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Optimization Design of Flexible Net Capture System for Low, Slow, and Small Unmanned Aerial Vehicles Based on Improved Multi-Objective wolf pack algorithm

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DRONES 2025年第3期9卷 190-190页

作者： Xu, Ran Peng, Qiang Wu, Husheng Engn Univ PAP Coll Equipment Engn Xian 710018 Peoples R China

In response to the increasing safety concerns posed by low, slow, and small unmanned aerial vehicles (UAVs), the use of flexible nets for interception emerges as a promising solution due to its high tolerance, minimal requirements, and cost-effectiveness. To enhance the effectiveness of the flexible net capture system for these types of UAVs, an optimization of the system's parameters is conducted. A dynamic model of the flexible net capture system is developed, and its deployment process is simulated and analyzed through a combination of ABAQUS 2022/Explicit and MATLAB R2020b software. The coverage rate and hang time are proposed as the key performance indicators for quantitatively assessing the interception capabilities of the rope net. A mathematical model is formulated to optimize the capture system parameters, considering both spatial and temporal tolerances. The Multi-objective wolf pack algorithm, which incorporates an Elite Leadership Strategy and a crowding distance-based population update mechanism, is utilized to optimize the design variables. This approach leads to the derivation of the optimized design parameters for the flexible net. Ultimately, the optimal parameter configuration for the flexible net capture system is achieved through the application of the Multi-objective wolf pack algorithm to the design variables. This optimization ensures the system's peak performance in intercepting low, slow, and small UAVs.

关键词： anti-drone flexible nets wolf pack algorithm multi-objective optimization simulation analysis

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A Multi-Strategy Enhanced wolf pack algorithm for Three-Dimensional Path Planning of Unmanned Aerial Vehicles

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第9-11期37卷

作者： Peng, Qiang Zhan, Renjun Wu, Husheng Wang, Aiai Lai, Yuanda Engn Univ PAP Coll Equipment Support & Management Xian Peoples R China Engn Univ PAP Fdn Dept Xian Peoples R China

In this paper, a Multi-strategy Enhanced wolf pack algorithm (MSEWPA) is proposed to address the three-dimensional (3D) path planning problem for unmanned aerial vehicles (UAVs) in complex environments. Initially, a mathematical model for 3D path planning is constructed, comprehensively considering constraints such as UAV operational efficiency, path safety risks, performance limitations, obstacle avoidance requirements, and noise limits in urban functional areas. Subsequently, the design of the MSEWPA algorithm is elaborated in detail, including the utilization of the Good Lattice Point (GLP) theory to optimize population initialization for enhanced global search capability, the integration of selection, crossover, and mutation operations from the Differential Evolution (DE) algorithm to augment the randomness of wandering, the introduction of a behavior transition factor for adaptive behavior adjustment, the incorporation of light propagation phenomena to improve random search capabilities during the running process, and the design of multiple siege strategies to guide the exploration of globally optimal solutions. To validate the robustness of the algorithm, sensitivity analysis is conducted on key parameters to determine their optimal settings, and ablation experiments are performed to verify the effectiveness of each improvement strategy. Experimental results on the CEC-2017 benchmark test functions demonstrate that MSEWPA excels in solving complex optimization problems, achieving rapid convergence to high-quality global optimal solutions. Furthermore, in four path planning problems of varying complexity, MSEWPA outperforms 11 other state-of-the-art metaheuristic optimization algorithms, demonstrating a strong balance between global and local exploration capabilities. This provides an effective solution for UAV 3D path planning.

关键词： three-dimensional path planning unmanned aerial vehicle wolf pack algorithm

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Design of high uniform magnetic field with four coils based on improved wolf pack algorithm

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2025年 61卷

作者： Zhu, Xuehua Ye, Juntao Ren, Ziruo Liu, Xinyu Anhui Polytech Univ Sch Integrated Circuits Wuhu 241000 Peoples R China Anhui Polytech Univ Anhui Engn Res Ctr Vehicle Display Integrated Syst Wuhu 241000 Peoples R China Anhui Polytech Univ Key Lab Adv Percept & Intelligent Control High End Minist Educ Wuhu 241000 Peoples R China

Uniform magnetic fields are commonly utilized in scientific and engineering domains for a variety of purposes, such as atomic magnetometers, nuclear magnetic resonance, and other magnetic tools. However, the conventional Helmholtz coils have limitations in generating the highly uniform magnetic fields required for larger devices. To tackle this challenge, a new four-coil Helmholtz configuration has been devised in this paper to produce extremely uniform magnetic fields. Through the utilization of an enhanced wolf pack algorithm (WPA) for optimizing spatial parameters, the four-coil system notably enhances the effective coverage ratio (ECR) of the uniform magnetic field. Finite element simulations confirm that this configuration delivers superior magnetic field uniformity, the ratio of the uniform magnetic field space, known as the ECR, experienced an increase from 18.5495% to 34.3046% when the magnetic field change rate remained below 0.1%. The research underscores the enhanced adaptability and effectiveness of the improved WPA in addressing multi-dimensional optimization challenges, providing a swift and efficient method for attaining uniform magnetic fields. This progress supports applications reliant on uniform magnetic fields, such as geomagnetic navigation, sensor calibration, and magnetic guidance systems, opening up possibilities for future applications of intelligent optimization algorithms in intricate physical and engineering tasks.

关键词： Uniform magnetic field McLaughlin expansion method wolf pack algorithm Effective coverage rate Finite elements simulation

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Fast Area Optimization Approach for XNOR/OR-based Fixed Polarity Reed-Muller Logic Circuits based on Multi-strategy wolf pack algorithm

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ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS 2023年第3期28卷 1-16页

作者： Zhou, Yuhao He, Zhenxue Jiang, Jianhui Liu, Jia Wang, Tao Xiao, Limin Wang, Xiang Tongji Univ Sch Software Engn 4800 CaoAn Rd Shanghai 201804 Peoples R China Hebei Agr Univ 289 LingYuSi St Baoding 071001 Peoples R China Northeastern Univ Qinhuangdao 289 LingYuSi St Baoding 071001 Peoples R China Beijing Informat Sci & Technol Univ Beijing 100192 Peoples R China Beihang Univ 37 XueYuan Rd Beijing 100191 Peoples R China

Area optimization is one of the most important contents of circuits logic synthesis. The smaller area has stronger testability and lower cost. However, searching for a circuit with the smallest area in a large-scale space of polarity is a combinatorial optimization problem. The existing optimization approaches are inefficient and do not consider the time cost. In this paper, we propose a multi-strategy wolf pack algorithm (MWPA) to solve high-dimension combinatorial optimization problems. MWPA performs global search based on the proposed global exploration strategy, extends the search area based on the Levy flight strategy, and performs local search based on the proposed deep exploitation strategy. In addition, we propose a fast area optimization approach (FAOA) for fixed polarity Reed-Muller (FPRM) logic circuits based on MWPA, which searches the best polarity corresponding to a FPRM circuit. The experimental results confirm that FAOA is highly effective and can be used as a promising EDA tool.

关键词： Area optimization FPRM logic circuits Levy flight wolf pack algorithm combinatorial optimization problem

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A distance determination wolf pack algorithm for solving high-dimensional complex functions and its application

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JOURNAL OF SUPERCOMPUTING 2025年第8期81卷 1-52页

作者： Lai, Yuanda Wu, Husheng Peng, Qiang Hong, Chen Cheng, Shi Engn Univ PAP Coll Equipment Support & Management A1 Wujing Rd Xian 710086 Peoples R China Engn Univ PAP Fdn Dept A1 Wujing Rd Xian 710086 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

The wolf pack algorithm (WPA), a swarm intelligence method inspired by wolf hunting behaviors, faces limitations in convergence accuracy, computational efficiency, and local optima avoidance. This paper proposes a Distance Determination wolf pack algorithm (DDWPA) to address these challenges. Key innovations include: (1) An initial wandering position recording mechanism to prevent premature local convergence;(2) a distance judgment factor integrated into the summoning phase for accelerated population evolution;and (3) a head wolf relative positioning strategy and distance-updated regeneration operator to enhance global search capability. The algorithm's convergence is theoretically verified through Markov process analysis. Extensive evaluations on CEC2022 and CEC2020 benchmark functions demonstrate DDWPA's superiority over existing swarm intelligence algorithms in terms of solution precision and convergence speed. Four engineering case studies further validate its effectiveness in handling high-dimensional real-world optimization problems. Results consistently show that DDWPA achieves superior global convergence and computational efficiency while maintaining robustness against local optima traps.

关键词： wolf pack algorithm Wander threshold Population-based optimization Distance determination factor High-dimensional function optimization

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Combining the wolf pack algorithm with the Curve-adaptive Grasshopper Optimization algorithm 5

Combining the Wolf Pack Algorithm with the Curve-adaptive Gr...

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

作者： Yao, Lejia Hu, Haidong Jiang, Feng Fan, Pu Inner Mongolia Univ Sci & Technol Sch Digital Intelligence Ind Baotou Inner Mongolia Peoples R China

ISBN: (纸本)9798350386783;9798350386776

To address the issues of the Grasshopper Optimization algorithm (GOA) falling into local optima and achieving low optimization precision, this paper proposes a hybrid algorithm called Combining the wolf pack algorithm (WPA) with the Curve-adaptive Grasshopper Optimization algorithm (WCGOA). Firstly, the population is initialized using Logistic mapping to ensure an optimal initial population, thereby enhancing population diversity. Secondly, the linear weights are replaced with curve-adaptive weights to improve search speed and precision. Thirdly, by incorporating the hierarchical hunting concept from the wolf pack algorithm, individual awareness of grasshoppers is developed to enhance global search capabilities. Subsequently, Cauchy mutation is applied to the best individuals to strengthen their ability to escape local optima. Finally, GOA is compared with four other classical algorithms as benchmarks for WCGOA. Statistical analysis and Wilcoxon rank-sum tests conducted on 11 commonly used benchmark functions demonstrate that WCGOA significantly outperforms other algorithms in terms of convergence precision, convergence speed, stability, and optimization success rate.

关键词： Grasshopper optimization algorithm wolf pack algorithm curve adaptation Cauchy mutation

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Improved wolf pack algorithm for UAV Path Planning Problem

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷 1-22页

作者： Chen, Yueyi Wu, Husheng Xiao, Renbin Huazhong Univ Sci & Technol Wuhan Peoples R China Armed Police Force Engn Univ Xian Peoples R China

In order to obtain the global optimal path of UAV flight path planning in low altitude penetration mode, this paper studies a method of UAV 3D flight path planning. Aiming at the shortcomings of original wolf pack algorithm, such as poor local search ability, invariable step size, and slow convergence rate, this paper proposes an improved wolf pack algorithm based on the fluorescein guide mechanism in the artificial glowworm swarm optimization algorithm, which also improves the searching mode in wandering behavior, updates the individual position in wolf pack with the teaching and learning optimization algorithms with feedback mechanism. In addition, dynamic mutation strategy was introduced to jump out of the local optimal solution. The results show that the improved algorithm is more effective in solving the problem of UAV 3D trajectory planning.

关键词： Dynamic Mutation Fluorescein Guidance Path Planning Teaching-Learning-Based Optimization With Feedback UAV wolf pack algorithm

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An Elite wolf pack algorithm Based on the Probability Threshold for a Multi-UAV Cooperative Reconnaissance Mission

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DRONES 2024年第9期8卷 513-513页

作者： Zhang, Hanrui Lv, Xiao Ma, Chao Cui, Liangzhong Naval Univ Engn Coll Power Engn Wuhan 430030 Peoples R China 717 Jiefang Ave Wuhan 430030 Peoples R China

In the task assignment problem of multi-UAV collaborative reconnaissance, existing algorithms have issues with inadequate solution accuracy, specifically manifested as large spatial spans and knots of routes in the task execution of UAVs. To address the above challenges, this paper presents a multi-UAV task assignment model under complex conditions (MTAMCC). To efficiently solve this model, this paper proposes an elite wolf pack algorithm based on probability threshold (EWPA-PT). The EWPA-PT algorithm combines the wandering behavior in the traditional wolf pack algorithm with the genetic algorithm. It introduces an ordered permutation problem to calculate the adaptive wandering times of the detective wolves in a specific direction. During the calling phase of the algorithm, the fierce wolves in the wolf pack randomly learn the task assignment results of the head wolf. The sieging behavior introduces the Metropolis criterion from the simulated annealing algorithm to replace the distance threshold in traditional wolf pack algorithms with a probability threshold, which dynamically changes during the iteration process. The wolf pack updating mechanism leverages the task assignment experience of the elite group to reconstruct individual wolves, thereby improving the individual reconstruction's efficiency. Experiments demonstrate that the EWPA-PT algorithm significantly improves solution accuracy compared to typical methods in recent years.

关键词： multi-UAVs task assignment wolf pack algorithm probability threshold elite group adaptive wandering times

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Application of improved wolf pack algorithm in planning and operation of multi-microgrid systems with electric vehicles

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第9期35卷 096209-096209页

作者： Sun, Guohao Zhang, Shouming Yang, Sen Zhao, Yuhao Kunming Univ Sci & Technol Fac Informat Engn & Automation Kunming Yunnan Peoples R China

With the rapid growth of renewable energy sources and the widespread use of electric vehicles (EVs), the planning and operation problems of multiple microgrids (MMGs) have become more complex and diverse. This paper develop an MMG model with multiple renewable energy sources and small-scale EVs, aiming to maximize the use of renewable energy sources and realize the charging demand of EVs, and highlighting the potential role of EVs in MMGs. In addition, the paper underscores the indispensable role of measurement technology in microgrids and the impetus that microgrid development provides for advancements in measurement technology. To this end, this paper proposes an improved wolf pack algorithm (IWPA) based on the standard wolf pack algorithm (WPA) with a spiral search approach and chaotic updating of individuals to improve the global search capability of the algorithm and the complexity of solving the scheduling problem. Through simulation experiments on ten standard test functions and examples, it is verified that the IWPA algorithm improves the search accuracy by 2.8%-6.8% and 13.9%-18.3% in the worst and best cases, respectively, in comparison with other algorithms, and it also has a faster convergence speed. Meanwhile, this paper proposes a load interval pricing strategy for the shortcomings of time-of-use pricing strategy and traditional real-time pricing strategy, which is simulated under grid-connected operation, isolated grid operation, and multi-microgrid cooperative operation modes, and the simulation results of the arithmetic example show that this strategy can effectively reduce carbon emissions, and IWPA can effectively coordinate renewable energy, EVs, and other energy resources to achieve efficient energy management of MMGs and supply-demand balance.

关键词： microgrid electric vehicle wolf pack algorithm pricing strategy optimization scheduling

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Design of Highly Uniform Magnetic Field Coils With wolf pack algorithm

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IEEE SENSORS JOURNAL 2021年第4期21卷 4412-4424页

作者： Xu, Jun Kang, Xiangyu Fan, Zhengkun Zhang, Zhanfei Li, Yang Dong, Xiangmei Gao, Xiumin Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China

Uniform magnetic fields are widely used in atomic magnetometers, MRI and other fields. The traditional method of solving closed-form equations is usually used to design highly uniform magnetic field coils. In this paper, a new intelligent algorithm, wolf pack algorithm (WPA), is proposed to design the coil structure of a highly uniform magnetic field. This method can replace the traditional method to obtain the coil parameters. We additionally use a discrete optimization method to solve the rounding error. According to the investigation, compared with particle swarm optimization (PSO) algorithm, WPA has stronger global optimization ability, faster convergence speed and multiple optimization strategies. We set appropriate coil constraints (structural and process constraints) and simplify the multi-dimensional solutions to one-dimensional solutions to reduce the difficulty of operation. The distribution of magnetic field intensity of three axes of coils is analyzed by the finite element analysis method, and the 1%, 0.1%, and 0.01% uniform regions are studied. Under the same conditions, the uniform magnetic field regions of the coils designed by the PSO and WPA, as well as a single pair of axial and radial coils are compared. The performance results of multiple pairs of coils are superior to those of a single pair of coils. In general, the uniformity of the axial and radial magnetic field coils designed by the WPA is better than that designed by the PSO. Finally, the experimental value of the magnetic field uniformity is nearly consistent with the theoretical value.

关键词： Coils Magnetic fields Magnetic sensors Sensors Magnetometers Optimization Convergence Uniform magnetic field coil structure design wolf pack algorithm finite element analysis

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