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Energy Optimization in Multi-UAV-Assisted Edge Data Collection System

Computers, Materials & Continua 2021年第11期69卷 1671-1686页

作者： Bin Xu Lu Zhang Zipeng Xu Yichuan Liu Jinming Chai Sichong Qin Yanfei Sun Nanjing University of Posts and Telecommunications Nanjing210003China Nanjing Pharmaceutical Co. Ltd.Nanjing210012China Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjing211169China Central Washington University Ellensburg98926United States

In the IoT(Internet of Things)system,the introduction of UAV(Unmanned Aerial Vehicle)as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery ***,the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the *** this work,to deal with the problem,a deployment model of a mobile edge computing(MEC)system based on multi-UAV is *** goal of the model is to minimize the energy consumption of the system in the process of data transmission by optimizing the deployment of *** DEVIPSK(differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means)is proposed to solve the *** DEVIPSK,the population is initialized by K-Means to obtain better initial positions of ***,considering the limitation of the fixed mutation strategy in the traditional evolutionary algorithm,a mutation strategy pool is used to update the positions of *** experimental results show the superiority of the DEVIPSK and provide guidance for the deployment of UAVs in the field of edge data collection in the IoT system.

关键词： UAV mobile edge computing differential evolution algorithm K-Means edge data collection

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differential evolution algorithm Used for Parameters Reconstruction of Fiber Gratings

Differential Evolution Algorithm Used for Parameters Reconst...

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2nd International Conference on Advances in Materials and Manufacturing Processes (ICAMMP 2011)

作者： Wen, Xiaoyan Qian, Yu Fei, Nie Wuhan Univ Technol Natl Engn Lab Opt Fiber Sensor Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Econ Wuhan Peoples R China

ISBN: (纸本)9783037853283

differential evolution (DE) algorithm has been used to solve the reverse problem of fiber gratings, namely, reconstructing the structure parameters of uniform and chirped fiber gratings from the target reflection spectra. The reconstructed parameters and corresponding spectra are in good agreement with the target ones. Numerical examples demonstrate the highly efficiency and accuracy of DE algorithm in solving the reverse problem of fiber Gratings.

关键词： differential evolution algorithm Reverse problem Fiber gratings Parameter reconstruction

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A comparative study of different optimization algorithms for the optimum operation of the Mahabad dam reservoir

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RESULTS IN ENGINEERING 2024年 21卷

作者： Feizi, Hajar Sattari, Mohammad Taghi Apaydin, Halit Univ Tabriz Fac Agr Dept Water Engn Tabriz 51666 Iran Ankara Univ Fac Agr Dept Agr Engn TR-06110 Ankara Turkiye

In this study, the performance of the differential evolution (DE) algorithm, the Genetic algorithm (GA) and Teaching-Learning Based Optimization (TLBO) algorithm are compared for the optimum operation of the Mahabad dam reservoir. The Reservoir supplies agricultural, environmental, municipal and industrial water demands of the area. The desktop reserve model is used for estimating the minimum environmental demand. Six alternative agricultural water management scenarios are proposed and a sensitivity analysis is performed on the agricultural demands under different scenarios. The results of the study indicate that both DE and GA algorithms performed nearly equally;however, the DE algorithm reached its result more quickly with the reliability of 74.19 % compared to the results of the GA and TLBO. A 30 % decrease in agricultural demand in the fifth scenario with 87.56 % reliability provides the best results.

关键词： Optimal operation differential evolution algorithm Genetic algorithm Teaching-learning based optimization algorithm Environmental flow

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differential evolution algorithm for the Optimization of the Vehicle Routing Problem in Logistics

Differential Evolution Algorithm for the Optimization of the...

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8th International Conference on Computational Intelligence and Security (CIS)

作者： Xu, Huan Wen, Jiechang Guangdong Univ Technol Sch Appl Math Guangzhou Guangdong Peoples R China

ISBN: (纸本)9780769548968;9781467347259

The unidirectional logistics distribution vehicle routing problem with no time windows is considered. It contains the vehicle capacity restriction, the longest distance restriction and the full loaded vehicle. The solution must ensure the non-full loaded factor is the least and the total distance is the shortest. A multi-objective optimization mathematical model for the problem is established. And the differential evolution algorithm is proposed for this problem. The optimal solutions to the model are obtained by selecting the appropriate encoding method. The results of simulation show that the differential evolution algorithm is an effective way for solving the vehicle routing problem.

关键词： differential evolution algorithm logistics distribution multi-objective optimization vehicle routing problem

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An evolutionary generation method of deep neural network sets combined with Gaussian random field

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WIRELESS NETWORKS 2021年 1-10页

作者： Zhang, Chen Dai, Zifeng Liang, Xiaolong Xu, Guanghua Zhang, Changsheng Zhang, Bin Northeastern Univ Shenyang 110819 Peoples R China

As a research hotspot in the field of machine learning, ensemble learning improved the prediction accuracy of the final model by constructing and combining multiple basic models. In recent years, many experts and scholars are committed to combining deep networks with ensemble learning to improve the accuracy of neural network models in various scenarios and tasks. But not all neural networks are suitable for participating in the construction of ensemble models. Deep networks with ensemble learning require that the single neural network involved in the integration has high accuracy and great discrepancy with other networks. In the initial stage of deep networks with ensemble learning, the process of generating sets of candidate deep networks is first required. After studying an existing multiobjective deep belief networks ensemble (MODBNE) method, the Gaussian random field model is used as a pre-screening strategy in the process of generating the candidate deep network sets. Individuals with great potential for improvement are selected for fitness function evaluation so that a large number of neural network models with higher accuracy and the larger discrepancy between networks can be easily obtained, which effectively improves the quality of the solution and reduces the time consumed in training the neural networks.

关键词： Gaussian random field Deep neural network differential evolution algorithm Ensemble learning

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Research on IRP of Perishable Products Based on Mobile Data Sharing Environment

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2021年第2期15卷 5-+页

作者： Wang, Zelin Wei, Xiaoning Pan, Jiansheng Nantong Univ Sch Informat Sci & Technol Nantong Peoples R China

Inventory routing problem (IRP) has always been a hot issue. Due to its particularity, perishable products have high requirements for inventory and transportation. In order to reduce the losses of perishable goods and improve the storage efficiency of perishable goods, based on the general inventory path problem, this paper further has studied the IRP of perishable goods. In addition, in the process of product distribution and transportation, there are a lot of real-time product information generated dynamically. These real-time mobile data must be shared by the whole distribution network, which will also dynamically affect the efficiency of IRP research. On the basis of some assumptions, the mathematical model has been established with inventory and vehicle as constraints and the total cost of the system as the objective. In view of the particularity of perishable inventory path problem, this paper proposed an improved differential evolution algorithm (IDE) to improve the differential evolution algorithm from two aspects. Firstly, the population has been initialized by gridding and the greedy local optimization algorithm has been used to assist the differential evolution algorithm, with these measures to improve the convergence speed of the algorithm. Then, the accuracy of the algorithm is improved by the adaptive scaling factor, two evolution modes and changing the constraints of the problem. Then the improved algorithm has been used to solve the inventory path problem. The results of numerical experiments show that the algorithm is effective and feasible and can improve the accuracy and speed up the convergence of the algorithm.

关键词： Accuracy of the algorithm Convergence Speed of the algorithm differential evolution algorithm Inventory Routing Problem

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A novel structural damage detection method using a hybrid IDE-BP model

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KNOWLEDGE-BASED SYSTEMS 2023年第1期273卷

作者： Mei, Jiangtao Wu, Lei Chen, Erqi Xiao, Wensheng Zhong, Liang Guo, Jingjing Li, Wentao China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China China Univ Petr East China Natl Engn Res Ctr Offshore Geophys & Explorat Equi Qingdao 266580 Peoples R China Nanyang Technol Univ Maritime Inst NTU Sch Civil & Environm Engn Singapore 639798 Singapore Macao Polytech Univ Fac Appl Sci Macau 999078 Peoples R China CNOOC Safety & Technol Serv Co Ltd Tianjin 300450 Peoples R China

Recently, artificial intelligence has been widely applied in the field of structural damage detection. In this study, a back-propagation neural network (BPNN) hybrid with an improved differential evolution algorithm (IDE) is proposed to advance the development of structural damage detection. Generally, most engineering structures' dynamic characteristics can be directly obtained by modal analysis, and the corresponding vibration features can provide information on structural damage. Thus, based on the results of modal analysis, artificial intelligent methods such as BP neural network find their way to locate the damage and quantify the damage severity. However, the existing research shows that the original BP has insufficient accuracy in structural damage detection, especially cannot effectively deal with complex structural damage. In this study, an improved differential evolution algorithm is proposed, introducing a group sampling strategy and an adaptive mutation strategy. Then, the proposed IDE and BP neural networks are hybridized into IDE-BP to promote the neural network's data classification and regression ability. Subsequently, based on the IDE-BP model, a method for detecting the location and severity of multi-damage scenarios is established. The decision tree algorithm (DT) is utilized to identify the number of damaged elements. To verify the effectiveness of the proposed damage detection method, the IDE-BP model is applied to the damage detection for a steel pipe and a cantilever plate. To study the anti-noise ability and robustness of the IDE-BP model, the noise and incomplete modes are introduced. Compared with the original BP, SVM, KNN, PSO-BP, and DE-BP, the numerical results show that the damage detection method based on the IDE-BP model performs better for all single and multiple damage scenarios.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Damage detection Back -propagation neural network differential evolution algorithm Hybrid IDE-BP model

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Map-aided and UWB-based anchor placement method in indoor localization

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NEURAL COMPUTING & APPLICATIONS 2021年第18期33卷 11845-11859页

作者： Pan, Hao Qi, Xiaogang Liu, Meili Liu, Lifang Xidian Univ Sch Math & Stat Xian Peoples R China Xidian Univ Sch Comp Sci Xian Peoples R China

Nowadays, ultra wideband (UWB) has been popular in indoor positioning systems. Because of obstacles such as walls, doors, desks and pedestrians in the indoor area, UWB devices have to be conducted in a non-line-of-sight (NLOS) environment. The placement of nodes has significant influences on the performance of localization. In this paper, an anchor placement method for the target's trajectory based on genetic heuristic differential evolution algorithm is proposed to improve the UWB localization accuracy. To be specific, in an area with low anchor densities, a target does not have enough anchors for multilateration localization;thus, a coverage degree criterion is defined. Meanwhile, the Cramer-Rao lower bound (CRLB) is used as an evaluation metric for localization accuracy, and both of CRLB and coverage degree criterion are incorporated into the evaluation function of the differential evolution (DE) algorithm. Furthermore, instead of using the liner distance path loss model, the more practical UWB-based through-wall signal propagation model is adopted to reflect the influences of obstacles that are widespread in indoor environments. In addition, a software application is developed to visualize the indoor scenario, signal propagation status and anchor placement results. Finally, field experiments and simulations are performed, and a thorough case study confirms the effectiveness of the proposed method. The average localization error of the proposed placement scheme can be reduced by 28.2% and 12.5% compared to the random scheme and the default DE-based scheme.

关键词： Indoor localization Ultra wideband differential evolution algorithm Simulation software

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A novel self-adaption macroscopic fundamental diagram considering network heterogeneity

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2023年 613卷

作者： Yao, Wenbin Chen, Nuo Su, Hongyang Hu, Youwei Jin, Sheng Rong, Donglei Zhejiang Univ Inst Intelligent Transportat Syst Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China Zhejiang Univ Ctr Balance Architecture Hangzhou 310058 Peoples R China Pengcheng Lab Shenzhen 518052 Peoples R China

The spatial homogeneity is the basic assumption of macroscopic fundamental diagram (MFD) analysis, however, this assumption cannot be satisfied in practice. The current so-lutions to this problem include the network partition and using the function form of MFD that considers heterogeneity of network, while both solutions have their limitations. This paper proposes a self-adaption macroscopic fundamental diagram (SAMFD) considering network heterogeneity, which optimizes the effective road section length coefficient based on differential evolution algorithm. The effectiveness of the SAMFD is verified on the simulation data and real world datasets. The method proposed in this paper provides a new idea to solve the network heterogeneity problem when constructing MFD, and this method can be combined with the network partitioning method to better solve the heterogeneity problem. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Macroscopic fundamental diagram Heterogeneity Traffic state differential evolution algorithm Network partition Time stage division

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Comparison between differential evolution algorithms and response surface methodology in ethylene plant optimization based on an extended combined energy - exergy analysis

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ENERGY 2018年 164卷 1114-1134页

作者： Jahromi, Farid Sadeghian Beheshti, Masoud Rajabi, Razieh Fereydon Univ Isfahan Dept Chem Engn Esfahan Iran Univ Isfahan Proc Engn Inst Esfahan Iran Univ Isfahan Chem Engn Dept Esfahan Iran

This paper applies an extend energy - exergy analysis as a strategy for evaluating the performance of different types of distillation columns in ethylene production process by using industrial data. Regarding to the limitation and deficiency of energy exergy combination, a new method named exergy destruction level (EDL) and conceptual diagram based on equipment target value is proposed for process equipment with pressure and chemical composition changes. The effects of different operational parameters on the component separation are evaluated by sensitivity analysis. Eventually response surface methodology (RSM) and artificial intelligence (DE) method are developed for optimization of the chemical plant. Comparing obtained results from ethylene plant optimization by using RSM and DE, it was found that annual profit percentage with DE method is 61.6% more than RSM method. Also, results of optimization showed that the most effective operating parameters consist of feed stream temperature, boil-up ratio, reflux ratio, column pressure and feed stage. It was observed that utilities and refrigeration cycle consumption work have been declined significantly by using DE optimizer and EDL analysis (12.6% and 11.6%) compared to RSM optimizer and exergy analysis (11.9% and 4.8%). It is obvious that, EDL analysis and DE method expedite process optimization and provide more precise analysis than conventional energy-exergy-analysis. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Distillation column Exergy destruction level Target value Response surface methodology differential evolution algorithm

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