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Design of Highly Uniform Magnetic Field Coils With wolf pack algorithm

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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Comparative Study of wolf pack algorithm and Artificial Bee Colony algorithm: Performance Analysis and Optimization Exploration

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2024年第1期15卷 1页

作者： Peng, Qiang Zhan, Renjun Wu, Husheng Shi, Meimei Engn Univ PAP Coll Equipment Support & Management Xian Peoples R China Engn Univ PAP Xian Peoples R China

Swarm intelligence optimization algorithms have been widely used in the fields of machine learning, process control and engineering prediction, among which common algorithms include ant colony algorithm (ACO), artificial bee colony algorithm (ABC) and particle swarm optimization (PSO). wolf pack algorithm (WPA) as a newer swarm intelligence optimization algorithm has many similarities with ABC. In this paper, the basic principles, algorithm implementation processes, and related improvement strategies of these two algorithms were described in detail;A comparative analysis of their performance in solving different feature-based standard CEC test functions was conducted, with a focus on optimization ability and convergence speed, re-validating the unique characteristics of these two algorithms in searching. In the end, the future development trend and prospect of intelligent optimization algorithms was discussed, which is of great reference significance for the research and application of swarm intelligence optimization algorithms.

关键词： Swarm Intelligence wolf pack algorithm Artificial Bee Colony algorithm Performance Comparison

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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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Drunkard Adaptive Walking Chaos wolf pack algorithm in Parameter Identification of Photovoltaic Module Model

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ENERGIES 2022年第17期15卷 6340页

作者： Wu, Husheng Peng, Qiang Shi, Meimei Xing, Lining Cheng, Shi Engn Univ PAP Coll Equipment Support & Management Xian 710086 Peoples R China Engn Univ PAP Fdn Dept Xian 710086 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

The rapid and accurate identification of photovoltaic (PV) model parameters is of great significance in solving practical engineering problems such as PV power prediction, maximum power point tracking and battery failure model recognition. Aiming at the shortcomings of low accuracy and poor reliability and being easy to fall into local optimization when standard intelligent optimization algorithms identify PV model parameters, a novel drunken adaptive walking chaotic wolf swarm algorithm is proposed, which is named DCWPA for short. The DCWPA uses the chaotic map sequence to initialize the population, thus to improve the diversity of the initial population. It adopts the walking direction mechanism based on the drunk walking model and the adaptive walking step size to increase the randomness of walking, enhance the individual's ability to explore and develop and improve the ability of algorithm optimization. It also designs the judgment conditions for half siege in order to accelerate the convergence of the algorithm and improve the speed of the algorithm. In the iterative process, according to the change of the optimal solution, the Hamming Distance is used to judge the similarity of individuals in the population, and the individuals in the population are constantly updated to avoid the algorithm from stopping evolution prematurely due to falling into local optimization. This paper firstly analyzes the time complexity of the algorithm, and then selects eight standard test functions (Benchmark) with different characteristics to verify the performance of the DCWPA algorithm for continuous optimization, and finally the improved algorithm is applied for parameter identification of PV models. The experiments show that the DCWPA has higher identification accuracy than other algorithms, and the results are more consistent with the measured data. Thus, the effectiveness and superiority of the improved algorithm in identifying solar cell parameters are verified, and the iden

关键词： photovoltaic model parameter identification swarm intelligence wolf pack algorithm chaotic initialization drunken walk

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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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Array Antenna Pattern Synthesis Based on Selective Levy Flight Culture wolf pack algorithm

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Journal of Harbin Institute of Technology(New Series) 2020年第5期27卷 68-80页

作者： Ting Wang Hailin Tang Yuebao Yu Bin Zheng Huijuan Liu School of Electronic Information Engineering Hebei University of TechnologyTianjin 300401China People’s Liberation Army Air Force 93756 Tianjin 300000China Department of Film and Television Technology Tianjin Broadcasting TV and Film InstituteTianjin 300112China

Due to the shortcomings such as the premature convergence and the bad local optimal searching capability in traditional intelligence methods for pattern synthesis,a new type of wolf pack algorithm named Levy⁃Cultural wolf pack algorithm(LCWPA)was designed on the basis of the Cultural wolf pack algorithm(CWPA),which obeys the selective Levy *** of the good overall management ability provided by the cultural algorithm in optimization process and the characteristics of excellent population diversity brought by Levy flight,the search efficiency of the new algorithm was greatly *** the algorithm was applied in the pattern synthesis of array antenna,the simulation results showed its high performance with multi⁃null and low side⁃lobe *** addition,the algorithm was superior to the Quantum Particle Swarm Optimization(QPSO),Particle Swarm Optimization(PSO),and Genetic algorithm(GA)in optimization accuracy and operation speed,and is of very good generalization.

关键词： array antenna pattern synthesis Levy flight wolf pack algorithm

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Multi-UAV task assignment based on improved wolf pack algorithm 2020

Multi-UAV task assignment based on improved Wolf Pack Algori...

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Haowen Chen Jinyu Xu Chao Wu Aeronautics Engineering College Air Force Engineering University Xi'an Shaanxi China

ISBN: (纸本)9781450387828

Aiming at the task allocation problem of heterogeneous multi-UAVs with different loads, an improved wolf pack algorithm is proposed. For the heterogeneous UAVs with multiple payloads, this paper divides the complex combat tasks into three subtasks: reconnaissance, strike and evaluation, and builds a task allocation model based on the specific load performance. The Gaussian walking in Stochastic Fractal Search (SFS) after chaos optimization is introduced into the wolf pack algorithm (WPA) through an adaptive mechanism, and a chaotic wolf pack algorithm based on enhanced Stochastic Fractal Search (MSFS-CWPA) is proposed. After the classic benchmark function including single/ multiple peak and the example verification of heterogeneous multi-UAV task allocation, the results show that MSFS-CWPA has higher convergence accuracy and robustness than the standard WPA and other improved WPA.

关键词： Adaptive mechanism wolf pack algorithm Task allocation problem Stochastic Fractal Search

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Improved wolf pack algorithm for Solving a Class of Multi-Objective Route Optimization Problem

Improved Wolf Pack Algorithm for Solving a Class of Multi-Ob...

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作者： Xin Jing Hao Gao Li Zheng Tianxin Yue College of Science Shenyang Jianzhu University SIASUN Robot & Automation CO. Ltd

This paper considers a class of Multi-objective path planning and solving *** choice of step size is a very important factor that affects the iterative algorithm. This paper improves the classical wolf pack algorithm(WPA) by designing an adaptive step size. This new improved wolf pack algorithm(IWPA) improves the classical wolf colony algorithm by designing an adaptive step size. It effectively solves the problem that the WPA may fall into a endless loop. The simulation results by MATLAB show that the IWPA has a good effectiveness than the WPA to solve multi-objective route optimization.

关键词： wolf pack algorithm Multi-objective path optimization wolf pack algorithm Adaptive step size Simulation

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