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11th International Conference on Intelligent Computation Technology and Automation (ICICTA)

作者： Huang Bin Bao Li-yong Ding Hong-wei Zhao Lei Yunnan Univ Sch Informat Sci & Engn Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9781538683088

Aimed at the problem of the calculation value tends to distribute at both ends in the traditional chaos optimization algorithm, the paper proposes a method to improve the efficiency of the optimization problem by improving the Chebyshev mapping distribution characteristics through a homogenizing regulator. The homogenization regulator based on the probability density function in this method can improve the ergodic properties of Chebyshev mapping. This paper lists two classical examples to test the optimization algorithm based on homogenization regulator, and compares it with the optimization results based on traditional Tent mapping and Logistic mapping. The test results show that the algorithm significantly reduces the solution step length on the basis of maintaining accuracy. Therefore, this algorithm is feasible and effective in solving optimization problems.

关键词： optimization algorithm Chebyshev mapping Homogenizing regulator optimization algorithm Step length

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Building energy model calibration using automated optimization-based algorithm

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ENERGY AND BUILDINGS 2019年 198卷 106-114页

作者： Asadi, Somayeh Mostavi, Ehsan Boussaa, Djamel Indaganti, Madhavi Penn State Univ Dept Architectural Engn Engn 104 Unit A University Pk PA 16802 USA Qatar Univ Dept Architecture & Urban Planning POB 2713 Doha Qatar Qatar Univ Architecture & Urban Planning Dept Coll Engn Doha Qatar

Multiple numbers of Building Energy Simulation (BES) programs have been improved and implemented during the last decades. BES models play a crucial role in understanding building energy demands and accelerating the malfunction diagnosis. However, due to the very high number of interacting parameters, most of the developed energy simulation programs do not accurately predict building energy performance under a known condition. Even the energy models which are developed with the very precise assignment of parameters, there is always significant discrepancies between the simulation results and the real-time data measurements. Current study develops an optimization-based framework to calibrate the whole building energy model. The optimization algorithm attempts to set the identified parameters to minimize the error between the simulation results and the real-time measurements. Due to the high number of parameters, the developed optimization algorithm utilizes a Harmony Search algorithm as its search engine coupled with the energy simulation model to accelerate the calibration process. Moreover, to illustrate the efficiency of using the developed framework, a case study of the office building is modeled and calibrated and the statistical analysis was conducted to assess the accuracy of the results. The results of the calibration process show the reliability of the framework. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Building energy performance Energy model calibration Occupants' behavior optimization algorithm Harmony search optimization

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A review on the recent application of Jaya optimization algorithm 1

A review on the recent application of Jaya optimization algo...

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1st Annual International Conference on Information and Sciences (AiCIS)

作者： Alsajri, Mohammad Ismail, Mohd Arfian Salamn, Saba Abdul-baqi Univ Malaysia Pahang Fac Comp Syst & Software Engn Pahang Malaysia Al Iraqia Univ Dept Comp Sci Coll Educ Baghdad Iraq

ISBN: (纸本)9781538691885

Jaya is a new metaheuristic that in recent years, has been applied to numerous intractable optimization problems. The important difference between Jaya and other optimization algorithms is that Jaya does not require the tuning of its parameters (a process needed in the other algorithms to escape unwanted convergence). Another different is the efficiency of Jaya in always choosing the best solution. In this paper, a review of the recent application of Jaya algorithm in the field of optimization problem was reviewed.

关键词： Jaya optimization algorithm metaheuristic algorithm application of Jaya

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Nodes Deployment optimization algorithm Based on Energy Consumption of Underwater Wireless Sensor Networks 1

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14th International Conference on Advanced Data Mining and Applications (ADMA)

作者： Cui, Min Mei, Fengtong Li, Qiangyi Li, Qiangnan Zhengzhou Univ Ind Technol Zhengzhou 451150 Henan Peoples R China Henan Univ Sci & Technol Luoyang 471023 Henan Peoples R China

ISBN: (数字)9783030050900

ISBN: (纸本)9783030050900;9783030050894

Underwater wireless sensor networks nodes deployment optimization problem is studied and underwater wireless sensor nodes deployment determines its capability and lifetime. If no underwater wireless sensor node is available in the monitoring area of underwater wireless sensor networks due to used up energy or any other reasons, the monitoring area where is not detected by any underwater wireless sensor node forms coverage holes. In order to improve the coverage of the underwater wireless sensor networks and prolong the lifetime of the underwater wireless sensor networks, based on the perception model, establish nodes detection model, combining with the data fusion. Because the underwater wireless sensor networks nodes coverage holes appear when the initial randomly deployment, a nodes deployment algorithm based on perception model of underwater wireless sensor networks is designed in this article. The simulation results show that this algorithm can effectively reduce the number of deployment underwater wireless sensor networks nodes, improve the efficiency of underwater wireless sensor networks coverage, reduce the underwater wireless sensor networks nodes energy consumption, prolong the lifetime of the underwater wireless sensor networks.

关键词： Nodes deployment optimization algorithm Underwater wireless sensor networks

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optimization of Spectrum Auction in 5th Generation Mobile Networks by the optimization algorithm The simulated annealing algorithm and genetic algorithm for the spectrum below 6 GHz 18

Optimization of Spectrum Auction in 5th Generation Mobile Ne...

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18th IEEE International Conference on Communication Technology (IEEE ICCT)

作者： Chiang, Johannes K. Chiang, Yi-Fang Yen, Eric H. W. Natl Chengchi Univ Dept Management Informat Syst 64Sect 2Chi Nan Rd Taipei 116 Taiwan

ISBN: (纸本)9781538676356

It is now important to prepare spectrum auction for the 5th Generation Mobile Networks that will start operation in 2020. This paper proposes a novel approach for the optimization for the 5G spectrum. Our ultimate target is to determine the various variables of 5G to optimize the revenue of the spectrum auction by the optimization algorithms. For the optimization, we develop advanced Simulated Annealing algorithm and Genetic algorithm. We use the costs and benefits of telecommunication companies as a constraint to achieve the goal of revenue maximization. Finally, this paper shows the optimal results by chart. This study is the first of its kind thus far.

关键词： 5th Generation Mobile Networks IMT-2020 optimization algorithm Simulated Annealing algorithm Genetic algorithm

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Design optimization of moderately thick hexagonal honeycomb sandwich plate with modified multi-objective particle swarm optimization by genetic algorithm (MOPSOGA)

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COMPOSITE STRUCTURES 2020年 252卷 112626-112626页

作者： Namvar, A. R. Vosoughi, A. R. Shiraz Univ Sch Engn Dept Civil & Environm Engn Shiraz Iran

Design optimization of moderately thick hexagonal honeycomb sandwich plate has been investigated via employing an improved multi-objective particle swarm optimization with genetic algorithm (MOPSOGA). Based on the first-order shear deformation theory (FSDT), governing equations of the plate are obtained. The equations are solved analytically. Total weight and maximum deflection of the plate under static gravity loads are considered to be objective functions of the problem. Core height, faces thickness, cell walls thickness, vertical and inclined cell wall length and the angle between inclined cell wall and horizontal line are set to be design variables of the problem. The geometrical and failure constrains are chosen to have desirable performance and stability of the sandwich plate. In the used multi-objective optimization technique, the optimum velocity parameter, inertia weight and acceleration coefficients for next iteration of the MOPSO are obtained by employing the genetic algorithm via minimizing generational distance between the sets of dominated and non-dominated particles in the previous iteration. Efficiency and accuracy of the proposed solution procedure are demonstrated and effects of different parameters on design optimization of the plate are studied. Also, TOPSIS multi-criteria decision-making method has been selected to report appreciate results from the Pareto-front curve of the MOPSOGA.

关键词： Hexagonal honeycomb sandwich plate Design optimization of cellular material First-order shear deformation theory Modified multi-objective particle swarm optimization algorithm Genetic algorithm

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Corrigendum to “A novel efficient optimization algorithm for parameter estimation of building thermal dynamic models” [Build. Environ. 153 (2019) 233–240]

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Building and Environment 2020年 176卷

作者： Jiangyu Wang Huanxin Chen Yue Yuan Yao Huang Department of Refrigeration and Cryogenic Engineering School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan PR China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan PR China

Parameter estimation applied to gray-box modeling approaches is of great importance for advanced building control and demand response. Stochastic optimization algorithms based on the swarm intelligence, e.g. generic algorithm, have been applied widely. However, these algorithms are time-consuming to accomplish optimization due to the large populations employed in random searching. To overcome this problem, this paper presents, for the first time, a novel efficient optimization algorithm so-called Beetle Swarm Antennae Search (BSAS) to estimate parameters of a resistance-capacitance(RC) model using simulated data collected from EnergyPlus. Furthermore, this paper also investigates the application of BSAS and four other widely used optimization algorithms including genetic algorithm(GA), particle swarm optimization(PSO), differential evolution(DE) and beetle antennae search(BAS) on parameter estimation. A case study is conducted on a single-room building simulated by EnergyPlus. BSAS and four other algorithms are employed to estimate the parameters of a 4R3C model for this tested building. The results demonstrate that the BSAS can achieve good performance on parameter estimation with sufficient accuracy and less computational cost by comparison with other algorithms. More specifically, the computational cost of the proposed algorithm is only about one third of the GA and a quarter of the DE while the mean fitness index is very close.

关键词： Gray-box thermal model System identification optimization algorithm Building simulation

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Coverage enhancing of 3D underwater sensor networks based on improved fruit fly optimization algorithm

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SOFT COMPUTING 2017年第20期21卷 6019-6029页

作者： Zhang, Ying Wang, Mingxing Liang, Jixing Zhang, Haiyang Chen, Wei Jiang, Shengming Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Tennessee State Univ Dept Comp Sci Nashville TN 37209 USA

The coverage rate of the underwater sensor networks directly influences on the monitoring efficiency in underwater environment, and it can be effectively improved by adjusting the positions of the mobile nodes reasonably for a 3D underwater sensor network which consists of mobile nodes as underwater robots like Autonomous Underwater Vehicles. An optimal deployment method can quickly set up a reasonable topology of the sensor networks and achieve a higher efficiency for detecting or investigating. An optimal algorithm of coverage enhancing for 3D Underwater sensor networks based on improved Fruit Fly optimization algorithm (UFOA) is proposed in this paper. This method realizes the global optimal coverage based on foraging behavior of fruit flies, and it has the features of higher speed of convergence, few parameters to set up and stronger global searching ability. Simulation result indicates that the proposed UFOA method can significantly improve the effective coverage rate of the sensor networks compared with some widely studied PSO and IPSO algorithms.

关键词： 3D underwater sensor networks Deployment optimization algorithm Coverage rate Fruit fly

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Studying the varied shapes of gold clusters by an elegant optimization algorithm that hybridizes the density functional tight-binding theory and the density functional theory

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COMPUTER PHYSICS COMMUNICATIONS 2017年 220卷 143-149页

作者： Yen, Tsung-Wen Lim, Thong-Leng Yoon, Tiem-Leong Lai, S. K. Natl Cent Univ Dept Phys Complex Liquids Lab Chungli 32001 Taiwan Multimedia Univ Fac Engn & Technol Melaka CampusJalan Ayer Keroh Lama Bukit Beruang 75450 Melaka Malaysia Univ Sains Malaysia Sch Phys Usm 11800 Pulau Penang Malaysia

We combined a new parametrized density functional tight-binding (DFTB) theory (Fihey et al. 2015) with an unbiased modified basin hopping (MBH) optimization algorithm (Yen and Lai 2015) and applied it to calculate the lowest energy structures of Au clusters. From the calculated topologies and their conformational changes, we find that this DFTB/MBH method is a necessary procedure for a systematic study of the structural development of Au clusters but is somewhat insufficient for a quantitative study. As a result, we propose an extended hybridized algorithm. This improved algorithm proceeds in two steps. In the first step, the DFTB theory is employed to calculate the total energy of the cluster and this step (through running DFTB/MBH optimization for given Monte-Carlo steps) is meant to efficiently bring the Au cluster near to the region of the lowest energy minimum since the cluster as a whole has explicitly considered the interactions of valence electrons with ions, albeit semi-quantitatively. Then, in the second succeeding step, the energy-minimum searching process will continue with a skilledly replacement of the energy function calculated by the DFTB theory in the first step by one calculated in the full density functional theory (DFT). In these subsequent calculations, we couple the DFT energy also with the MBH strategy and proceed with the DFT/MBH optimization until the lowest energy value is found. We checked that this extended hybridized algorithm successfully predicts the twisted pyramidal structure for the Au-40 cluster and correctly confirms also the linear shape of C-8 which our previous DFTB/MBH method failed to do so. Perhaps more remarkable is the topological growth of Au-n: it changes from a planar (n = 3-11) -> an oblate-like cage (n = 12-15) -> a hollow-shape cage (n = 16-18) and finally a pyramidal-like cage (n = 19, 20). These varied forms of the clusters shapes are consistent with those reported in the literature. (C) 2017 Elsevier B.V. All righ

关键词： Metallic cluster Density functional theory optimization algorithm Topological transition

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Shape and size determination of plasmonic nano particles using particle swarm optimization algorithm based absorption coefficient

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OPTIK 2017年 130卷 44-49页

作者： Goodarzi, M. Fakharzadeh, A. J. Shiraz Univ Technol Dept Appl Math Shiraz Iran

Plasmonic nano particles can be greatly enhancing the optical absorption coefficient spectrum. Since optical properties of these particles strongly depends on the size and shape of the nano particles, in this paper particle swarm optimization algorithm (PSO) is used to optimize the nano particles shape and size in order to amplification of the absorption coefficient. In PSO a swarm consists of a matrix with decimal numbers, controls the particles shape and size in order to increase the absorption coefficient in the visible part of light spectrum. It is found that significant plasmonic enhancement of above 100000 can be obtained by optimize selection of particle shapes and sizes. (C) 2016 Elsevier GmbH. All rights reserved.

关键词： Plasmonic nano particle optimization algorithm PSO algorithm

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