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第九届中国卫星导航学术年会

作者： Jinrui Lin Guangxia Li Shiwei Tian Longlong Suo College of Communications Engineering Army Engineering University of PLA

Wireless location network combines data communication,contextual data collection and *** purpose of this network is to determine the positions of agents based on measurements between nodes and *** order to improve the positioning accuracy of wireless location network,the usual method is to increase the density of nodes,especially the density of anchor nodes,so as to optimize the topology of the ***,the wireless location network node power is limited,especially the wireless sensor *** is necessary to increase power consumption to meet the needs of a large number of ***,it is required that a certain level of power consumption should be set in the wireless location network,and even require further reduction in power consumption in daily *** the same time,we need to maintain a certain positioning accuracy,which requires the optimization of power allocation in the network,to make sure that power and positioning accuracy of the network can meet the needs of the actual *** this paper,an optimization of power allocation based on particle swarm optimization in wireless location network is proposed to optimize the power *** square position error bound is introduced as the evaluation standard of the network positioning *** the power optimization,the positioning accuracy of the network is improved.

关键词： wireless location network power optimization allocation square position error bound particle swarm optimization algorithm

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Chaotic grey wolf optimization algorithm for constrained optimization problems

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2018年第4期5卷 458-472页

作者： Kohli, Mehak Arora, Sankalap DAV Univ Jalandhar India

The Grey Wolf Optimizer (GWO) algorithm is a novel meta-heuristic, inspired from the social hunting behavior of grey wolves. This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed. Firstly, detailed studies are carried out on thirteen standard constrained benchmark problems with ten different chaotic maps to find out the most efficient one. Then, the chaotic GWO is compared with the traditional GWO and some other popular meta-heuristics viz. Firefly algorithm, Flower Pollination algorithm and particle swarm optimization algorithm. The performance of the CGWO algorithm is also validated using five constrained engineering design problems. The results showed that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimization problems. (C) 2017 Society for Computational Design and Engineering. Publishing Services by Elsevier.

关键词： Chaotic grey wolf optimization Firefly algorithm Flower pollination algorithm particle swarm optimization algorithm

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Non-contact voltage measurement of three-phase overhead transmission line based on electric field inverse calculation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第12期12卷 2952-2957页

作者： Xiao, Dongping Xie, Yutong Ma, Qichao Zheng, Qi Zhang, Zhanlong Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se 174 Shapingba Dist Chongqing Peoples R China

With the development of smart grid, the demand for voltage measurement along the overhead transmission lines is increasing. However, the installation of voltage transformers in the existing lines entails numerous difficulties. This study proposes a new non-contact measurement method by inversely calculating the voltages on AC overhead transmission lines based on the power-frequency electric field measurement data. To improve the calculation accuracy, the 3D model of transmission lines is built, and the relations between the 3D electric fields and voltages are presented. An improved algorithm that intermingles with the particle swarm algorithm and genetic algorithm is developed to ascertain optimal inverse solutions, meanwhile to improve the convergence speed and calculation stability. To further reduce the computational complexity, the constraint relations between the voltages and electric fields are derived, thereby the simplification from three decision variables to one is achieved. Then, some simulation cases with different measurement errors and voltage running states are conducted to show the good robustness and high accuracy of the proposed inversion method. Finally, a three-phase experimental system is built and the actual measured data are used to inversely calculate, which verifies the practicability of the proposed non-contact voltage measurement method.

关键词： voltage measurement power overhead lines inverse problems electric fields smart power grids potential transformers particle swarm optimisation computational complexity three-phase overhead transmission line electric field inverse calculation smart grid development voltage parameter measurement voltage transformers AC overhead transmission lines inverse calculation electric field measurement data 3D mathematical model particle swarm optimization algorithm genetic algorithm optimal inverse solutions convergence speed computational complexity optimisation algorithm multiple decision variables single decision variable selection strategy asymmetrical simulation three-phase symmetrical simulation measurement error 3D real-time electric field measurement devices noncontact voltage measurement method

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Shape optimization of U-shaped steel dampers subjected to cyclic loading using an efficient hybrid approach

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ENGINEERING STRUCTURES 2019年 197卷 108874-000页

作者： Khatibinia, Mohsen Jalaipour, Mehdi Gharehbaghi, Sadjad Univ Birjand Dept Civil Engn Birjand Iran Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran

U-shaped steel damper (USSD), as an energy dissipation device, has been recommended in the literature for using in the isolation systems. This type of damper is capable of appropriately dissipating the input energy which a structure imparts from an earthquake. The capability is provided through a large range of plastic deformations occurred in the USSD. This paper aimed at presenting a methodology structured in the framework of a shape optimization problem to enhance the seismic energy dissipation and deformation capability of the USSD under cyclic loading. To achieve this goal, the straight part, thickness and height of the USSD were considered as the design variables of the optimization problem and optimized through maximizing the ratio of energy dissipation through plastic deformation to the maximum equivalent plastic strain. In order to find the optimum shape of the USSD under cyclic loading, a hybrid approach consisted of two phases was applied. In the first phase, as an alternative for the time-consuming finite element analysis, a support vector machine (SVM) approach was trained, tested and used to predict the inelastic responses of the USSD. In the second phase, a modified particle swarm optimization (PSO) algorithm was adopted to find the optimum shape of the USSD subjected to two critical directions of cyclic loading. After finding the optimum shape of the USSD, the energy dissipation and deformation capability of the optimum shaped-USSD were assessed. Results demonstrate that the proposed shape optimization methodology renders an optimum-shaped USSD with significantly improved energy dissipation and deformation capability compared with those of available in the literature.

关键词： U-shaped steel damper Cyclic Loading Energy dissipation Shape optimization Support vector machine particle swarm optimization algorithm

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Optimized BP neural network for Dissolved Oxygen prediction

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IFAC-PapersOnLine 2018年第17期51卷 596-601页

作者： Wu, Jing Li, Zhenbo Zhu, Ling Li, Guangyao Niu, Bingshan Peng, Fang College of Information and Electrical Engineering China Agricultural University Beijing China Key Laboratory of Agricultural Information Acquisition Department of Agriculture Beijing China Agricultural Internet of Things Engineering Technology Research Center Beijing China

To solve the low accuracy, slow convergence and poor robustness problem of traditional neural network method for water quality forecasting, a new model of dissolved oxygen content prediction is proposed based on sliding window, particle swarm optimization (PSO) and BP neural network. dissolved oxygen content prediction model in water quality is established by handling dissolved oxygen content data through sliding window, and using particle swarm optimization algorithm to obtain BP neural network parameters. This model is applied to prediction analysis of dissolved oxygen with online monitoring of regional groundwater in Xilin Gol League on July 25, 2017 to December 5, 2017. Experimental results show that the model has better prediction effect, and mean square error (MSE), root mean square error(RMSE), mean absolute error(MAE) value of PSO algorithm to optimize the BP neural network based on sliding window are 0.437% and 6.611%, 0. 251% respectively, which are better than single forecasting method by using sliding window, PSO, and BP neural network individually. The Optimized BP neural network not only has fast convergence speed and high prediction accuracy, but also provides decision-making basis for water pollution control and water management. © 2018

关键词： Neural networks Backpropagation Biochemical oxygen demand Decision making Dissolution Dissolved oxygen Errors Forecasting Groundwater Mean square error particle swarm optimization (PSO) Predictive analytics Water management Water pollution Water pollution control Water quality BP neural networks Dissolved oxygen contents Fast convergence speed Neural network method particle swarm optimization algorithm Root mean square errors Sliding Window Water quality predictions

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Knowledge modeling for root cause analysis of complex systems based on dynamic fuzzy cognitive maps

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IFAC-PapersOnLine 2018年第21期51卷 13-18页

作者： Yue, Weichao Chen, Xiaofang Huang, Keke Zeng, Zhaohui Xie, Yongfang School of Information Science and Engineering Central South University Changsha410083 China

This paper proposes a knowledge model for root cause analysis (RCA) of complex systems based on fuzzy cognitive maps (FCMs) and particle swarm optimization algorithm (PSO). The process knowledge and experience of technicians can be captured by FCMs that are characterized by briefness of knowledge modeling and execution. The traditional methods for RCA based on FCMs are restricted to fixed incidence matrix. However, the individualized features are there existing in each system of the same kind, therefore fixed weights are unreasonable. PSO is introduced to detect the weight that can reveal the individualized features of systems among concepts of FCMs. And then a dynamic knowledge model for RCA is obtained, including predictive, diagnostic, and hybrid RCA. The three types RCA can be used for forecasting future event of output, identifying root cause and presenting measures of abnormal event. The effectiveness of proposed method is validated in aluminum reduction process, and the experiments results show the proposed method is effective and application potential. © 2018

关键词： Cognitive systems Fuzzy rules Large scale systems Models Ore reduction particle swarm optimization (PSO) Aluminum reduction Fuzzy cognitive map Fuzzy cognitive maps (FCMs) Incidence matrices Knowledge particle swarm optimization algorithm Process knowledge Root cause analysis

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Adaptive Balance Factor in particle swarm optimization 1

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6th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Sharma, Siddhi Kumari Sharma, R. S. Rajasthan Tech Univ Kota India

ISBN: (数字)9789811033223

ISBN: (纸本)9789811033223;9789811033216

particle swarm optimization (PSO) is a refined optimization method, that has drawn interest of researchers in different areas because of its simplicity and efficiency. In standard PSO, particles roam over the search area with the help of two accelerating parameters. The proposed algorithm is tested over 12 benchmark test functions and compared with basic PSO and two other algorithms known as Gravitational search algorithm (GSA) and Biogeography based optimization (BBO). The result reveals that ABF-PSO will be a competitive variant of PSO.

关键词： Meta-heuristic optimization techniques particle swarm optimization algorithm swarm intelligence Acceleration coefficients Nature inspired algorithm

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Hybrid QPSO-NNIA2 algorithm for Multi-Objective optimization Problem

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2019年第8期33卷

作者： Zhang, Lan Northwestern Polytech Univ Dept Appl Math Xian Shaanxi Peoples R China Xian Aeronaut Polytech Inst Basic Dept Xian Shaanxi Peoples R China

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

关键词： Multi-objective optimization problem Pareto optimal solution vicinity distance Quantum particle swarm optimization algorithm particle swarm optimization algorithm

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optimization of the Local Split-Window algorithm for FY-4A Land Surface Temperature Retrieval

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REMOTE SENSING 2019年第17期11卷 2016-2016页

作者： Wang, Lijuan Guo, Ni Wang, Wei Zuo, Hongchao Lanzhou Univ Coll Atmospher Sci Lanzhou 730000 Gansu Peoples R China China Meteorol Adm Key Lab Drought Climate Change & Disaster Reduct Key Lab Drought Climate Change & Disaster Reduct Inst Arid Meteorol Lanzhou 730020 Gansu Peoples R China

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

关键词： land surface temperature split-window algorithm FY-4A particle swarm optimization algorithm

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Image reconstruction for an electrical capacitance tomography system based on a least-squares support vector machine and a self-adaptive particle swarm optimization algorithm

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MEASUREMENT SCIENCE AND TECHNOLOGY 2011年第10期22卷

作者： Chen, Xia Hu, Hong-li Liu, Fei Gao, Xiang Xiang Xi An Jiao Tong Univ State Key Lab Power Equipment & Elect Insulat Xian 710049 Peoples R China

The task of image reconstruction for an electrical capacitance tomography (ECT) system is to determine the permittivity distribution and hence the phase distribution in a pipeline by measuring the electrical capacitances between sets of electrodes placed around its periphery. In view of the nonlinear relationship between the permittivity distribution and capacitances and the limited number of independent capacitance measurements, image reconstruction for ECT is a nonlinear and ill-posed inverse problem. To solve this problem, a new image reconstruction method for ECT based on a least-squares support vector machine (LS-SVM) combined with a self-adaptive particle swarm optimization (PSO) algorithm is presented. Regarded as a special small sample theory, the SVM avoids the issues appearing in artificial neural network methods such as difficult determination of a network structure, over-learning and under-learning. However, the SVM performs differently with different parameters. As a relatively new population-based evolutionary optimization technique, PSO is adopted to realize parameters' effective selection with the advantages of global optimization and rapid convergence. This paper builds up a 12-electrode ECT system and a pneumatic conveying platform to verify this image reconstruction algorithm. Experimental results indicate that the algorithm has good generalization ability and high-image reconstruction quality.

关键词： ECT image reconstruction support vector machine particle swarm optimization algorithm

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