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...
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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
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 pa...
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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.
Here, we suggest the possibility of optical circuit design approach by employing the binary optimization of plasmonic nano rods. The proposed mechanism is based on combination of binary particle swarm optimization (BP...
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Here, we suggest the possibility of optical circuit design approach by employing the binary optimization of plasmonic nano rods. The proposed mechanism is based on combination of binary particle swarm optimization (BPSO) algorithm and discrete dipole approximation method. BPSO, a group of birds including a matrix with binary entries responsible for controlling nano rods in the array, shows the presence with symbol of ('1') and the absence with ('0'). The current research represents a nanoscale and compact four channels plasmonic Demultiplexer as optical circuit. It includes eight coherent perfect absorption (CPA)-type filters. The operation principle is based on the absorbable formation of a conductive path in the dielectric layer of a plasmonic nano-rods waveguide. Since the CPA efficiency depends strongly on the number of plasmonic nano-rods and the nano rods location, an efficient binary optimization method based the BPSO algorithm is used to design an optimized array of the plasmonic nano-rod in order to achieve the maximum absorption coefficient in the 'off' state.
Since cloud computing provides computing resources on a pay per use basis, a task scheduling algorithm directly affects the cost for users. In this paper, we propose a novel cloud task scheduling algorithm based on an...
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Since cloud computing provides computing resources on a pay per use basis, a task scheduling algorithm directly affects the cost for users. In this paper, we propose a novel cloud task scheduling algorithm based on ant colony optimization that allocates tasks of cloud users to virtual machines in cloud computing environments in an efficient manner. To enhance the performance of the task scheduler in cloud computing environments with ant colony optimization, we adapt diversification and reinforcement strategies with slave ants. The proposed algorithm solves the global optimization problem with slave ants by avoiding long paths whose pheromones are wrongly accumulated by leading ants.
In this paper, by analyzing the best chaotic sequences generated by sixteen different chaotic maps, a novel chaos optimization algorithm is presented. It can intelligently base on different chaotic maps to select diff...
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In this paper, by analyzing the best chaotic sequences generated by sixteen different chaotic maps, a novel chaos optimization algorithm is presented. It can intelligently base on different chaotic maps to select different strategies so as to map the chaotic variables into the optimization variables. For the proposed algorithm, the obtained best values, the run time, and the role of the first and the second stage search by using different chaotic maps are also analyzed and compared. The simulation results implemented on several classic test functions demonstrate that the proposed algorithm has a high performance and an outstanding efficiency.
Self-learning process is an important factor that enables learners to improve their own educational experiences when they are away of face-to-face interactions with the teacher. A well-designed self-learning activity ...
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Self-learning process is an important factor that enables learners to improve their own educational experiences when they are away of face-to-face interactions with the teacher. A well-designed self-learning activity process supports both learners and teachers to achieve educational objectives rapidly. Because of this, there has always been a remarkable trend on developing alternative self-learning approaches. In this context, this study is based on two essential objectives. Firstly, it aims to introduce an intelligent software system, which optimizes and improves computer engineering students' self-learning processes. Secondly, it aims to improve computer engineering students' self-learning during the courses. As general, the software system introduced here evaluates students' intelligence levels according to the Theory of Multiple Intelligences and supports their learning via accurately chosen materials provided over the software interface. The evaluation mechanism of the system is based on a hybrid Artificial Intelligence approach formed by an Artificial Neural Network, and an optimization algorithm called as Vortex optimization algorithm (VOA). The system is usable for especially technical courses taught at computer engineering departments of universities and makes it easier to teach abstract subjects. For having idea about success of the system, it has been tested with students and positive results on optimizing and improving self-learning have been obtained. Additionally, also a technical evaluation has been done previously, in order to see if the VOA is a good choice to be used in the system. It can be said that the whole obtained results encourage the authors to continue to future works. (C) 2017 Wiley Periodicals, Inc.
Heuristic optimization is used to tune parameters in various scientific fields. Therefore, a successful optimization algorithm that must be able to evolve in a parsimonious manner in many situations is necessary. Ofte...
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Heuristic optimization is used to tune parameters in various scientific fields. Therefore, a successful optimization algorithm that must be able to evolve in a parsimonious manner in many situations is necessary. Often, heuristic optimization algorithms are inspired by nature but imperialist competitive algorithm, inspired by the laws and policies governing human society, was presented in recent decade. Imperialist competitive algorithm was applied in various fields. This algorithm had a very good performance compared to other optimization algorithms. For this reason, this study tries to modify imperialist competitive algorithm for improve the accuracy and performance of the algorithm. Assimilation operator of the imperialist competitive algorithm is modified. The main motivation of this work is to introduce a powerful heuristic optimization algorithm. A comparison between the proposed imperialist competitive algorithm framework and several versions imperialist competitive algorithm on 6 standard numerical benchmarks and four famous optimization algorithm indicate that the proposed algorithm has a good performance on a wide variety of problems.
Aiming at the reliability optimization algorithm based on wireless sensor network, a data fusion algorithm based on extreme learning machine for wireless sensor network was proposed according to the temporal spatial c...
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Aiming at the reliability optimization algorithm based on wireless sensor network, a data fusion algorithm based on extreme learning machine for wireless sensor network was proposed according to the temporal spatial correlation in data collection process. After analyzing the principles, design ideas and implementation steps of extreme learning machine algorithm, the performance and results were compared with traditional BP algorithm, LEACH algorithm and RBF algorithm in simulation environment. The simulation results showed that the data fusion optimization algorithm based on the limit learning machine for wireless sensor network was reliable. It improved the efficiency of fusion and the comprehensive reliability of the network. Thus, it can prolong the life cycle and reduce the total energy consumption of the network.
In recent years, digital technology has been used in all aspects of peoples work and life more and more frequently and has exerted a certain impact on the design of buildings. More and more architectural design teams ...
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In recent years, digital technology has been used in all aspects of peoples work and life more and more frequently and has exerted a certain impact on the design of buildings. More and more architectural design teams add mathematical logic and digital technology to the early stages of their design to better control the design work and implement the construction. Digital building design mainly includes parametric design and algorithm generation design, the former of which is mainly studied in this paper. As a modeling design algorithm which puts an emphasis on logic and reason, parametric design emphasizes the scientific nature of architectural design. In order to achieve the optimal design of the combination of building physics, institutional performance and parameterization, this paper applies the Dijkstra algorithm and the most energy efficient scheme generation (MEESG) energy consumption prediction method to the optimization design of wiring and performance and achieves good results, suggesting that the parameter optimization algorithm has a good impetus to the design of digital building.
In order to improve the accuracy and the convergence speed of the sphericity error, an improved teaching and learning algorithm is proposed to evaluate the sphericity error. Based on the basic teaching-learning-based ...
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In order to improve the accuracy and the convergence speed of the sphericity error, an improved teaching and learning algorithm is proposed to evaluate the sphericity error. Based on the basic teaching-learning-based optimization, the initial solution quality is improved by logistic chaotic initialization;At the end of each iteration, the interpolation algorithm is applied to the global optimal solution to further improve the search accuracy of the algorithm. Finally, one group of sphericity error algorithm though the measurement data in the related literature is verified the effectiveness of the ITLBO, the test result show that the ITLBO algorithm has advantages in the calculating accuracy and iteration convergence speed, and it is very suitable for the application in the sphericity error evaluation.
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