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

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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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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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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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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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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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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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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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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作者： Jiang Yi Zhang Lu Lu Jinhu Lanzhou Jiaotong University

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 *** the visiting point of each overlapping area,and use the wolf pack algorithm(WPA) to plan a traveling *** 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 *** 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 *** 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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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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