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A LOdbo algorithm for multi-UAV search and rescue path planning in disaster areas

Chinese Journal of Aeronautics 2025年第2期38卷 200-213页

作者： Liman Yang Xiangyu Zhang Zhiping Li Lei Li Yan Shi School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

In disaster relief operations,multiple UAVs can be used to search for trapped *** recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path *** Dung Beetle Optimization(dbo)algorithm has been widely applied due to its diverse search patterns in the above ***,the update strategies for the rolling and thieving dung beetles of the dbo algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal *** address these issues,we propose an improved dbo algorithm guided by the Landmark Operator(LOdbo).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search ***,we expand the search range of the rolling ball dung beetle by using the landmark ***,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local *** verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LOdbo algorithm can obtain the optimal path using the shortest time compared with the Genetic algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization algorithm(WOA)and the original dbo algorithm in the disaster search and rescue task set.

关键词： Unmanned aerial vehicle Path planning Meta heuristic algorithm dbo algorithm NP-hard problems

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Robot Manipulator Minimum Jerk Trajectory Planning Based on the Improved Dung Beetle Optimizer algorithm

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2025年第1期34卷

作者： Ma, Haohao As'arry, Azizan Cong, Miaomiao Delgoshaei, Aidin Ismail, Mohd Idris Shah Ramli, Hafiz Rashidi Wu, Xuping Tianshui Normal Univ Fac Electromech Engn Dept Mech Engn Tianshui Gansu Peoples R China Univ Putra Malaysia Fac Engn Dept Mech & Mfg Engn Upm Serdang 43400 Selangor Darul Malaysia Univ Putra Malaysia Fac Engn Dept Elect & Elect Engn Upm Serdang 43400 Selangor Darul Malaysia

Trajectory planning of robotic manipulators in complex environments involves generating smooth and collision-free paths, and key aspects to consider include dynamic environment perception, path planning, trajectory smoothing and optimization, and obstacle avoidance. This paper discusses different algorithms and finally proposes a fusion of the improved Dung Beetle Optimizer (Idbo) algorithm using chaotic mapping, sinusoidal random mutation, and nonlinear convergence strategies with the bidirectional Rapidly exploring Random Tree (RRT) algorithm to achieve manipulator trajectory planning and minimum jerk optimization trajectory. In the experiment, the Idbo algorithm was compared with other heuristic algorithms. The path obtained was shortened by 9.18% on average, and the calculation time was shortened by 33.81% on average. Then, the paper explored Cartesian coordinate space trajectory planning and joint space trajectory planning when the robot was working in 3D space, and verified the superiority of the latter. In controlling the movement of the robot, by combining the minimum angle jerk planning and the bidirectional RRT obstacle avoidance algorithm to drive the joint angle parameters obtained by the spatial trajectory planning, the trajectory of the robot manipulator can be smoother, more efficient, and avoid obstacles in complex 3D space. This research helps improve the performance, safety, and technical support of robots, and is of great research significance.

关键词： Robot manipulator trajectory planning dbo algorithm minimum jerk RRT algorithm

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Couple-stress asymmetric wave equations modelling with an optimal finite-difference scheme

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ACTA GEOPHYSICA 2024年第6期72卷 4005-4026页

作者： Wei, Xuruo Bai, Wenlei Feng, Haixin Zhou, Zhichun Wang, Zhiyang Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China Chinese Acad Sci Inst Geol & Geophys Key Lab Petr Resources Res Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China China Elect Technol Grp Corp Res Inst 3 Beijing Peoples R China Beihang Univ Sch Energy & Power Engn Beijing Peoples R China

The asymmetric wave equation encompasses the influence of the actual fine structure inside the medium on the wave field, which can better represent the complex seismic wavefield excited by the complex source and reflect the scale effects of the seismic wave response under equal computational power. However, when the finite-difference (FD) operator is applied to implement the numerical modelling using the asymmetric wave equation, numerical dispersion appears due to the use of difference operator to approximate the differential operator, which negatively affects the analysis of the seismic wavefield. To suppress the numerical dispersion, this paper proposes an improved Dung Beetle optimization (Idbo) algorithm to obtain the optimized FD operators. The Idbo algorithm adopts an improved Tent map and the opposition-based learning strategy to initialize the population, which improves the diversity of the population. The nonlinear function adaptive control strategy is introduced to adjust the population allocation ratio and boundary selection parameter R to achieve an adequate balance between global exploration and local exploitation. In addition, adaptive weights and the Levy flight mechanism are combined to improve the ball-rolling dung beetle position updating strategy to avoid falling into local extremes. Numerical dispersion analysis and numerical modelling results demonstrate that the optimization of FD operators based on the Idbo algorithm can effectively suppress numerical dispersion. It is of great significance to extract the wave field perturbation caused by heterogeneity due to the complex microstructure in the medium and analyse the influence of the microstructural properties in the medium on seismic wave propagation.

关键词： Asymmetric wave equation Numerical modelling dbo algorithm Optimization Finite difference

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Joint Optimization Scheme for User Association and Resource Allocation in Internet of Vehicles 98

Joint Optimization Scheme for User Association and Resource ...

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98th IEEE Vehicular Technology Conference (VTC-Fall)

作者： Yang, Junyi Fu, Yuchuan Li, Changle Yuan, Xiaoming Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Xidian Univ Res Inst Smart Transportat Xian 710071 Shaanxi Peoples R China Northeastern Univ Qinhuangdao Branch Campus Qinhuangdao 066004 Hebei Peoples R China

ISBN: (纸本)9798350329285

Integrated sensing, communication, and computation (ISCC) has become one of the research hotspots focused on the sixth generation (6G) communication systems. However, due to the demand for the coexistence of sensing, communication, and computing functions, there is uneven scheduling of resources among all parties, resulting in difficulties in accurately perceiving the environment, processing massive data efficiently in real-time, and experiencing large latency in task processing in the Internet of Vehicles (IoV). In this work, we propose a novel wireless scheduling architecture to enhance the coordination gains of sensing, communication, and computation from a perspective of joint optimization, enabling true on-demand services. Specifically, we first adopt the modified Cobb-Douglas utility function, the communication rate, perception of mutual information (MI), and calculation delay as coordinated gains. Next, we formulate a joint user association and subchannel assignment problem to capture the network externalities induced by resource competition among vehicle user devices (VUEs) with multi-functional requirements. To achieve a mutually satisfactory solution, we propose the dung beetle optimizer (dbo) algorithm to maximize the average utility of all VUEs in the network. Simulation results show that, compared with the baseline algorithm, the average utility gain of the proposed algorithm can reach up to 7.21%.

关键词： Integrated sensing and communication mobile edge computing user association subchannel allocation dbo algorithm 6G

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Improved BP neural network based on Dung Beetle Optimization algorithm for battery SOH prediction 5

Improved BP neural network based on Dung Beetle Optimization...

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Zhao, Faxing An, Ziliang Sun, Yannan Li, Xinxin Shanghai Institute of Technology School of Railway Transportation Shanghai China

ISBN: (纸本)9798350309034

Lithium battery state of health (SOH) prediction is one of the key tasks in the current battery field. Traditional model-based prediction approaches are difficult to model and have insufficient estimation accuracy. The prediction methods based on error back-propagation (BP) neural networks are easily affected by the initial weights and thresholds and fall into the local optimization problem, which affects the accuracy of the prediction results. Using the dung beetle optimization algorithm to optimize the initial weights and thresholds of BP neural networks can effectively increase the accuracy of prediction. In order to verify the feasibility of the algorithm, the data of battery B0005 in the public battery dataset of NASA of the National Aeronautics and Space Administration of the United States was used for experiments, and the root mean square error and mean absolute percentage error of SOH predicted by the method can be obtained as 0.0074292 and 0.84369%, respectively, and the experimental results show that the optimized BP neural network of the Dung Beetle Optimization algorithm has a higher prediction accuracy. © 2023 IEEE.

关键词： battery SOH BP neural network dbo algorithm machine learning

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A Hybrid Dung Beetle Optimization algorithm with Simulated Annealing for the Numerical Modeling of Asymmetric Wave Equations

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Applied Geophysics 2024年第3期21卷 513-527,618页

作者： Wei Xu-ruo Bai Wen-lei Liu Lu Li You-ming Wang Zhi-yang College of Information Science and Technology Beijing University of Chemical TechnologyBeijingPRC Key Laboratory of Petroleum Resources Research Institute of Geology and GeophysicsChinese Academy of SciencesBeijingPRC University of Chinese Academy of Sciences BeijingChina Aramco Research Center-Beijing Aramco AsiaBeijingChina

In the generalized continuum mechanics(GCM)theory framework,asymmetric wave equations encompass the characteristic scale parameters of the medium,accounting for microstructure *** study integrates two theoretical branches of the GCM,the modified couple stress theory(M-CST)and the one-parameter second-strain-gradient theory,to form a novel asymmetric wave equation in a unified *** modeling of the asymmetric wave equation in a unified framework accurately describes subsurface structures with vital implications for subsequent seismic wave inversion and imaging ***,employing finite-difference(FD)methods for numerical modeling may introduce numerical dispersion,adversely affecting the accuracy of numerical *** design of an optimal FD operator is crucial for enhancing the accuracy of numerical modeling and emphasizing the scale ***,this study devises a hybrid scheme called the dung beetle optimization(dbo)algorithm with a simulated annealing(SA)algorithm,denoted as the SA-based hybrid dbo(Sdbo)*** FD operator optimization method under the Sdbo algorithm was developed and applied to the numerical modeling of asymmetric wave equations in a unified *** the dbo and SA algorithms mitigates the risk of convergence to a local *** numerical dispersion outcomes underscore that the proposed Sdbo algorithm yields FD operators with precision errors constrained to 0.5‱while encompassing a broader spectrum *** result confirms the efficacy of the Sdbo ***,the numerical modeling results demonstrate that the new FD method based on the Sdbo algorithm effectively suppresses numerical dispersion and enhances the accuracy of elastic wave numerical modeling,thereby accentuating scale *** result is significant for extracting wavefield perturbations induced by complex microstructures in the medium and the analysis of scale effects.

关键词： Finite-difference Asymmetric wave equation Numerical modeling dbo algorithm SA algorithm

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Research on stamping formability and process simulation of AZ31 magnesium alloy based on deep learning

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MATERIALS TODAY COMMUNICATIONS 2024年 40卷

作者： Wang, Shuo Wu, Yan Xu, Qian Ma, Yanman Wang, Tianzhu Wuhan Polytech Univ Coll Mech Engn Wuhan 430048 Peoples R China

To study the formability of AZ31 magnesium alloy under different temperature conditions and improve the forming quality of its parts, this paper aims to solve the minimization problem of pursuing the "maximum thinning rate". This study obtained the mechanical properties of AZ31 magnesium alloy at 25 degrees C, 150 degrees C, 250 degrees C, and 350 degrees C through uniaxial tensile tests and explored the stamping formability of AZ31 magnesium alloy mobile phone cases using the finite element inverse calculation method (MSTEP). Based on the hot stamping of AZ31 magnesium alloy at 250 degrees C, the Monte Carlo Simulation (MCS) method was used to randomly sample the blank holder force, stamping speed, friction coefficient, and resistance coefficient, and then simulate to obtain the corresponding "maximum thinning rate". In addition, a hybrid prediction model optimized by Genetic algorithm (GA) and Dung Beetle Optimization (dbo) based on the Back Propagation Neural Network (BPNN) - Random Forest (RF) was used to explore the nonlinear relationship between blank holder force, stamping speed, friction coefficient, resistance coefficient, and the "maximum thinning rate". The results show that the plasticity of AZ31 magnesium alloy enhances with the temperature increase, especially exhibiting the best stamping formability at 250 degrees C. Feature importance indicates that the resistance coefficient and stamping speed have a greater impact on the maximum thinning rate, while the influence of blank holder force and friction coefficient is relatively smaller. Mean Square Error (MSE) shows that the predictive capability of the hybrid model is significantly superior to that of the single BP neural network and Random Forest model, displaying optimal efficiency. The predicted maximum thinning rate by the model is 15.39 %, with an error rate of only 0.32 % compared to the DYNAFORM finite element software simulation results, confirming the correctness, accuracy, and effectiveness o

关键词： AZ31 magnesium alloy Stamping forming GA dbo algorithm BP neural network RF regression model

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Parameter identification method of load modeling based on improved dung beetle optimizer algorithm

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FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Xing, Chao Xi, Xinze He, Xin Deng, Can Yunnan Power Grid Co Ltd Elect Power Res Inst Kunming Peoples R China

The role of load modeling in power systems is crucial for both operational and regulatory considerations. It is essential to develop an effective and reliable method for optimizing load modeling parameter identification. In this paper, the dung beetle algorithm is improved by using the good point set, and a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (Gdbo) within the framework of the measurement-based load modeling method. The proposed parameter identification strategy involves utilizing PMU voltage data as input, selecting a comprehensive load model, and refining the initialization process based on the good point set to mitigate the influence of local maxima. Through iterative optimization of the objective function using the Dung Beetle Optimizer (dbo) algorithm, the optimal parameters for the comprehensive load model are determined, enhancing the model's ability to accurately capture the power curve. Analysis of examples pertaining to PMU-measured modeling parameter identification reveals that the proposed Gdbo algorithm, which incorporates a good point set, outperforms alternative methods such as the improved differential evolution algorithm (IDE), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO), and conventional dbo algorithm. This demonstrates the superior performance of the introduced approach in the context of load model parameter identification.

关键词： dbo algorithm good point set parameter identification load modeling electric power system

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