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Conference Applications of Digital Image Processing XLIV

作者： Voronin, Sergei Makovetskii, Artyom Kober, Vitaly Voronin, Aleksei Chelyabinsk State Univ Dept Math 129 Br Kashirinykh Chelyabinsk 454001 Russia CICESE Dept Comp Sci Carretera Ensenada Tijuana 3918 Ensenada 22860 Baja California Mexico

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

3D reconstruction has been widely applied in medical images, industrial inspection, self-driving cars, and indoor modeling. The 3D model is built by the steps of data collection, point cloud registration, surface reconstruction, and texture mapping. In the process of data collection, due to the limited visibility of the scanning system, the scanner needs to scan multiple angles and then splice the data to obtain a complete point cloud model. The point clouds from different angles must be merged into a unified coordinate system, which is known as point cloud registration. The result of point cloud registration can directly affect the accuracy of the point cloud model;thus, point cloud registration is a key step in the construction of the point cloud model. The ICP (Iterative Closest Points) algorithm is the most known technique of the point cloud registration. The variational ICP problem can be solved not only by deterministic but also by stochastic methods. One of them is grey wolf Optimizer (GWO) algorithm. Recently, GWO has been applied to rough point clouds alignment. In the proposed paper, we apply the GWO approach to the realization of the point-to-point ICP algorithms. Computer simulation results are presented to illustrate the performance of the proposed algorithm.

关键词： surface reconstruction point cloud registration iterative closest points (ICP) variational functionals orthogonal transformations grey wolf optimization algorithm

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A Guided Mutation grey wolf Optimizer algorithm Integrating Flower Pollination Mechanism

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电脑学刊 2022年第2期33卷 51-67页

作者： Jing-Sen Liu Qing-Qing Liu Fang Zuo

The basic grey wolf Optimizer (GWO) has some shortcomings, for example, the convergence speed is slow, it is easy to fall into local extremum, and high-dimensional optimization ability is poor and so on. In response to these shortcomings, an improved grey wolf algorithm which combines flower pollination mechanism, teaching mechanism and polynomial variation is proposed in this study. The flower pollination mechanism is integrated with GWO algorithm, Levy distribution is introduced into the global search of grey wolf population. And the double random mechanism is added in the local search, for these improvements, this algorithm's overall optimization performance is improved. The teaching mechanism is added to α wolf to improve the algorithm's convergence speed. Polynomial mutation is applied to the individuals with poor optimization effect to improve the algorithm's accuracy and its ability to jump out of local extremum. Theoretical analysis shows that the time complexity of the improved algorithm is the same as that of the basic algorithm. The test results of five representative comparison algorithms on multiple different characteristics and different dimensions of CEC2017 benchmark functions and two classical engineering problems show that FMGWO algorithm has high optimization accuracy, convergence speed and solution stability. Therefore, it has obvious advantages in global optimization.

关键词： grey wolf optimization algorithm flower pollination algorithm teaching mechanism polynomial mutation CEC2017

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An Improved Approach for Load Balancing among Virtual Machines in Cloud Environment

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Procedia Computer Science 2022年 215卷 556-566页

作者： Suman Sansanwal Nitin Jain Research Scholar Chandigarh University Mohali 140413 India Professor Chandigarh University Mohali 140413 India

Cloud computing has shown noteworthy evolution in information technology. Users can enjoy various services of cloud technology only if internet connection is available. In cloud computing, load balancing considered as a fundamental issue that has confronted researchers in this domain. Load balancing basically works by allotting fair and efficient work among computing resources which ultimately achieve high user satisfaction and raises systems productivity. Many load-balancing techniques made efforts to resolve this problem using metaheuristics algorithm, and amplify the operation and efficiency of systems. In this paper, existing load balancing techniques has been discussed and research gaps in existing lirture hasbeeb discussed. Also a new technique has been proposed called IG-GWO - Inquisitive Genetic grey wolf optimization algorithm using combination of grey wolf optimization (GWO) algorithm and Genetic algorithm.

关键词： Cloud computing load balancing grey wolf optimization algorithm Cloudsim

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Time Difference of Arrival Passive Location algorithm Based on grey wolf optimization 3

Time Difference of Arrival Passive Location Algorithm Based ...

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3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Guo, Limin Xie, Qiang Huang, Xiangsong Chen, Tao Harbin Engn Univ China Coll Informat & Commun Engn Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781509063529

Aiming at the nonlinear equation problems of time difference of arrival (TDOA) passive location, this paper proposes the improved grey wolf optimization (IGWO) algorithm. By adding the individual learning strategy to the standard grey wolf optimization (GWO) algorithm, and taking trigonometric function nonlinear convergence factor to balance the exploration ability and development ability, the proposed algorithm can search optimal coordinates of TDOA passive location. Simulation results show that the modified algorithm has stable performance and simple controlling parameters. The algorithm can quickly approach the global optimal solution with the small scale of wolf pack, and has faster convergence speed and higher locating precision.

关键词： passive locating time difference of arrival grey wolf optimization algorithm maximum likelihood estimation

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US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model

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ENERGY 2020年第0期194卷 116905-000页

作者： Lu, Hongfang Ma, Xin Azimi, Mohammadamin Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploita Chengdu 610500 Peoples R China Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Louisiana Tech Univ Trenchless Technol Ctr Ruston LA 71270 USA

Natural gas (NG) is a vital energy in the energy structure transition, and its consumption prediction is a significant issue in energy structure management and energy security. As the second largest energy consumer and producer in the world, the status of NG in the United States (US) energy system has been increasing since the "An America First Energy Plan" was proposed in 2017. Accurate prediction of natural gas consumption (NGC) can provide an effective reference for decision-makers, policymakers, and energy companies. This paper proposes an improved kernel-based nonlinear extension of the Arps decline model (KNEA) to forecast NGC in the US. The grey wolf optimization (GWO) algorithm is used to optimize the regularization parameter and kernel width in the KNEA model, and applies the hybrid model to the NGC datasets of different sectors (including lease and plant fuel usage, pipeline and distribution usage, residential users, commercial users, industrial users, vehicle fuels users, and power generation users) in the US. Compared with the prediction results of five benchmark models, it is shown that the GWO-KNEA model has the best performance in each dataset, and the range of mean absolute percentage error is less than 5%. By comparing the computational time and memory occupancy of the model, it can be concluded that the time and space complexity of the GWO-KNEA model is greater than that of the original KNEA model, but lower than that of other benchmark models. Moreover, this paper uses the newly proposed model to predict the NGC and consumption mix of the US from 2019 to 2025. The main conclusions are drawn: (1) NGC in the US will show a slow growth trend (the average annual growth rate is only 1.2%);(2) The proportion of NGC in power generation will increase significantly, reaching about 39% in 2025;(3) The proportion of residential, commercial and industrial NGC will decline slightly. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： United States Natural gas consumption Prediction KNEA grey wolf optimization algorithm Kernel method

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Model identification and strategy application for Solid Oxide Fuel Cell using Rotor Hopfield Neural Network based on a novel optimization method

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2020年第51期45卷 27694-27704页

作者： Ba, Shusong Xia, Dong Gibbons, Edward M. Univ Sci & Technol China Sch Management Dept Stat & Finance Hefei 230026 Anhui Peoples R China Univ Nova Gorica Sch Engn & Management Vipavska Cesta 5000 Nova Gorica Slovenia

The novelty of this paper is to suggest an effective method according to the application of the Rotor Hopfield Neural Network optimized by the grey wolf optimization (GWO) method for the identification of the Solid Oxide Fuel Cell (SOFC) model. In this literature, the basic required metrics to present the transient models of Solid Oxide Fuel Cell are defined. The proposed model is a hybrid model that is composed of the Rotor Hopfield Neural Network (RHNN) optimized by the GWO algorithm. The hybrid RHNN-GWO model, including a steady-state RHNN Neural Network, ensured by an optimization method. The RHNN algorithm is presented to assess the metrics of the RHNN-GWO model. In contrast to the wavering, the Mean Squared Error (MSE) for the RHNN-GWO model is calculated by 0.0017. The presented model results are examined with some well-known model results. The lowest values for Mean Squared Error belongs to the RHNN-GWO model. Also, the proposed model conserves a tremendous value of calculation time compared to the other models. Also, the proposed model shows a good agreement with SOFC results with lower computational difficulty. For 5000 samples, the variation of the voltage is in the [320, 360] V interval, which completely follows the reference voltage of the SOFC. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： SOFC Model identification Neural network Rotor Hopfield Neural Network grey wolf optimization algorithm

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Localization of multiple leaks in a fluid pipeline based on ultrasound velocity and improved GWO

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2020年 137卷 1-7页

作者： Lang Xianming Li Ping Zhang Baocun Cao Jiangtao Guo Ying Kan Zhe Lu Siyu Liaoning Shihua Univ Sch Informat & Control Engn Fushun 113001 Peoples R China Shandong Nat Gas Pipeline Co Ltd Jinan 250013 Shandong Peoples R China Shenyang Longchang Pipeline Survey Ctr Shenyang 110000 Peoples R China

It is difficult to effectively locate the position of multiple leaks in fluid pipelines;therefore, a localization method for multiple leak positions is proposed based on ultrasound velocity and improved grey wolf optimization algorithm (GWO). First, the mathematical relationship between ultrasound velocity and the pressure signal inside a pipeline is established. Second, the ultrasound velocity is decomposed by local mean decomposition (LMD), and the inflection point of the ultrasound velocity by using a wavelet transform after denoising is extracted. Then, the simulated annealing GWO (SAGWO) method is proposed to improve the performance of GWO which is easy to converge to local optimum. Finally, the inflection time of the ultrasound velocity is obtained at the ends of a pipeline with multiple leaks, and an objective function is established to estimate the localization of the multiple leaks by SAGWO. The field experiment demonstrates that the proposed method can effectively and accurately locate multiple leak positions in a fluid pipeline. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Ultrasound velocity Local mean decomposition Wavelet transform Multiple leaks localization Simulated annealing grey wolf optimization algorithm

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grey wolf based compressive sensing scheme for data gathering in IoT based heterogeneous WSNs

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WIRELESS NETWORKS 2020年第5期26卷 3395-3418页

作者： Aziz, Ahmed Osamy, Walid Khedr, Ahmed M. El-Sawy, Ahmed A. Singh, Karan Benha Univ Fac Comp & Artificial Intelligence Dept Comp Sci Banha Egypt Univ Sharjah Dept Comp Sci Sharjah 27272 U Arab Emirates Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India Sharda Univ Fac Engn & Technol Andijon Uzbekistan Qassim Univ Coll Community Unaizah Dept Appl Sci POB 931 Buridah 51931 Unizah Saudi Arabia Zagazig Univ Math Dept Zagazig Egypt

Sensor node energy constraint is considered as an impediment in the further development of the Internet of Things (IoT) technology. One of the most efficient solution is to combine between compressive sensing (CS) and routing techniques. However, this combination faces many challenges that makes it an attractive point for research. This paper proposes an Efficient Multi-hop Cluster-based Aggregation scheme using Hybrid CS (EMCA-CS) for IoT based heterogeneous wireless sensor networks (WSNs). EMCA-CS efficiently combines between CS and routing protocols to extend the network lifetime and reduces the reconstruction error. EMCA-CS includes the following: a new algorithm to partition the field into various hexagonal cells (clusters) and based on multiple criteria, selects a node from each cluster as cluster head (CH). Each CH will then compress its cluster data using hybrid CS method. Also, a new grey wolf based algorithm to create optimal path for CHs to deliver the compressed data to base station (BS) and a CSMO-GWO algorithm to optimize the CS matrix construction process is introduced. Moreover, a new grey wolf and reversible Greedy based Reconstruction algorithm is proposed to recover the actual data. The simulation results indicate that the performance of the proposed work exceeds the existing baseline techniques in terms of prolonging WSN lifetime, reducing the power consumption and reducing normalized mean square error.

关键词： Compressive sensing Cluster-based Energy consumption grey wolf optimization algorithm Network lifetime Internet of Things Routing techniques Wireless sensor networks

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Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 186卷

作者： Dong, Jianhua Liu, Xiaogang Huang, Guomin Fan, Junliang Wu, Lifeng Wu, Jie Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China Kunming Univ Sci & Technol Fac Agr & Food Kunming 650500 Yunnan Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Wuhan Polytech Univ Sch Civil Engn & Architecture Wuhan 430023 Peoples R China

Accurate estimation of reference crop evapotranspiration (ET0) is of great significance to crop water use and agricultural water resources management. This study evaluated the performance of four bio-inspired algorithm optimized kernel-based nonlinear extension of Arps decline (KNEA) models, namely KNEA with grasshopper optimization algorithm (GOA-KNEA), KNEA with grey wolf optimizer algorithm (GWO-KNEA), KNEA with particle swarm optimization algorithm (PSO-KNEA), and KNEA with salp swarm algorithm (SSA-KNEA), on estimating monthly ET0 across China. Monthly meteorological data [including maximum air temperature (Tmax), minimum air temperature (Tmin), extra-terrestrial solar radiation (Ra), relative humidity (RH), global solar radiation (Rs), wind speed (U)] during 1966-2015 from 51 weather stations across the seven different climate zones of China were used for model training and testing. Four different combinations of meteorological data were applied as model input, and results from the FAO-56 Penman-Monteith formula were used as a control. Results showed that the GWO-KNEA model overall performed better than the other three coupling models, of which the GWO-KNEA2 model (i.e., the model with input combination 2) was the best (on average R2 = 0.9814, RMSE = 0.2143 mm d-1). The convergence rate and the population size of the GWO-KNEA model were also superior to the other three models. Among input combinations, models with combination 2 had the best overall performance, while models with combination 3 were the worst on average. In terms of the importance of each meteorological parameter contributing to model accuracy, Rs was greater than Ra, RH, or U. Among different climate zones, station specific models in the semi-arid steppe of Inner Mongolia showed the best estimating performance in general, while models in the Qinghai-Tibetan Plateau overall performed relatively poorly. The GOA-KNEA and SSA-KNEA models with the combination 4 showed large increases (29.7% and 28.7

关键词： Reference evapotranspiration Climatic zones Grasshopper optimization algorithm grey wolf optimization algorithm Machine learning

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algorithms to Model and Optimize a Stand-Alone Photovoltaic-Diesel-Battery System: An Application in Rural Libya

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第2期28卷 523-529页

作者： Elbaz, Abdurazaq Tahir Guneser, Muhammet Karabuk Univ Engn Fac Elect & Elect Dept TR-78100 Karabuk Turkey

This paper introduces a new optimum calculation technique for a stand-alone hybrid photovoltaic-diesel-battery system (PDBS), which meets the energy requirements of a small village in southern Libya. The bat algorithm design strategy is applied to reduce the annual cost of the system, taking into consideration the controlled electricity restriction and the optimal numbers of PV panels, diesel generators, and batteries. Comparative tests are performed using MATLAB for the bat algorithm with the grey wolf search algorithm and particle swarm optimization, demonstrating that the bat algorithm determines the optimum size of the PDBS effectively at a lower expense. Results then indicate that, taking into account the reliability characteristics, this has a significant effect on optimum capacity, load supply, and cost.

关键词： annual system cost bat algorithm grey wolf optimization algorithm particle swarm optimization algorithm photovoltaic-diesel-battery system

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