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Estimation of thermophysical properties of lightweight mortars made of wood shavings and expanded polystyrene beads using a hybrid algorithm

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ENERGY AND BUILDINGS 2016年 118卷 133-141页

作者： Adili, A. Lachheb, M. Brayek, A. Guizani, A. Ben Nasrallah, S. Res & Technol Ctr Energy Thermal Proc Lab BP 95 Hammam Lif 2050 Tunisia Univ Monastir Lab Thermal & Energet Syst Studies Natl Engn Sch Monastir Monastir Tunisia

Over the past few decades, buildings energy consumption has risen seriously due to the growth of population and to the increase demand of comfort in indoor environment. Thus, the use of low thermal conductivities building materials and the development of powerful techniques to determine their thermal properties are receiving greater attention. To this end, lightweight mortar samples made with different percentage of polystyrene beads and wood shavings have been prepared and thermally characterized. To determine the thermal properties of these lightened mortar samples, an inverse problem based on a hybrid algorithm combining a genetic algorithm and the Nelder Mead simplex algorithm has been implemented. The experimental bench using the boxes method has been used in transitory state. The implemented hybrid algorithm is used to minimize an objective function which represents the gap between a measured temperature and a calculated one. The obtained results demonstrate that the thermal conductivities and the densities of the elaborated samples have been improved until 71% and until 36%, respectively. Thus, energy consumption in buildings is expected to decrease. In addition the replacement of a percentage of cement by wood shavings and polystyrene beads leads to a reduction of the total produced energy and in CO2 emission. Furthermore, results show that the implemented hybrid algorithm has the ability to correlated parameters and the efficiency to combine convergence speed and accuracy of results. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Lightweight mortar Thermophysical properties Estimation hybrid algorithm

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hybrid algorithm Based on Max And Min Ant System And Particle Swarm Optimization for Solving TSP Problem 33

Hybrid Algorithm Based on Max And Min Ant System And Particl...

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33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Qian, Hao Su, Tao Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781538672556

A hybrid algorithm which combines ant colony optimization algorithm and particle swarm optimization algorithm(ACO-PSO) is proposed to solve travelling salesman problem. Max-Min Ant System, whose parameters are optimized by PSO, is utilized to solve the problems. Massive of benchmark problems are utilized to test the performance of proposed algorithm.

关键词： particle swarm optimization max-min ant system traveling salesman problem hybrid algorithm

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Optimizing an emission trading scheme for local governments: A Stackelberg game model and hybrid algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2017年 193卷 172-182页

作者： Hong, Zhaofu Chu, Chengbin Zhang, Linda L. Yu, Yugang Lanzhou Univ Sch Management Lanzhou Gansu Peoples R China Univ Paris Saclay Cent Supelec Lab Genie Ind Chatenay Malabry France Catholic Univ Lille IESEG Sch Management LEM CNRS Lille France Univ Sci & Technol China Sch Management Hefei Anhui Peoples R China

This study investigates a policy-making problem for a local government to implement an emission trading scheme by considering the interactive production decisions of firms in its administrative region. The market-based allowance trading price formed freely among the firms in the region is investigated by taking into account regional environmental bearing capacities. Under the scheme, the government sets the emission reduction target of the region and allocates tradable initial allowances to firms, and firms plan their production according to their allowances on hand. A Stackelberg game model is formulated to analyze the decisions of the government and firms aiming to maximize the social welfare of the region and maximize the profit of each firm. In view of the non-concavity and discreteness of the decision model for the government, we propose a hybrid algorithm to solve the game model efficiently. This algorithm consists of a polynomial time dynamic programming, binary search, and genetic algorithm. Results reveal that i) the Stackelberg game model greatly supports local governments' policy-making on the market-driven emission allowance trading scheme, and that ii) the social welfare is a great metric for policy-making decisions on environmental regulations. The market-driven emission trading scheme is an effective mechanism for local governments to induce emission reduction through green technology adoption by firms. However, governments should set their emission reduction targets appropriately because a tight or easy regulation policy significantly affects the environmental and economic benefits as well as the social welfare.

关键词： Emission trading scheme Local government Social welfare Game theory hybrid algorithm

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A satellite-based hybrid algorithm to determine the Priestley-Taylor parameter for global terrestrial latent heat flux estimation across multiple biomes

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REMOTE SENSING OF ENVIRONMENT 2015年 165卷 216-233页

作者： Yao, Yunjun Liang, Shunlin Li, Xianglan Chen, Jiquan Wang, Kaicun Jia, Kun Cheng, Jie Jiang, Bo Fisher, Joshua B. Mu, Qiaozhen Gruenwald, Thomas Bernhofer, Christian Roupsard, Olivier Beijing Normal Univ Sch Geog State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China Beijing Normal Univ Coll Global Change & Earth Syst Sci Beijing 100875 Peoples R China Univ Maryland Dept Geog Sci College Pk MD 20742 USA Michigan State Univ CGCEO Geog E Lansing MI 48823 USA CALTECH Jet Prop Lab Pasadena CA 91109 USA Univ Montana Dept Ecosyst & Conservat Sci Numer Terradynam Simulat Grp Missoula MT 59812 USA Tech Univ Dresden Inst Hydrol & Meteorol Chair Meteorol D-01062 Dresden Germany CIRAD UMR Eco&Sols Ecol Fonct & Biogeochim Sols & Agroe F-34060 Montpellier France CATiE Trop Agr Ctr Res & Higher Educ Turrialba 7170 Costa Rica

Accurate estimation of the terrestrial latent heat flux (LE) for each plant functional type (PET) at high spatial and temporal scales remains a major challenge. We developed a satellite-based hybrid algorithm to determine the Priestley-Taylor (PT) parameter for estimating global terrestrial LE across multiple biomes. The hybrid algorithm combines a simple empirical equation with physically based ecophysiological constraints to obtain the sum of the weighted ecophysiological constraints (f(e)) from satellite-based normalized difference vegetation index (NDVI) and ground-measured air temperature (TO, relative humidity (RH), vapor pressure deficit (VPD) and LE for 2000 to 2009 provided by 240 globally distributed FLUXNET eddy covariance (ECOR) tower sites. Cross-validation analysis indicated that the optimization at a PFT level performed well with a RMSE of less than 0.15 and a R-2 between 0.61 and 0.88 for estimated monthly f(e). Cross-validation analysis also revealed good performance of the hybrid-based PT method in estimating seasonal variability with a RMSE of the monthly LE varying from 43 W/m(2) (for 6 deciduous needleleaf forest sites) to 18.1 W/m(2) (for 34 crop sites) and with a R-2 of more than 0.67. The algorithm's performance was also good for predicting among-site and inter-annual variability with a R-2 of more than 0.78 and 0.70, respectively. We implemented the global terrestrial LE estimation from 2003 to 2005 for a spatial resolution of 0.05 degrees by recalibrating the coefficients of the hybrid algorithm using Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product and ground-measured LE. This simple but accurate hybrid algorithm provides an alternative method for mapping global terrestrial LE, with a performance generally improved as compared to other satellite algorithms that are not calibrated with tower. The calibrated f(e) differs for differe

关键词： Global latent heat flux hybrid algorithm Plant functional type Priestley-Taylor parameter Ecophysiological constraints

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Improving Land Surface Temperature and Emissivity Retrieval From the Chinese Gaofen-5 Satellite Using a hybrid algorithm

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2018年第2期56卷 1080-1090页

作者： Ren, Huazhong Ye, Xin Liu, Rongyuan Dong, Jiaji Qin, Qiming Peking Univ Sch Earth & Space Sci Inst Remote & Geog Informat Syst Beijing 100871 Peoples R China Peking Univ Beijing Key Lab Spatial Informat Integrat & Its A Beijing 100871 Peoples R China China Aero Geophys Survey & Remote Sensing Ctr La Beijing 10083 Peoples R China

Land surface temperature (LST) is a key surface feature parameter. Temperature and emissivity separation (TES) and split-window (SW) algorithms are two typical LST estimation algorithms that have been applied to a variety of sensors to generate LST products. The TES algorithm can synchronously obtain LST and emissivity, but it requires high accuracy for atmospheric correction of the thermal infrared (TIR) data and does not perform well for surfaces with low spectral emissivity contrast. On the contrary, the SW algorithm can retrieve LST without detailed atmospheric data because the linear or nonlinear combination of brightness temperatures in the two adjacent TIR channels can reduce the atmospheric effect;however, this algorithm requires prior accurate pixel emissivity. Combining the two algorithms can improve the accuracy of LST estimation because the emissivity calculated from the TES algorithm can be used in the SW algorithm, and the LST from the SW algorithm can then be applied to the TES algorithm as an initial value to refine emissivity and LST. This paper investigates the aforementioned hybrid algorithm using Chinese Gaofen-5 satellite data, which will provide four-channel data for TIR at 40 m for synchronously retrieving LST and emissivity. The results showed that the hybrid algorithm was less sensitive to instrument noise and atmospheric data error, and can obtain LST and emissivity with an error less than 1 K and 0.015, respectively, which is better than those obtained with the single TES or SW algorithm. Finally, the hybrid algorithm was tested in simulated image and ground-measured data, and obtained accurate results.

关键词： Gaofen-5 (GF-5) satellite hybrid algorithm land surface temperature (LST) and emissivity split-window (SW) algorithm temperature and emissivity separation (TES) algorithm

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A hybrid algorithm of particle swarm optimization, metropolis criterion and RTS smoother for path planning of UAVs

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APPLIED SOFT COMPUTING 2018年 73卷 735-747页

作者： Wu, Xiande Bai, Wenbin Xie, Yaen Sun, Xinzhu Deng, Chengchen Cui, Hongtao Harbin Engn Univ Coll Aerosp & Civil Engn Harbin 150001 Heilongjiang Peoples R China Harbin Engn Univ Coll Automat Harbin 150001 Heilongjiang Peoples R China

Particle Swarm Optimization (PSO) algorithm is a simple approach with premature convergence and stagnation prone. The loss of efficiency and sub-optimal solution occur frequently while solving path planning problem with PSO. Therefore, a method is proposed to optimize parameters which affect performance of the PSO algorithm by using Rauch-Tung-Striebel (RTS) smoother. Moreover the Metropolis Criterion is applied as acceptance policy, which can prevent the PSO algorithm from falling into local minimums in the proposed method. The RTS smoother is applied to eliminate the irregular error of the PSO updated position, and to smooth the produced path. Experimental results show the proposed method which is based on the fusion of the PSO, Metropolis Criterion and RTS performs better than the existing methods in terms of solution's quality and robustness in the path planning problem for UAVs. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Path planning Particle Swarm Optimization Metropolis criterion Rauch-Tung-Striebel hybrid algorithm

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Non-convex Dynamic Economic Dispatch Using an Innovative hybrid algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2022年第2期17卷 863-902页

作者： Verma, Pooja Parouha, Raghav Prasad Indira Gandhi Natl Tribal Univ Dept Math Amarkantak 484887 MP India

An innovative hybrid algorithm (ihPSODE) is proposed in this paper to solve non-convex dynamic economic dispatch (DED) problem. It integrated with a novel particle swarm optimization (nPSO) and differential evolution (nDE). A new inertia weight, gradually varying acceleration coefficient and position update equation introduced in nPSO, to avoid stagnation. And in nDE a new mutation strategy and crossover rate are presented, to avoid premature convergence. Additionally, in ihPSODE after calculation identify best half member and discard rest of members from the population. In current population applied nPSO, to maintain exploration and exploitation. Then to enhance local search ability and improve convergence accuracy applied nDE. Hence, proposed ihPSODE has higher probability of avoiding local optima and it is likely to find global optima more quickly. Firstly, proposed ihPSODE as well as its anticipated integrating component nPSO and nDE are testified on IEEE CEC2006 constrained benchmark functions. Then performance of these proposed algorithms are evaluated with non-convex DED problem with valve-point effects using five different test systems (5-unit with losses, 10-unit without losses, 10-unit with losses, 15-unit with losses and 30-unit without losses). Several numerical and graphical experiments have been done to verify performances of proposed algorithms. Additionally, statistical and comparative analysis confirms superiority of proposed algorithms over many state-of-the-art algorithms.

关键词： Dynamic economic dispatch Constrained optimization Meta-heuristics algorithm hybrid algorithm

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Optimization of Electric Field Distributions in OVS With hybrid algorithm

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IEEE SENSORS JOURNAL 2019年第21期19卷 9748-9754页

作者： Huang, Yifan Xu, Qifeng Tan, Qiao Xie, Nan Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350002 Fujian Peoples R China Minjiang Univ Coll Comp & Control Engn Fuzhou 350018 Fujian Peoples R China

The optimization of electric field distributions in the optical voltage sensor (OVS) are mostly based on the finite element calculations and the exhaustive search method. It is unable to map complicated and nonlinear relationships between the electric field distribution and the sensing structure. Moreover, these methods turn out to be time-consuming, inefficient, and easy to fall into local traps. A hybrid optimization algorithm based on particle swarm optimization (PSO) and support vector machine (SVM) is developed to fix these issues in which the PSO-SVM establishes an electric field model for the electro-optic crystal and then the model is modulated by the PSO to achieve optimization. The medium attaching structure is taken as an example to test and verify the job, and the simulations and experiments show that the uniformity of electric field distributions is improved by 58% and the training time is saved by 87%.

关键词： Electric fields Support vector machines Optimization Finite element analysis Training Sensors Crystals Optical voltage sensor hybrid algorithm electric field model finite element calculation exhaustive search method

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A novel method to solve supplier selection problem: hybrid algorithm of genetic algorithm and ant colony optimization

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MATHEMATICS AND COMPUTERS IN SIMULATION 2019年 156卷 294-309页

作者： Luan, Jing Yao, Zhong Zhao, Futao Song, Xin Beijing Jiaotong Univ Sch Econ & Management 3 Shangyuancun Beijing 100044 Peoples R China Beihang Univ Sch Econ & Management 37 Xueyuan Rd Beijing 100191 Peoples R China Baidu Online Network Technol Beijing Co Ltd 10 Shangdi10 Rd Beijing 100094 Peoples R China

Nowadays, with the development of information technology and economic globalization, supplier selection problem gets more and more attraction. The recent literature shows huge interest in hybrid artificial intelligence (AI)-based models for solving supplier selection problem. In this paper, to solve a multi-criteria supplier selection problem, based on genetic algorithm (GA) and ant colony optimization (ACO), hybrid algorithm of GA and ACO is developed. It combines merits of GA with great global converging rate and ACO with parallelism and effective feedback. A numerical experiment was conducted to optimize parameters and to analyze and compare the performance of the original and hybrid algorithms. Results demonstrate the quality and efficiency improvement of new integrated algorithm, verifying its feasibility and effectiveness. It is an innovative pilot research to leverage hybrid AI-based algorithm of GA and ACO to settle the supplier selection problem, which not only makes a clear methodological contribution for optimization algorithm research, but also can be served as a decision tool and provide management reference for companies.(C) 2018 International Association for Mathematics and Computers in Simulation(IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Supplier selection hybrid algorithm Genetic algorithm Ant colony optimization

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An intelligent hybrid algorithm for 4- dimensional TSP

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2017年 5卷 39-50页

作者： Maity, Samir Roy, Arindam Maiti, Manoranjan Vidyasagar Univ Dept Comp Sci Medinipur 721102 MB India PK Coll Dept Comp Sci Purba Medinipur 721401 WB India Vidyasagar Univ Dept Appl Math Medinipur 721102 WB India

In this paper, a hybridized algorithmic approach to solve 4-dimensional Travelling Salesman Problem (4DTSP) where different paths with various number of conveyances are available to travel between two cities. The algorithm is a hybridization of rough set based ant colony optimization (rACO) with genetic algorithm (GA). The initial solutions are produced by ACO which act as a selection operation of GA and then GA is developed with a virgin extended rough set based selection (7-point scale), comparison crossover and generation dependent mutation. The said hybrid algorithm rough set based Ant Colony Optimization (rACO) with Genetic algorithm (rACO-GA) is tested against some test functions and efficiency of the proposed algorithm is established. The 4DTSPs are formulated with crisp and bi-fuzzy costs. In each environment, some statistical significant studies due to different time constraint values and other system parameters are presented. The models are illustrated with some numerical data. The proposed algorithm or its modified form can be easily adapted for applications in real life industrial information gathering. Proposed hybrid algorithm can be apply for industrial enterprise such as airline and supply chain, etc. (C) 2017 Elsevier Inc. All rights reserved.

关键词： hybrid algorithm ACO-GA Generation dependent mutation 4DTSP

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