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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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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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Approach to Port Logistics Optimization in Promoting One Belt One Road Policy Based on Improved wolf pack algorithm

Approach to Port Logistics Optimization in Promoting One Bel...

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第十届（2018）金融风险与公司金融国际研讨会

作者： Li Guangqiang Zhang Qing Yu Zhihao Zhang Zhaobao Liu Qi Xu Xueliu College of Marine Electrical Engineering Dalian Maritime University Faculty of Management and Economics Dalian University of Technology

There are many optimization problems in port logistics automation in promoting One Belt One Road *** intelligent algorithms are regarded as promising approach to complex port logistics optimization *** pack algorithm(WPA) is a newly proposed algorithm,which has been widely applied in many fields such as automated container terminals,port logistics optimization,and regional economic ***,defects still exist in *** at the defects of WPA,such as fixed scouting direction and R artificial wolves with worst function values will directly be removed,an improved wolf pack algorithm named RHWPA is proposed based on random scouting direction and hunger value *** each scouting,fixed directions h is selected randomly within a certain *** can avoid the scout wolves falling into local optima,and improve the convergence speed and optimization *** further improve the convergence speed,the hunger value strategy is applied instead of the stronger-survive renewing rule for the wolf *** results on the benchmark functions show that RHWPA is feasible and *** work is expected to provide inspiration and help to solving corresponding port logistics optimization problems perfectly on promotion One Belt One Road policy.

关键词： wolf pack algorithm optimization function optimization One Belt One Road

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An Adaptive, Discrete Space Oriented wolf pack Optimization algorithm for a Movable Wireless Sensor Network

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SENSORS 2019年第19期19卷 4320-4320页

作者： Wang, Dongxing Wang, Huibo Ban, Xiaojuan Qian, Xu Ni, Jingxiu China Univ Min & Technol Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Beijing Adv Innovat Ctr Mat Genome Engn Beijing 100083 Peoples R China Beijing Union Univ Engn Integrated Expt Teaching Demonstrat Ctr Beijing 100101 Peoples R China

Recently, many related algorithms have been proposed to find an efficient wireless sensor network with good sustainability, a stable connection, and a high covering rate. To further improve the coverage rate of movable wireless sensor networks under the condition of guaranteed connectivity, this paper proposes an adaptive, discrete space oriented wolf pack optimization algorithm for a movable wireless sensor network (DSO-WPOA). Firstly, a strategy of adaptive expansion based on a minimum overlapping full-coverage model is designed to achieve minimum overlap and no-gap coverage for the monitoring area. Moreover, the adaptive shrinking grid search wolf pack optimization algorithm (ASGS-CWOA) is improved to optimize the movable wireless sensor network, which is a discrete space oriented problem. This improvement includes the usage of a target-node probability matrix and the design of an adaptive step size method, both of which work together to enhance the convergence speed and global optimization ability of the algorithm. Theoretical research and experimental results indicate that compared with the coverage algorithm based on particle swarm optimization (PSO-WSN) and classical virtual force algorithm, the newly proposed algorithm possesses the best coverage rate, better stability, acceptable performance in terms of time, advantages in energy savings, and no gaps.

关键词： wireless sensor network coverage rate wolf pack algorithm optimization probability matrix adaptive step size

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Modified Artificial wolf pack Method for Maximum Power Point Tracking under Partial Shading Condition

Modified Artificial Wolf Pack Method for Maximum Power Point...

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International Conference on Power and Embedded Drive Control (ICPEDC)

作者： Gupta, Shweta Saurabh, Kumar Polvent Control Engineers Dept Engn New Delhi India

ISBN: (纸本)9781509046799

Photovoltaic power generation systems operate at maximum power point to maximize the use of solar energy. MPP tracking (MPPT) is usually attained using several traditional methods. But during partial shading conditions, a PV panel shows multiple peaks in power versus voltage curve and traditional methods do not always promise to make operate PV panel at true MPP. This paper introduces modified artificial wolf pack (MAWP) algorithm for global maximum power point (GMPP) tracking under various conditions. The problem formulation, application of MAWP and the results are analyzed in this paper. The proposed method has been simulated on MATLAB/SIMULINK for one PV configuration under one shading pattern. The results have been compared with artificial bee colony (ABC) and particle swarm optimization (PSO) method which suggests that the proposed method is best amongst all MPPT alternatives.

关键词： maximum power point tracking wolf pack algorithm partial shading artificial bee colony particle swarm optimization

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A wolf pack Optimization Theory Based Improved Density Peaks Clustering Approach

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2020年第6期27卷 1910-1916页

作者： Tian, Jinlong Zhao, Jianping Zhou, Wei Han, Xuming Wang, Limin Changchun Univ Sci & Technol Sch Comp Sci & Engn Changchun 130022 Peoples R China Jinan Univ Coll Informat Sci Technol Guangzhou 510632 Peoples R China Guangdong Univ Finance Sch Internet Finance & Informat Engn Guangzhou 510521 Peoples R China

In view of the problem that the Density Peaks Clustering (DPC) algorithm needs to manually set the parameter cut-off distance (d(c)) we propose a wolf pack optimization theory based Density Peaks Clustering approach (WPA-DPC). Firstly, we introduce d, parameter into the wolf pack algorithm (WPA) to speed up the search. Secondly, we introduce the WPA into the DPC algorithm;the cut-off distance is used as the location of the wolf group. Finally, we make silhouette index in the search process as the fitness value, and the optimal location of the wolf group is the parameter value at the end. The simulation results show that compared with the traditional Density Peaks Clustering algorithm, the proposed algorithm is closer to the true clustering number. According to the evaluation results of silhouette and f-measure, the quality of clustering and the accuracy are greatly improved.

关键词： cut-off distance density peaks clustering evaluation index wolf pack algorithm

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Robust Voltage Control Considering Uncertainties of Renewable Energies and Loads via Improved Generative Adversarial Network

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Journal of Modern Power Systems and Clean Energy 2020年第6期8卷 1104-1114页

作者： Qianyu Zhao Wenlong Liao Shouxiang Wang Jayakrishnan Radhakrishna Pillai the Key Laboratory of Smart Grid of Ministry of Education and Tianjin Key Laboratory of Power System Simulation and Control Tianjin UniversityTianjinChina the Department of Energy Technology Aalborg UniversityAalborgDenmark

The fluctuation of output power of renewable energies and loads brings challenges to the scheduling and operation of the distribution *** this paper,a robust voltage control model is proposed to cope with the uncertainties of renewable energies and loads based on an improved generative adversarial network(IGAN).Firstly,both real and predicted data are used to train the IGAN consisting of a discriminator and a *** noises sampled from the Gaussian distribution are fed to the generator to generate a large number of scenarios that are utilized for robust voltage control after scenario ***,a new improved wolf pack algorithm(IWPA)is presented to solve the formulated robust voltage control model,since the accuracy of the solutions obtained by traditional methods is *** simulation results show that the IGAN can accurately capture the probability distribution characteristics and dynamic nonlinear characteristics of renewable energies and loads,which makes the scenarios generated by IGAN more suitable for robust voltage control than those generated by traditional ***,IWPA has a better performance than traditional methods in terms of convergence speed,accuracy,and stability for robust voltage control.

关键词： Robust voltage control uncertainty generative adversarial network wolf pack algorithm

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Optimal Deployment Analysis of Airborne Radars Detecting Stealth Target Using UAV Swarms

Optimal Deployment Analysis of Airborne Radars Detecting Ste...

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IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)

作者： Wang, Weijia Bai, Peng Liang, Xiaolong Zhang, Jiaqiang Hu, Liping Air Force Engn Univ Natl Key Lab Air Traff Collis Prevent Xian Peoples R China

ISBN: (纸本)9781538611715

Radar deployment is of great importance for airborne radar network performance. An efficient deployment required a good detection performance and energy consumption. This paper focuses on the detection of directional stealth target using UAV swarms equipped with active and passive radars. Based on calculated bistatic radar cross section (RCS) of stealth targets and Cassini oval sensing region, the optimum geometrical structures are analyzed with bistatic radars (BRs) using multiple mobile adaptive forward receivers to extend the dynamic detection range and coverage. Multi-objective optimization model is introduced and many critical constraints have been identified. Grid partition method and wolf pack algorithm (WPA) are applied to search the optimal deployments. In particular, we consider the problem of deploying an airborne network of BRs in the surveillance region to maximize the worst-case intrusion detectability as well as the vulnerability of active radar. Our findings provide valuable and practical insights into deployment of UAVs for stealth target tracking and the efficiency of the proposed algorithm is also validated by our simulation results.

关键词： radar deployment stealth target bistatic radars multi-objective optimization wolf pack algorithm

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Research on Delay Prediction Compensation of Teleoperation Robot Network Control System 5

Research on Delay Prediction Compensation of Teleoperation R...

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5th International Conference on Fuzzy Systems and Data Mining (FSDM)

作者： Zhang, Lanyong Meng, Kun Wang, Keqing Harbin Engn Univ Coll Automat Harbin Peoples R China State Key Lab Millimeter Waves Nanjing Peoples R China CGNPC Inspect Technol Co Ltd Suzhou Peoples R China

ISBN: (纸本)9781643680194;9781643680187

Time delays in network transmission have been a concern in teleoperation robot. In order to solve the problem, this paper proposes a delay prediction compensation scheme based on wolf pack algorithm (WPA)-Back Propagation (BP) neural network model combined with generalized predictive control algorithm. The WPA has optimized the initial weights and threshold of the BP neural network and has improved the convergence speed and prediction accuracy of the BP neural network so that the WPA-BP model can accurately predict the time delay of a network. The time delay obtained by the model was combined with the improved generalized predictive control algorithm to calculate control increments for the design of a controller. The improved generalized predictive control algorithm can directly identify controller parameters without having to solve the Diophantine equation, saving time on online computation. The simulation results show that the solution can well compensate the time delay of network transmission ensuring a real-time and stable system.

关键词： Teleoperation robot Network time delay wolf pack algorithm BP neural network Generalized predictive control

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Temporarily Distributed Hierarchy in Unmanned Vehicles Swarms

Temporarily Distributed Hierarchy in Unmanned Vehicles Swarm...

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7th International Conference on Swarm Intelligence (ICSI)

作者： Yang, Hong-an Velasco, Luis Carlos Zhang, Ya Zhang, Ting Wang, Jingguo Northwestern Polytech Univ Sch Mech Engn Xian Peoples R China

ISBN: (纸本)9783319410098;9783319410081

There is an increasing number of situations where a group of unmanned vehicles, instead of only one, would optimize efficiency in several military and civilian applications. One of the main limitations with current technology is the fact that the number of unmanned vehicles under control is directly linked with the number of operators controlling each one of them. In this paper the authors propose a system, integrating the social hierarchical behavior of a wolf pack, aiming to obtain an algorithm with hybrid and dynamic features designed to make possible the effective control of a swarm of unmanned vehicles. This system temporarily distributes levels of hierarchy among the members of the drone swarm, which makes possible to control any member of the swarm at any given time. The experimental results in a testbed with three mobile robots prove the efficiency of the proposed system.

关键词： Unmanned vehicles Swarm intelligence Swarm robotics Distributed hierarchy wolf pack algorithm

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