未知情形下,Accuracy和F-measure的平均值分别可达91.37%、93.69%;与DAOD算法相比,上述结果分别提升了3.77%、1.83%、21.17%和12.06%。因此,DBO-DAOD算法可有效提升未知雷达调制方式的识别率。
