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Prediction of Novel High-Pressure Structures of Magnesium Niobium Dihydride

ACS APPLIED MATERIALS & INTERFACES 2017年第31期9卷 26169-26176页

作者： Zhang, Chuanzhao Sun, Guoliang Wang, Jingjing Lu, Cheng Jin, Yuanyuan Kuang, Xiaoyu Hermann, Andreas Sichuan Univ Inst Atom & Mol Phys Chengdu 610065 Sichuan Peoples R China Nanyang Normal Univ Dept Phys Nanyang 473061 Peoples R China Yangtze Univ Dept Phys & Optoelect Engn Jingzhou 434023 Peoples R China Univ Nevada Dept Phys Las Vegas NV 89154 USA Univ Nevada High Pressure Sci & Engn Ctr Las Vegas NV 89154 USA Univ Edinburgh Sch Phys & Astron Ctr Sci Extreme Condit Edinburgh EH9 3JZ Midlothian Scotland Univ Edinburgh Sch Phys & Astron SUPA Edinburgh EH9 3JZ Midlothian Scotland

On the basis of a combination of the particle-swarm optimization technique and density functional theory (DFT), we explore the crystal structures of MgH2, NbH2, and MgNbH2 under high pressure. The enthalpy-pressure (H-P) diagrams indicate that the structural transition sequence of MgH2 isay alpha -> gamma -> delta -> epsilon -> zeta and that NbH2 transforms from the Fm (3) over barm phase to the Pnma phase at 47.80 GPa. However, MgNbH2 is unstable when the pressure is too low or too high. Two novel MgNbH2 structures, the hexagonal P (6) over bar m2 phase and the orthorhombic Cmcm phase, are discovered, which are stable in the pressure ranges of 13.24-128.27 GPa and 128.27-186.77 GPa, respectively. The P (6) over bar m2 phase of MgNbH2 consists of alternate layers of polymetric NbH6 and MgH6 triangular prisms, while the Cmcm phase contains distorted MgH6 trigonal prisms. The calculated elastic constants and phonon dispersions confirm that both phases are mechanically and dynamically stable. The analyses of density of states (DOS), electron localization function (ELF), and Bader charge demonstrate that a combination of ionic and metallic bonds exist in both P (6) over bar m2 and Cmcm phases. We hope the newly predicted magnesium niobium dihydrides with desirable electronic properties will promote future experimental and theoretical studies on mixed main group-transition metal hydrides.

关键词： magnesium niobium dihydride particle swarm optimization algorithm density functional theory structural transition electronic structures

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A Novel Flood Forecasting Method Based on Initial State Variable Correction

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WATER 2018年第1期10卷

作者： Li, Kuang Kan, Guangyuan Ding, Liuqian Dong, Qianjin Liu, Kexin Liang, Lili Minist Water Resources China Inst Water Resources & Hydropower Res Res Ctr Flood & Drought Disaster Reduct State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China Beijing IWHR Technol Co Ltd Beijing 100038 Peoples R China Tsinghua Univ Dept Hydraul Engn State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China

The influence of initial state variables on flood forecasting accuracy by using conceptual hydrological models is analyzed in this paper and a novel flood forecasting method based on correction of initial state variables is proposed. The new method is abbreviated as ISVC (Initial State Variable Correction). The ISVC takes the residual between the measured and forecasted flows during the initial period of the flood event as the objective function, and it uses a particle swarm optimization algorithm to correct the initial state variables, which are then used to drive the flood forecasting model. The historical flood events of 11 watersheds in south China are forecasted and verified, and important issues concerning the ISVC application are then discussed. The study results show that the ISVC is effective and applicable in flood forecasting tasks. It can significantly improve the flood forecasting accuracy in most cases.

关键词： flood forecasting forecast accuracy conceptual hydrological model initial state variable particle swarm optimization algorithm

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Simultaneous allocation of electric vehicles' parking lots and distributed renewable resources in smart power distribution networks

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SUSTAINABLE CITIES AND SOCIETY 2017年 28卷 332-342页

作者： Amini, M. Hadi Moghaddam, Mohsen Parsa Karabasoglu, Orkun Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Sun Yat Sen Univ Carnegie Mellon Univ Joint Inst Guangzhou 510006 Guangdong Peoples R China SYSU Sch Elect & Informat Technol Guangzhou Guangdong Peoples R China Tarbiat Modares Univ Fac Elect & Comp Engn Tehran Iran

Electric vehicles (EVs) and distributed renewable resources (DRRs) are introduced to achieve three of the most pivotal objectives of this century: using environmentally-friendly energy resources, reliable supply of the load demand, and sustainable development of power systems. To achieve the aforementioned goals, simultaneous utilization of DRRs and EVs should be implemented in a scheduled manner. In this paper, we propose a two-stage approach for allocation of EV parking lots and DRRs in power distribution network. Our method considers both the economical benefits of parking lot investor and the technical constraints of distribution network operator. First, the parking lot investor offers the candidate buses for installing the parking lot to the distribution network operator based on economic objectives. Then, the distribution network decision-making is obtained to reduce loss of system. The proposed framework not only improves the distribution network loss, but also ameliorates the availability of the parking lot from the economical point of view. In order to solve the formulated optimization problem, we utilize two optimization techniques. Genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used for the distribution network loss minimization purpose. Besides, we model the EV parking lot by expanding single EV probabilistic model. The performance of the proposed method is evaluated by allocating DRRs and EV parking lots simultaneously on the IEEE standard distribution test system. This system is bus 2 of Roy Billinton Test System (RBTS). (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Distributed renewable resource Electric vehicle Power distribution network Parking lot investor Genetic algorithm particle swarm optimization algorithm Multi-objective optimization mart distribution network

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A kernel extreme learning machine algorithm based on improved particle swam optimization

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MEMETIC COMPUTING 2017年第2期9卷 121-128页

作者： Lu, Huijuan Du, Bangjun Liu, Jinyong Xia, Haixia Yeap, Wai K. China Jiliang Univ Coll Informat Engn Hangzhou 310018 Zhejiang Peoples R China Ctrip Travel Network Shanghai 200335 Peoples R China Zhejiang Sci Tech Univ Coll Informat Hangzhou 310018 Zhejiang Peoples R China Auckland Univ Technol Ctr Artificial Intelligence Res Auckland New Zealand

Kernel extreme learning machine (KELM) increases the robustness of extreme learning machine (ELM) by turning linearly non-separable data in a low dimensional space into a linearly separable one. However, the internal power parameters of ELM are initialized at random, causing the algorithm to be unstable. In this paper, we use the active operators particle swam optimization algorithm (APSO) to obtain an optimal set of initial parameters for KELM, thus creating an optimal KELM classifier named as APSO-KELM. Experiments on standard genetic datasets show that APSO-KELM has higher classification accuracy when being compared to the existing ELM, KELM, and these algorithms combining PSO/APSO with ELM/KELM, such as PSO-KELM, APSO-ELM, PSO-ELM, etc. Moreover, APSO-KELM has good stability and convergence, and is shown to be a reliable and effective classification algorithm.

关键词： Kernel extreme learning machine Active operator particle swarm optimization algorithm Gene expression data classification

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Parameter estimation of MIMO bilinear systems using a Levy shuffled frog leaping algorithm

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SOFT COMPUTING 2017年第14期21卷 3849-3858页

作者： Kawaria, Narendra Patidar, Rohan George, Nithin V. Indian Inst Technol Gandhinagar Gandhinagar India

Parameter identification of bilinear systems has been considered as an evolutionary computing algorithm-based optimization problem in this paper. A new Levy shuffled frog leaping algorithm (LSFLA), which is an improved version of the conventional shuffled frog leaping algorithm (SFLA), has been designed and has been applied for this parameter identification task. LSFLA offers enhanced local search behaviour in comparison with other traditional evolutionary computing algorithms. The ability of the new algorithm in accurately modeling parameters in single input single output (SISO) as well as multiple input multiple output (MIMO) has been checked using an extensive simulation study. The parameter estimation efficiency of the new scheme has been compared with that obtained using other popular evolutionary computing algorithms and the simulation study reveals the enhanced parameter identification ability of the proposed LSFLA.

关键词： Bilinear system System identification particle swarm optimization algorithm Cuckoo search algorithm Shuffled frog leaping algorithm

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Forward and reverse logistics network and route planning under the environment of low-carbon emissions: A case study of Shanghai fresh food E-commerce enterprises

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 106卷 351-360页

作者： Guo, Jianquan Wang, Xinyue Fan, Siyuan Gen, Mitsuo Univ Shanghai Sci & Technol Shanghai Peoples R China Univ Shanghai Sci & Technol Sch Business Shanghai Peoples R China Tokyo Univ Sci Japan Fuzzy Log Syst Inst Tokyo Japan

In view of the rapid development of low-carbon economy, the increasing distribution demand and returned demand which caused by the short shelf life and spoilage of fresh food, network and route planning model of a two-stage forward/reverse logistics is firstly proposed for fresh food e-commerce enterprises under the environment of low-carbon emissions (The objective in the first stage is to minimize the overall cost of the system, and the minimum overall cost of the circulation-type distribution vehicles routing is considered in the second stage). And the validity of the model is verified by adopting genetic algorithm (GA) and particle swarm optimization (PSO) algorithm with the study of the fresh food e-commerce enterprises of Shanghai. Furthermore, a good reference can be provided to build the forward and reverse logistics network and optimal route planning model of fresh food e-commerce enterprises and reduce the carbon emissions during its operation process. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Location and route planning Fresh food e-commerce enterprises Low-carbon Logistics network Genetic algorithm particle swarm optimization algorithm

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An improved DV-Hop localization algorithm based on evolutionary algorithms

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TELECOMMUNICATION SYSTEMS 2017年第4期64卷 639-647页

作者： Mehrabi, Mahshid Taheri, Hassan Taghdiri, Pooria Amirkabir Univ Technol Dept Elect Engn Switching & Network Lab Tehran Iran Amirkabir Univ Technol Photon Engn Grp Tehran Iran

Localization is one of the most important issues in wireless sensor networks and designing accurate localization algorithms is a common challenge in recent researches. Among all localization algorithms, DV-Hop attracts more attention due to its simplicity;so, we use it as a basis for our localization algorithm in order to improve accuracy. The various evolutionary algorithms such as Genetic, Shuffled Frog Leaping and particle swarm optimization are employed in different phases of the main DV-Hop localization algorithm. Simulation results prove that our proposed method decreases the localization error efficiently without additional hardware.

关键词： Localization Wireless sensor networks DV-Hop Genetic algorithm Shuffled frog leaping algorithm particle swarm optimization algorithm

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Lower bound estimation of the maximum allowable initial error and its numerical calculation

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Atmospheric and Oceanic Science Letters 2018年第5期11卷 438-443页

作者： CAO Yi-Xing ZHENG Qin YAN Jun Graduate School PLA Army Engineering University Department of Basic PLA Army Engineering University

In the numerical prediction of weather or climate events,the uncertainty of the initial values and/or prediction models can bring the forecast result’s *** to the absence of true states,studies on this problem mainly focus on the three subproblems of predictability,i.e.,the lower bound of the maximum predictable time,the upper bound of the prediction error,and the lower bound of the maximum allowable initial *** at the problem of the lower bound estimation of the maximum allowable initial error,this study first illustrates the shortcoming of the existing estimation,and then presents a new estimation based on the initial observation precision and proves it ***,the new lower bound estimations of both the two-dimensional ikeda model and lorenz96 model are obtained by using the cnop(conditional nonlinear optimal perturbation)method and a pso(particle swarm optimization)algorithm,and the estimated precisions are also ***,the estimations yielded by the existing and new formulas are compared;the results show that the estimations produced by the existing formula are often incorrect.

关键词： Predictability problem maximum allowable initial error particle swarm optimization algorithm Conditional Nonlinear Optimal Perturbation(CNOP)

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A Day-Ahead Energy Optimal Configuration of Islanded Microgrid

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电脑学刊 2018年第2期29卷 75-85页

作者： Xiaoxuan Ma Yiping Huang Xiaohui Wang

Microgrid can effectively utilize renewable resources and reduce fossil fuel exploitation and environmental pollution. By setting the microgrid of renewable energy sources as the research object, this paper proposes a day-ahead energy optimization method under island mode. Overall management is realized for electric energy generated by multiple renewable energy sources. Moreover, by combining with energy storage devices and power generation equipment, economic configuration of operation optimization is completed for microgrid system on the basis of particle swarm optimization algorithm under MATLAB environment.

关键词： distributed power economic optimization energy management microgrid particle swarm optimization algorithm

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Research on gateway deployment of WMN based on maximum coupling subgraph and PSO algorithm

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SOFT COMPUTING 2017年第4期21卷 923-933页

作者： Li, Yang Huang, Shuqiang Fan, Rensheng Zhang, Zhen Zhou, Yuyu Jinan Univ Coll Informat Sci & Technol Guangzhou Guangdong Peoples R China Jinan Univ Network & Educ Technol Ctr Guangzhou Guangdong Peoples R China

The maximum distance from the access points to the nearest gateways determines the network time delay, and has an important effect on network performance in wireless mesh networks. Motivated by the gateway deployment problem, this study is focused on optimizing the gateway deployment by minimizing the maximum distance. This is done by first improving upon theorems so that the plane can be divided into several intersecting regions;vertices locate in the same region are equivalent and can connect the same access points;the coordinates of the regions can also be determined. Then, maximum coupling subgraph is used in order to recognize the maximum intersecting regions;meanwhile, the coordinates are calculated by representative points. Lastly, an RPSO algorithm is designed in which representative points are taken as the initial particles to search the optimal gateway deployment. The simulation results demonstrate that the optimal gateway deployment, as determined by the RPSO algorithm process, has a smaller coverage radius, a more stable result and a faster convergence rate as compared to other algorithms.

关键词： Wireless mesh network Gateway deployment Geometric K-center problem particle swarm optimization algorithm Maximum coupling subgraph Representative point

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