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Improved wolf pack algorithm for UAV Path Planning Problem

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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Prediction method of mine gas emission based on complex neural work optimized by wolf pack algorithm

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2018年第3期6卷 85-91页

作者： Xu, Liqiang Wang, Binguo Du, Xuedong Hong, Yongfa Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Shandong Peoples R China Shandong Univ Sci & Technol Prov Comp Expt Teaching Demonstrating Ctr Shandon Qingdao Shandong Peoples R China

In view of the local extreme problem of the gradient descent algorithm, which makes the working face of mine gas emission prediction uncertainly, this paper combined wolf pack algorithm (WPA) with complex neural network nonlinear prediction method to the established new prediction model. The WPA shows good global convergence and computational robustness in the solving process of complex high-dimensional functions. Working face in a coal mine as a case, this paper selects seven factors as input variables of the mine gas emission prediction, uses training data to mature prediction model and adopted it to predict six group gas emission data. Research results show that the mean absolute percentage value of the complex neural network model which has been optimized by WPA is 0.06%, the root mean square error value is 0.0191, the mean absolute error value is 0.0175 and the equal coefficient value is 0.9979. The prediction results are very close to the real value, and the change trend is highly consistent with the actual situation.

关键词： Mine gas emission prediction complex neural network wolf pack algorithm nonlinear prediction method

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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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Improved wolf pack algorithm with Differential Evolution for Global Numerical Optimization 10

Improved Wolf Pack Algorithm with Differential Evolution for...

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10th International Conference on Advanced Computational Intelligence (ICACI)

作者： Chen, Xiayang Tang, Chaojing Zhang, Lei Liu, Yi Natl Univ Def Technol Coll Elect Sci Changsha Hunan Peoples R China

ISBN: (纸本)9781538643624

wolf pack algorithm (WPA) is a new optimal algorithm with outstanding performance, but there is still room for improvement of its convergence Aiming at speeding up the convergence of the WPA, a novel hybrid optimal algorithm based on wolf pack algorithm and Differential Evolution (DE) is proposed, which named WPADE for short. Differential Evolution is introduced into the process of the wolves updating to replace the random updating. Four standard benchmark functions are applied to verify the effects of these improvements. Our experiments shows that the performance of WPADE is better than the basic WPA and other evolution algorithm or swarm intelligence optimal algorithm, such as GA, DE, PSO, and ABS.

关键词： wolf pack algorithm Differential Evolution swarm intelligence intelligent optimization algorithms metaheuristic algorithms

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The Path Planning Of Mobile Sink Based On wolf pack algorithm

The Path Planning Of Mobile Sink Based On Wolf Pack Algorith...

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International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS)

作者： Jiang Yi Zhang Lu Liu Jinhu Lanzhou Jiaotong Univ Lanzhou 710073 Gansu Peoples R China

ISBN: (纸本)9781728113074

Mobile sinks avoids imbalances in energy consumption caused by multihop transmission in wireless sensor networks (WSNs). In order to shorten the length of the travel path and further reduce the energy consumption of the network transmission, the mobile sinks travel path was planned. Determining the visiting point of each overlapping area, and use the wolf pack algorithm (WPA) to plan a traveling path. Because the visiting point is a point within the overlapping area of communication ranges of sensors not one by one access to each node, it effectively reduces the length of the path. This method not only shortens the length of the traveling path for the mobile sink but reduces the computational effort required for traveling-path planning by the TSP algorithm as well. The computer simulation proved that the routing protocol can reduce and balance the energy consumption efficiently, prolonging the wireless sensor network survival period and improve the link quality.

关键词： Wireless Sensor Networks wolf pack algorithm Contribution Distribution Energy Consumption

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Target recognition of BP network based on Optimized wolf pack algorithm 3

Target recognition of BP network based on Optimized wolf pac...

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3rd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Gao, Xiaoyang Wan, Pengcheng Air Force Engn Univ Air & Missile Def Coll Xian Shaanxi Peoples R China

ISBN: (纸本)9781538645093

In air defense operation, the target identification of air is an important part. The BP network is more commonly used for target identification, but it has a slow convergence speed and easy to fall into local optimal disadvantages. Immune algorithm clone and mutation operation in reference to the wolves in the update mechanism improve the convergence speed of wolves in the algorithm, using immune wolf pack algorithm to optimize initial weights and threshold value of BP network, improve the defect of easily plunged into local optimum and improve the convergence speed of BP network, the optimized BP network is used to solve the problem of target recognition. The simulation results show that the algorithm is efficient and effective.

关键词： target recognition wolf pack algorithm immune algorithm BP network

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A Novel Hybrid Based on wolf pack algorithm and Differential Evolution algorithm 10

A Novel Hybrid Based on Wolf Pack Algorithm and Differential...

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10th International Symposium on Computational Intelligence and Design (ISCID)

作者： Chen, Xiayang Tang, Chaojing Wang, Jian Zhang, Lei Liu, Yi Natl Univ Def Technol Coll Elect Sci Changsha Hunan Peoples R China

ISBN: (纸本)9781538636756

The wolf pack algorithm (WPA) is a new optimal algorithm with good performance in optimation, but there is still room for improvement of its convergence. Aiming at enhancing the ability of the local search and increasing the individual diversity in the population of the wolf pack algorithm, a novel hybrid based on wolf pack algorithm and Differential Evolution (DE) was proposed, which named WPA/DE for short. Six standard benchmark functions are applied to verify the effects of these improvements. It is shown that the performance of WPA/DE is superior to, or at least highly competitive with, the standard WPA and other swarm intelligence optimal algorithm, such as GA, DE, PSO, and ABC in most situations.

关键词： wolf pack algorithm Differential Evolution swarm intelligence evolutionary computation

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An improved wolf pack algorithm for sustainable machining parameter optimization 3

An improved wolf pack algorithm for sustainable machining pa...

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3rd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Zhao, Dongsheng Li, Xiaoxia Yang, Jie Huazhong Agr Univ Coll Sci Wuhan 430072 Hubei Peoples R China Huazhong Agr Univ Coll Informat Wuhan 430072 Hubei Peoples R China

ISBN: (纸本)9781538645093

Machining parameter is one of the influence factors of energy consumption in numerical control machining process. In this paper, spindle speed, feedrate, cutting depth and cutting width are selected as the machining parameters to be optimized. The artificial neural network is used to establish an energy consumption model to characterize the relationship between energy consumption and the machining parameters. Based on the energy consumption model, an improved wolf pack algorithm is presented to search for optimal machining parameters to minimize the energy consumed in machining process. The method has been tested and verified based on experimental data. The experimental results show that this optimization method is very effective for the sustainable machining parameter optimization.

关键词： machining parameter optimization sustainable manufacturing wolf pack algorithm artificial neural network

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The Research of Improved wolf pack algorithm Based on Differential Evolution 1st

The Research of Improved Wolf Pack Algorithm Based on Differ...

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1st International Conference on Intelligent Manufacturing and Internet of Things (IMIOT) / 5th International Conference on Computing for Sustainable Energy and Environment (ICSEE)

作者： Wang, Yingxiang Chen, Minyou Cheng, Tingli Hassan, Muhammad Arshad Shehzad Chongqing Univ Sch Elect Engn State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China

ISBN: (纸本)9789811323843;9789811323836

Aiming at the problems of traditional wolf pack algorithm (WPA): easy to fall into local optimal, large computational resource cost and low robustness, an improved wolf pack algorithm based on differential evolution (DIWPA) is proposed. By introducing the search factor for search wolves, maximum number of raid wolves, adaptive siege step size and differential evolution strategy, the proposed algorithm can not only reduce the computational cost but also improve the global search ability. The DIWPA is used to conduct optimization test on 12 benchmark functions and compare to 3 typical optimization algorithms. The test results show that DIWPA has great robustness and global search ability, especially has excellent performance in multi-peak, high-dimension, indivisible functions.

关键词： wolf pack algorithm Local optimal Differential evolution Robustness Global search ability

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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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