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UAV path planning based on improved dung beetle algorithm with multiple strategy integration

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2025年第3期239卷 223-240页

作者： Chang, Baoshuai Xi, Wanqiang Lin, Junzhi Shao, Ziyan Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210000 Peoples R China Wuxi Univ Sch Automat 333 Xishan Dist Ave Wuxi 214000 Peoples R China

This research presents a multi-strategy augmented dung beetle algorithm (CDBO) for UAV path planning in intricate 3D environments. Its main objective is to address the path planning challenge by formulating an integrated cost function model and environment representation that aligns the optimisation task with the navigation prerequisites and safety constraints of the UAV. Firstly, the algorithm initialises the particle population based on the Levy flight principle to improve the species diversity and adequately search the solution space. Subsequently, an exponentially decreasing inertia weighting strategy is introduced to improve the convergence speed of the algorithm. In addition, the algorithm uses an adaptive t -distribution with perturbed variation of the updated positions so that the algorithm avoids falling into a local optimum. Finally, to enhance the robustness of the system, the algorithm incorporates an elite retention strategy based on the Pareto principle. In this study, rigorous tests using 12 CEC2022 test functions and Wilcoxon rank sum test are conducted as benchmarks for the performance of the algorithm. The results show that the enhanced dung beetle algorithm outperforms the traditional algorithm, highlighting its efficiency and robustness. In addition, the efficacy of the enhanced dung beetle algorithm was validated in a variety of complex 3D environments, affirming its ability to generate optimal paths. This study further highlights the importance of effective algorithms in UAV path planning, addressing the key issue of optimising flight operations in intricate spatial scenarios.

关键词： dung beetle algorithm path planning adaptive t-distribution L & eacute vy flight exponentially decreasing inertia weighting strategy

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Predicting Dye Formulations and Ultrasonic Pretreatment Power in Wood Staining with a SAT Fusion-Gated BiLSTM Model Optimized by Improved dung beetle algorithm

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1522-1522页

作者： Guan, Xuemei Chen, Xiangya He, Zhongsheng Cui, Hongbo Northeast Forestry Univ Sch Comp & Control Engn Harbin 150040 Peoples R China

Wood dyeing plays a crucial role in enhancing the value of plantation wood and addressing the imbalance between supply and demand in the wood industry. However, challenges such as low dye uptake and inaccurate color matching persist. This study introduces a novel Separable Attention Feature Fusion-Gated Bidirectional Long Short-Term Memory (SAT Fusion-Gated BiLSTM) model to more accurately predict dye concentration and ultrasonic pretreatment power to address these issues. The model integrates multispectral data and uses ultrasonic treatment to open fiber gaps and decompose wood components, thereby improving dye uptake. Unlike traditional models, SAT Fusion-Gated BiLSTM combines a Maximum Information Coefficient (MIC)-based feature selection module, gated linear units for enhanced feature extraction, and an improved cross-attention mechanism for efficient data fusion. Compared to traditional deep learning models, the model's performance is significantly improved, including an R2 of 0.9274, an RMSE of 0.0470, an RPIQ of 5.5825, an RPD of 5.0400, and a MAE of 0.0229. RPIQ and RPD improve by over 100.8% compared to traditional deep learning models, and the runtime is reduced by 83.94% when compared to Convolutional Neural Networks (CNNS). The Shapley Additive Explanation (SHAP) reveals the key spectral features that drive these predictions. These advancements in accuracy, efficiency, and interpretability make the model particularly promising for industrial applications, offering both improved operational efficiency and more precise resource management in the sustainable use of timber.

关键词： veneer dyeing cross-attention BiLSTM gated mechanism dung beetle algorithm

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Improved neural network model based on dung beetle algorithm to predict CO2-brine interfacial tension

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 239卷

作者： Li, Jia-Qi Bian, Xiao-Qiang Chen, Jing Liu, Yong -Bing Matthews, Aria Southwest Petr Univ Petr Engn Sch Chengdu 610500 Peoples R China Southwest Petr Univ Sch New Energy & Mat Chengdu 610500 Peoples R China

Geological carbon sequestration refers to the permanent storage of captured CO2 through injection into subterranean saline or rock formations. The CO2-brine interfacial tension (IFT) is a crucial factor that significantly impacts the process's efficacy. Since the experimental determination of the IFT of brine and CO2 is both timeconsuming and expensive, and a variety of sources of error may occur, developing a well -prepared and dependable model of CO2-brine IFT is crucial. In this paper, an attempt has been made to investigate the dung beetle optimization algorithm based back propagation neural network (DBO-BPNN) model for predicting CO2- brine IFT. The model contains 2616 experimental data of CO2-brine/water interfacial tension collected from various literatures, which can be divided into three regimes to be investigated: pure CO2-brine, pure CO2-water and impure CO2-water, and takes into account six independent variables: pressure, temperature, monovalent cation molality (Na+ and K+), bivalent cation molality (Ca2+ and Mg2+) in brine and the molar fractions of N2 and CH4 in the injected CO2 stream. The model's efficacy is assessed using a range of statistical and graphical techniques, and the model's validity is validated through the implementation of leverage methods, which identify anomalies across the entire dataset. Finally, the model is further compared with other intelligent models such as particle swarm optimization (PSO)-BPNN and grey wolf optimizer (GWO)-BPNN in terms of runtime, storage space and accuracy. According to the results, the DBO-BPNN model provides the best levels of accuracy and precision, with determination coefficient (R2), root mean square error (RMSE) and average absolute relative deviation (AARD%) of 0.9743, 1.598 and 3.16, respectively, and the R2 is enhanced by 0.8% and 2.2% in comparison to GWO-BPNN and PSO-BPNN models. Additionally, the DBO-BPNN model exhibits the least execution time, a reduction of 6.4% and 13.1% in comparison to

关键词： dung beetle algorithm Back propagation neural network Interfacial tension CO2-Brine system

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Three-dimensional path planning of UAV based on improved dung beetle algorithm 5

Three-dimensional path planning of UAV based on improved dun...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Liu, Congjian Bian, Jinxin Zheng, Xiaojun Li, Siyang Dalian Jiaotong Univ Sch Mech Engn Dalian Peoples R China

ISBN: (纸本)9798350386783;9798350386776

The environmental characteristics of mountainous ravines and complex terrain lead to the problems of slow speed and poor quality of UAV path planning. An improved dung beetle algorithm is proposed. In the traditional dung beetle algorithm, the Logistic chaotic map is called, the global survey strategy of the Osprey algorithm is fused, and the adaptive t-distribution perturbation strategy is introduced. The three-dimensional path planning space is established by the rasterization method. The proposed improved algorithm is compared on six classical benchmark functions, and the superiority of the improved DBO algorithm is fully verified. It is applied to the simulation experiment of mountain path planning, and the effectiveness of the improved dung beetle algorithm is verified.

关键词： dung beetle algorithm Chaotic map Osprey algorithm Adaptive t distribution Path planning

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Wsn coverage optimization based on convergence factor dung beetle algorithm 5

Wsn coverage optimization based on convergence factor dung b...

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

作者： Ma, Tong Ma, Hongwei Shanghai Dianji University Shanghai China

ISBN: (纸本)9798350309034

Current sensor distribution methods suffer from sluggish convergence and tend to drop into local extremes, the coverage of wireless sensor networks (WSN) is intimately connected to the distribution of sensor nodes in the area. A sensor node distribution optimization technique based on the convergence factor dung beetle algorithm (IRDBO) is suggested in this respect. First, Tent chaotic mapping is used to reset the population's parameters to produce a more consistent distribution, Second, the convergence factor and dynamic weighting strategy are used to modify the search step length to address the issue that the algorithm is prone to entering the local optimum. Compared to the traditional DBO technique, simulation studies demonstrate that the revised approach has the benefits of quick convergence and better coverage rate, and can successfully address the WSN coverage optimization challenge. © 2023 IEEE.

关键词： Convergence Factor dung beetle algorithm Node Distribution Tent Chaos Mapping WSN Coverage Optimization

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Optimization of Tungsten Heavy Alloy Cutting Parameters Based on RSM and Reinforcement dung beetle algorithm

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SENSORS 2023年第12期23卷 5616-5616页

作者： Zhu, Xu Ni, Chao Chen, Guilin Guo, Jiang Dalian Univ Technol State Key Lab High Performance Precis Mfg Dalian 116024 Peoples R China Dalian Univ Technol Ningbo Inst Ningbo 315000 Peoples R China

Tungsten heavy alloys (WHAs) are an extremely hard-to-machine material extensively used in demanding applications such as missile liners, aerospace, and optical molds. However, the machining of WHAs remains a challenging task as a result of their high density and elastic stiffness which lead to the deterioration of the machined surface roughness. This paper proposes a brand-new multi-objective dung beetle algorithm. It does not take the cutting parameters (i.e., cutting speed, feed rate, and depth of cut) as the optimization objects but directly optimizes cutting forces and vibration signals monitored using a multi-sensor (i.e., dynamometer and accelerometer). The cutting parameters in the WHA turning process are analyzed through the use of the response surface method (RSM) and the improved dung beetle optimization algorithm. Experimental verification shows that the algorithm has better convergence speed and optimization ability compared with similar algorithms. The optimized forces and vibration are reduced by 9.7% and 46.47%, respectively, and the surface roughness R-a of the machined surface is reduced by 18.2%. The proposed modeling and optimization algorithms are anticipated to be powerful to provide the basis for the parameter optimization in the cutting of WHAs.

关键词： tungsten heavy alloys multi-object optimization dung beetle algorithm response surface method multi-sensor

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A study of scheduling strategies for microgrids based on the non-dominated sorting dung beetle optimization algorithm

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Scientific Reports 2025年第1期15卷 1-16页

作者： Chen, Yutong Ning, Wu Du, Wei Department of Electronic and Information Engineering Liaoning University of Technology Jinzhou 121001 China

Microgrids possess robust self—operation and management capabilities, enabling effective utilization of renewable energy sources and enhancing the reliability and security of power supply within power systems. However, challenges such as low economic benefits and inefficient operation have long plagued microgrids. To address these issues, this paper presents a microgrid scheduling strategy based on the Non—Dominated Sorting dung beetle Optimization algorithm (NSDBO). By incorporating the non—dominated sorting mechanism into the dung beetle optimization algorithm, the solution set is divided into different tiers. Solutions within each tier demonstrate superior performance compared to those in other tiers. Moreover, when tackling multi—objective optimization problems, this approach effectively avoids falling into local optima and significantly enhances the algorithm’s global search capability. Experimental results reveal that, in comparison to other algorithms, the proposed NSDBO algorithm outperforms them in terms of hypervolume (HV) and generational distance (GD), indicating its advantages in both convergence and diversity. Specifically, compared with the Grey Wolf Optimizer (GWO) and the standard dung beetle Optimization (DBO) algorithm, the overall operation cost of NSDBO is reduced by 52% and 8.1%, respectively. These findings fully demonstrate the effectiveness of the proposed algorithm in improving users’ economic benefits and reducing environmental pollution. © The Author(s) 2025.

关键词： dung beetle algorithm Microgrid scheduling strategy Non-dominated sorting Overall operating cost

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Parameter identification of PMSM based on dung beetle optimization algorithm

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ARCHIVES OF ELECTRICAL ENGINEERING 2023年第4期72卷 1055-1072页

作者： Yang, Xiaoliang Cui, Yuyue Jia, Lianhua Sun, Zhihong Zhang, Peng Zhao, Jiane Wang, Rui Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou Peoples R China Henan Key Lab Informat Based Elect Appliances Zhengzhou Peoples R China China Railway Engn Equipment Grp Co Ltd Zhengzhou Peoples R China Zhengzhou Univ Sci & Technol Sch Elect & Elect Engn Zhengzhou Peoples R China

In this paper, a creative dung beetle optimization (CDBO) algorithm is proposed and applied to the offline parameter identification of permanent magnet synchronous motors. First, in order to uniformly initialize the population state and increase the population diversity, a strategy to improve the initialization of the dung beetle population using Singer chaotic mapping is proposed to improve the global search performance;second, in order to improve the local search performance and enhance the convergence accuracy of the algorithm, a new dung beetle position update strategy is designed to increase the spatial search range of the algorithm. Simulation results show that the proposed optimization algorithm can quickly and accurately identify parameters such as resistance, inductance, and magnetic chain of the PMSM, with significant improvements in convergence algebra, identification accuracy and stability.

关键词： chaotic mapping dung beetle algorithm Levy flight parameter identification permanent magnet synchronous motor spiral strategy

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Optimal scheduling model of microgrid based on improved dung beetle optimization algorithm

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2024年第1期12卷

作者： Gao, Yu Zhang, Yong Xiong, Zaibao Zhang, Penglin Zhang, Qin Jiang, Wenxu State Nucl Elect Power Planning Design & Res Inst Beijing Peoples R China Inner Mongolia Huo Coal Hongjun Aluminum Power Co Huo Coal Hongjun Aluminum Power Co Tongliao Peoples R China

In view of the strong uncertainty and intermittency of distributed power sources in microgrids and the shortcomings of the traditional dung beetle optimizer (DBO) algorithm with slow convergence, poor robustness and ease of falling into a local optimum, an optimal scheduling model for microgrids based on the improved dung beetle optimization algorithm is proposed. First, a multiobjective optimal scheduling model of the microgrid is constructed and a typical daily output scenario generation method for wind power generation and photovoltaic power generation is constructed based on the Gaussian kernel density estimation, Frank-copula and K-means clustering algorithms. Second, to address the shortcomings of the DBO algorithm, the spiral position update strategy, adaptive weight factor, levy flight strategy and t-distribution variation strategy are introduced on the basis of the DBO algorithm, which effectively solves the problem of premature convergence of particles owing to falling into a local optimum. Finally, five benchmark test functions were selected for simulation experiments. Finally, the simulation results show that the computational performance of the IDBO algorithm is significantly better than the other five intelligent algorithms. The algorithm proposed in this paper also achieves more satisfactory results in microgrid optimal scheduling based on the scenario generation method.

关键词： Microgrid intermittent generation scenario generation dung beetle algorithm optimal scheduling

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Rapid Determination of Hemicellulose Content in Corn Stalks by Near-infrared Spectroscopy Based on dung beetle Optimizer

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Agricultural Biotechnology 2024年第5期13卷 83-85,92页

作者： Baihong TONG Jinming LIU Jianfei SHI College of Information and Electrical Engineering Heilongjiang Bayi Agricultural UniversityDaqing 163000China

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 *** is an important component in corn stalks,and it is very important to determine its content in corn *** this paper,the feasibility of near-infrared spectroscopy(NIRS)combined with chemometrics for rapid detection of hemicellulose content in corn stalks was *** order to improve the accuracy of NIRS detection,a new intelligent optimization algorithm,dung beetle optimizer(DBO),was applied to select characteristic wavelengths of *** 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.

关键词： Hemicellulose Near-infrared spectrum Characteristic wavelength selection Intelligent optimization algorithm dung beetle algorithm

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