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Multi-Objective Parameter Estimation of Improved Muskingum Model by wolf pack algorithm and Its Application in Upper Hanjiang River, China

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WATER 2018年第10期10卷 1415-1415页

作者： Bai, Tao Wei, Jian Yang, Wangwang Huang, Qiang Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian 710048 Shaanxi Peoples R China CCCC Tianjin Ecoenvironm Protect Design & Res Ins Tianjin 300461 Peoples R China

In order to overcome the problems in the parameter estimation of the Muskingum model, this paper introduces a new swarm intelligence optimization algorithm-wolf pack algorithm (WPA). A new multi-objective function is designed by considering the weighted sum of absolute difference (SAD) and determination coefficient of the flood process. The WPA, its solving steps of calibration, and the model parameters are designed emphatically based on the basic principle of the algorithm. The performance of this algorithm is compared to the Trial algorithm (TA) and Particle Swarm Optimization (PSO). Results of the application of these approaches with actual data from the downstream of Ankang River in Hanjiang River indicate that the WPA has a higher precision than other techniques and, thus, the WPA is an efficient alternative technique to estimate the parameters of the Muskingum model. The research results provide a new method for the parameter estimation of the Muskingum model, which is of great practical significance to improving the accuracy of river channel flood routing.

关键词： Muskingum model wolf pack algorithm parameters optimization flood routing

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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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Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by wolf pack algorithm

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ENERGIES 2018年第6期11卷 1449页

作者： Zhang, Xing North China Elect Power Univ Dept Econ Management Baoding 071003 Peoples R China

Accurate short-term load forecasting is of momentous significance to ensure safe and economic operation of quick-change electric bus (e-bus) charging stations. In order to improve the accuracy and stability of load prediction, this paper proposes a hybrid model that combines fuzzy clustering (FC), least squares support vector machine (LSSVM), and wolf pack algorithm (WPA). On the basis of load characteristics analysis for e-bus charging stations, FC is adopted to extract samples on similar days, which can not only avoid the blindness of selecting similar days by experience, but can also overcome the adverse effects of unconventional load data caused by a sudden change of factors on training. Then, WPA with good global convergence and computational robustness is employed to optimize the parameters of LSSVM. Thus, a novel hybrid load forecasting model for quick-change e-bus charging stations is built, namely FC-WPA-LSSVM. To verify the developed model, two case studies are used for model construction and testing. The simulation test results prove that the proposed model can obtain high prediction accuracy and ideal stability.

关键词： short-term load forecasting electric bus charging station fuzzy clustering least squares support vector machine wolf pack algorithm

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A clustering routing algorithm based on wolf pack algorithm for heterogeneous wireless sensor networks

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COMPUTER NETWORKS 2020年第0期167卷 106994-000页

作者： Xiu-wu, Y. U. Hao, Y. U. Yong, Liu Ren-rong, Xiao Univ South China 28 Changsheng West Rd Hengyang 421001 Hunan Peoples R China Hunan Engn Res Ctr Uranium Tailings Decommiss & T 28 Changsheng West Rd Hengyang 421001 Hunan Peoples R China

In order to maximize the network performance of heterogeneous sensor networks and effectively control the network cost, a clustering routing algorithm based on wolf pack algorithm (CLWPA) for heterogeneous wireless sensor networks is proposed. Firstly, the optimal deployment of heterogeneous nodes is transformed into a mixed integer programming problem. The approximate optimal solution of the problem is obtained by through the wolf pack algorithm (WPA) which improved by logistic function and levy flight then a heterogeneous network routing algorithm based on the improved wolf pack algorithm (LWPA) is proposed. Secondly, in order to solve the problem of fixed path in LWPA routing algorithm, the concept of edge degree is introduced to improve DEEC algorithm. The improved DEEC algorithm (IDEEC) is used to dynamically cluster common nodes in heterogeneous networks, and the data transmission mode is carried out after the clustering mode set. Finally, through simulation analysis, compared with other three heterogeneous network routing algorithms, CLWPA algorithm effectively prolongs the network's stable period and lifetime, and the energy consumption is more balanced. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Heterogeneous sensor networks wolf pack algorithm Routing algorithm Dynamic clustering

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A Study of wolf pack algorithm for Test Suite Reduction

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2022年第5期29卷 1522-1527页

作者： LI, Zemin Lei, Shaobo Tan, Fugui Liu, Yupeng Xiao, Bin Huang, Xin Ren, Xu Inner Mongolia Power Grp Co Ltd Qianda Men Rd Hohhot Inner Mongolia Peoples R China Inner Mongolia Elect Power Grp Co 9 Qianda Men Rd Hohhot Inner Mongolia Peoples R China Holley Technol Co Ltd 181 Wuchang AveWuchang St Hangzhou Zhejiang Peoples R China

Modern smart meter programs are iterating at an ever-increasing rate, placing higher demands on the software testing of smart meters. How to reduce the cost of software testing has become a focus of current research. The reduction of test overhead is the most intuitive way to reduce the cost of software testing. Test suite reduction is one of the necessary means to reduce test overhead. This paper proposes a smart meter test suite reduction technique based on wolf pack algorithm. First, the algorithm uses the binary optimization set coverage problem to represent the test suite reduction of the smart meter program;then, the wolf pack algorithm is improved by converting the positions of individual wolves into a 0/1 matrix;finally, the optimal test case subset is obtained by iteration. By simulating different smart meter programs and different size test suites, the experimental result shows that the wolf pack algorithm achieves better results compared to similar algorithms in terms of the percentage of obtaining both the optimal solution and the optimal subset of test overhead.

关键词： smart meters software testing test suite reduction 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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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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TRUSS STRUCTURE OPTIMIZATION BASED ON IMPROVED wolf pack algorithm

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CIVIL ENGINEERING JOURNAL-STAVEBNI OBZOR 2019年第4期28卷 490-505页

作者： Li, Yan-cang Yang, Zong-jin Dang, Cheng-hua Hebei Univ Engn Coll Water Conservancy & Hydroelect Power 199 Guangmin South St Handan Peoples R China Hebei Univ Engn Coll Civil Engn 199 Guangmin South St Handan Peoples R China Hebei Univ Engn 199 Guangmin South St Handan Peoples R China

Aiming at the optimization of truss structure, a wolf pack algorithm based on chaos and improved search strategy was proposed. The mathematical model of truss optimization was constructed, and the classical truss structure was optimized. The results were compared with those of other optimization algorithms. When selecting and updating the initial position of wolves, chaos idea was used to distribute the initial value evenly in the solution space;phase factor was introduced to optimize the formula of wolf detection;information interaction between wolves is increased and the number of runs is reduced. The numerical results show that the improved wolf pack algorithm has the characteristics of fewer parameters, simple programming, easy implementation, fast convergence speed, and can quickly find the optimal solution. It is suitable for the optimization design of the section size of space truss structures.

关键词： wolf pack algorithm Improved wolf pack algorithm Truss optimization Chaos thought

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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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