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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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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 College of Equipment Support and Management Engineering University of PAP A1 Wujing Rd Xi’an 710086 China Foundation Department Engineering University of PAP A1 Wujing Rd Xi’an 710086 China School of Computer Science Shaanxi Normal University Xi’an 710119 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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

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

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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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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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Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid wolf pack algorithm and Particle Swarm Optimization

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IEEE ACCESS 2020年 8卷 29354-29369页

作者： Li Zhen Yang Liu Wang Dongsheng Zheng Wei Jiangsu Univ Sci & Technol Sch Elect & Informat Zhenjiang 212003 Jiangsu Peoples R China Jiangsu Univ Sci & Technol Reliabil & Syst Engn Open Grp Zhenjiang 212003 Jiangsu Peoples R China Jiangsu Univ Sci & Technol Sch Comp Sci Zhenjiang 212003 Jiangsu Peoples R China

Software reliability is estimated and predicted based on software reliability model and software failure data. As a new optimization method, swarm intelligence algorithm has been widely used in solving the parameter optimization of the model. WPA (wolf pack algorithm) and PSO (Particle Swarm Optimization) are two typical swarm intelligence algorithms. WPA has a strong global optimization ability, fast convergence speed and various optimization strategies, but the algorithm is relatively complex. PSO algorithm has a simple structure and fast convergence speed, but it is easy to fall into premature, which leads to low accuracy of solution. Considering the advantages and disadvantages of the two algorithms, a hybrid method of WPA and PSO is proposed, and a fitness function is constructed on maximum likelihood estimation, then the parameters of software reliability model are estimated and predicted based on the hybrid algorithm (WPA-PSO). Five sets of data from industry are used to estimate the parameters of GO model and make predictions. The simulation results show that the hybrid algorithm has higher accuracy of parameter estimation, better optimization performance, better accuracy of prediction and algorithm stability than single algorithm, and show obvious advantages than the single algorithm in the case of limited data.

关键词： Software reliability parameter estimation swarm intelligence wolf pack algorithm particle swarm optimization

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A Novel Fuzzy Time Series Forecasting Model Based on the Hybrid wolf pack algorithm and Ordered Weighted Averaging Aggregation Operator

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2020年第6期22卷 1832-1850页

作者： Xian, Sidong Li, Tangjin Cheng, Yue Chongqing Univ Posts & Telecommun Key Lab Intelligent Anal & Decis Complex Syst Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing 400065 Peoples R China

The fuzzy time series has received extensive attention since it was proposed and it has been widely used in various practical applications. This study proposes a new fuzzy time series forecasting model which considers a hybrid wolf pack algorithm (HWPA) and an ordered weighted averaging (OWA) aggregation operator for fuzzy time series. The HWPA is adopted to obtain a suitable partition of the universe of discourse to promote the forecasting performance. Furthermore, the improved OWA aggregation method is applied to make the aggregation of historical information more practical. To overcome the deficiency of slow convergence speed and easy to entrap into the local extremum of the wolf pack algorithm (WPA), the chemotactic behavior and elimination-dispersal behavior of bacterial foraging optimization (BFO) are employed to optimize the scouting behavior of WPA. The actual enrollments data of the University of Alabama and Taiwan Futures Exchange (TAIFEX) are utilized as the benchmark data and the computational results of both training and testing phases all indicate that the new forecasting model outperforms other existing models. The robustness of the proposed model is also tested and the robust results can be obtained when the historical data are inaccurate.

关键词： Fuzzy time series wolf pack algorithm Bacterial foraging optimization Ordered weighted averaging aggregation operator Hybrid wolf pack algorithm TAIFEX

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Infrared and Visible Image Fusion Based on the Total Variational Model and Adaptive wolf pack algorithm

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IEEE ACCESS 2020年 8卷 2348-2361页

作者： Feng, Xin Hu, Kaiqun Lou, Xicheng Chongqing Technol & Business Univ Coll Mech Engn Key Lab Mfg Equipment Mech Design & Control Chong Chongqing 400067 Peoples R China

The purpose of image fusion is to merge a substantial amount of information, such as contour, texture and intensity distribution information from original images, with a fusion image. To retain a considerable amount of the fusion image while retaining the texture details of the source image and maintaining the edge of the source image, this paper proposes an improved infrared and visible image fusion algorithm that is based on total variation. First, source infrared and visible light images and a difference image were decomposed by a total variation model, and their respective cartoon and texture components were acquired. A fitness function was solved according to the entropy, standard deviation and edge similarity of the infrared and visible light images. The optimal combination weight of various kinds of texture and cartoon components was sought via a wolf pack intelligence optimization algorithm to acquire the final fusion results with high-quality contrast ratio and edge details. The experimental results indicate that the proposed method not only can preserve edge contour information about the original image but also can effectively retain its texture detail information. The method is superior to the traditional multiscale and sparse representation fusion method with regard to various indicators, such as subjective visual effect, mutual information, gradient information, structural similarity and visual sensitivity.

关键词： Infrared and visible light image fusion total variation model wolf pack algorithm

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Self-adaptive wolf pack algorithm based on dynamic population updating for continuous optimisation problems

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INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL 2021年第4-5期15卷 502-544页

作者： Hu, Jinqiang Wu, Husheng Zhan, Renjun Li, Yongli Menassel, Rafik Armed Police Force Engn Univ Sch Equipment Management & Support Xian 710086 Peoples R China Shanghai Jiao Tong Univ Minist Educ Key Lab Marine Intelligent Equipment & Syst Shanghai 200240 Peoples R China Tebessa Univ Dept Math & Comp Sci Tebessa Algeria

wolf pack algorithm (WPA) is a relatively new swarm intelligence-based algorithm for solving complex continuous optimisation problems as well as real-world optimisation problems. The basic WPA and its variants are prone to trap into local optima and premature convergence when tackling multi-modal functions due to diversity loss problem and imbalance between exploration and exploitation. Inspired by the idea of integrating the heuristic information and stochastic strategies to balance exploration with exploitation, we propose a self-adaptive WPA based on dynamic population updating (SWPA-DU) strategy. First, the self-adaptive chaotic scouting behaviour is designed to develop the global exploration of scout wolves. Second, a novel Cauchy perturbation operator is proposed to generate a few mutation besieging wolves, which not only enhances the capability of jumping out of local optima but also improves local exploitation. Third, a dynamic population updating strategy is invented to improve diversity. Numerical experiments with a suit of benchmark functions and practical applications are performed to verify the effectiveness and advancement of the proposed algorithm. The experimental results indicate that SWPA-DU obtains superior performance on both multi-modal and high-dimensional problems over the compared algorithms.

关键词： swarm intelligence wolf pack algorithm WPA self-adaptive chaotic scouting behaviour Cauchy perturbation operator dynamic population updating

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