scholarly journals Bayesian inference of metal oxide ultrathin film structure based on crystal truncation rod measurements

2017 ◽  
Vol 50 (6) ◽  
pp. 1611-1616 ◽  
Author(s):  
Masato Anada ◽  
Yoshinori Nakanishi-Ohno ◽  
Masato Okada ◽  
Tsuyoshi Kimura ◽  
Yusuke Wakabayashi
Keyword(s):  
Bayesian Inference ◽  
Structural Parameters ◽  
Film Structure ◽  
Perovskite Oxide ◽  
Local Optima ◽  
Domain Structures ◽  
Crystal Truncation Rod ◽  
Global Optima ◽  
Optimum Model ◽  
Simulated Annealing Procedure

Monte Carlo (MC)-based refinement software to analyze the atomic arrangements of perovskite oxide ultrathin films from the crystal truncation rod intensity is developed on the basis of Bayesian inference. The advantages of the MC approach are (i) it is applicable to multi-domain structures, (ii) it provides the posterior probability of structures through Bayes' theorem, which allows one to evaluate the uncertainty of estimated structural parameters, and (iii) one can involve any information provided by other experiments and theories. The simulated annealing procedure efficiently searches for the optimum model owing to its stochastic updates, regardless of the initial values, without being trapped by local optima. The performance of the software is examined with a five-unit-cell-thick LaAlO3film fabricated on top of SrTiO3. The software successfully found the global optima from an initial model prepared by a small grid search calculation. The standard deviations of the atomic positions derived from a dataset taken at a second-generation synchrotron are ±0.02 Å for metal sites and ±0.03 Å for oxygen sites.

scholarly journals Chaotic Fitness Dependent Optimizer for Planning and Engineering Design

2021 ◽  
Author(s):  
Hardi M. Mohammed ◽  
Tarik A. Rashid
Keyword(s):  
Chaotic Maps ◽  
Task Assignment ◽  
Swarm Optimization ◽  
Local Optima ◽  
Tent Map ◽  
Engineering Problems ◽  
Global Optima ◽  
Specific Limitation ◽  
Task Assignment Problem ◽  
Better Than

Abstract Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that mimics the reproduction behavior of the bee swarm in finding better hives. This algorithm is similar to Particle Swarm Optimization (PSO) but it works differently. The algorithm is very powerful and has better results compared to other common metaheuristic algorithms. This paper aims at improving the performance of FDO, thus, the chaotic theory is used inside FDO to propose Chaotic FDO (CFDO). Ten chaotic maps are used in the CFDO to consider which of them are performing well to avoid local optima and finding global optima. New technic is used to conduct population in specific limitation since FDO technic has a problem to amend population. The proposed CFDO is evaluated by using 10 benchmark functions from CEC2019. Finally, the results show that the ability of CFDO is improved. Singer map has a great impact on improving CFDO while the Tent map is the worst. Results show that CFDO is superior to GA, FDO, and CSO. Both CEC2013 and CEC2005 are used to evaluate CFDO. Finally, the proposed CFDO is applied to classical engineering problems, such as pressure vessel design and the result shows that CFDO can handle the problem better than WOA, GWO, FDO, and CGWO. Besides, CFDO is applied to solve the task assignment problem and then compared to the original FDO. The results prove that CFDO has better capability to solve the problem.

Analytical procedures for determining global minimum friction requirement for a six wheeled rover negotiating hard uneven terrain

2020 ◽  
pp. 095440622096113
Author(s):  
Sam Noble ◽  
K Kurien Issac
Keyword(s):  
Global Minimum ◽  
Iterative Algorithms ◽  
Global Minima ◽  
Local Optima ◽  
Uneven Terrain ◽  
Order Of Magnitude ◽  
Global Optima ◽  
Reaction Forces ◽  
Cubic Polynomial ◽  
Bounded Below

The problem of determining wheel torques of a rover, to minimize friction requirement, has been addressed by many researchers. We address it by trying to understand the ways in which local optima can occur, and on the basis of that understanding, develop analytical and non-iterative algorithms for determining global optima. The problem is posed in two variations, with normal reaction forces on wheels bounded below by (a) zero, and (b) a positive limit. In both cases, the nature of optima is studied comprehensively, and illustrated by solving examples. Explicit expressions are used to find roots of up to a cubic polynomial. Our algorithms are on the average about an order of magnitude faster than an SQP based general optimization solver, when the latter is started from random guesses. Moreover, our algorithms were able to reach global minima without fail for all examples solved, which number more than a thousand.

Texture in films and Coatings

1997 ◽  
Vol 472 ◽  
Author(s):  
J.A. Szpunar
Keyword(s):  
Grain Orientation ◽  
Grain Shape ◽  
Structural Parameters ◽  
Relative Proportion ◽  
Stacking Faults ◽  
Orientation Distribution ◽  
Film Structure ◽  
Frame Of Reference ◽  
Micro Structure ◽  
Physical Chemical

The field of films and coatings is very broad. In general, the structure of films may comprise several phases, each having a different micro-structure and composition. These phases may be built up from grains which have different sizes, shape, morphology and orientation. A description of those types of films is complex and a large number of structural parameters is needed to fully characterize them. It is therefore more convenient to use a statistical description of them and to specify the relative proportion of different phases, grain orientation distribution [1], the orientation correlation between neighbouring crystals [2] and grain shape and size distribution [3]. Such a description provides details of the microstructural geometry of films, thereby giving a frame of reference within which various properties of films and a microstructural transformation of film structure, can be analyzed. Such microstructural descriptions have to be often supplemented by information about the nature and distribution of lattice defects, dislocations, stacking faults and antiphase boundaries. All this information put together, helps to provide a better understanding of the behaviour and various physical, chemical, mechanical and other properties, of films and coatings.

Super-High Dimension Complex Functions Optimization Using Adaptive Particle Swarm Optimizer

2012 ◽  
Vol 532-533 ◽  
pp. 1830-1835
Author(s):  
Ying Zhang ◽  
Bo Qin Liu ◽  
Han Rong Chen
Keyword(s):  
High Dimension ◽  
Large Scale ◽  
Particle Swarm ◽  
Search Space ◽  
Scale Space ◽  
Global Optimum ◽  
Particle Swarm Optimizer ◽  
Local Optima ◽  
Complex Functions ◽  
Global Optima

Due to the existence of large numbers of local and global optima of super-high dimension complex functions, general Particle Swarm Optimizer (PSO) methods are slow speed on convergence and easy to be trapped in local optima. In this paper, an Adaptive Particle Swarm Optimizer(APSO) is proposed, which employ an adaptive inertia factor and dynamic changes strategy of search space and velocity in each cycle to plan large-scale space global search and refined local search as a whole according to the fitness change of swarm in optimization process of the functions, and to quicken convergence speed, avoid premature problem, economize computational expenses, and obtain global optimum. We test the proposed algorithm and compare it with other published methods on several super-high dimension complex functions, the experimental results demonstrate that this revised algorithm can rapidly converge at high quality solutions.

scholarly journals LATTICE ENERGY DETERMINATION AND MOLECULAR SIMULATION OF PEROVSKITE OXIDES

2010 ◽  
Vol 8 (3) ◽  
pp. 385-391 ◽  
Author(s):  
Hellna Tehubijuluw ◽  
Ismunandar Ismunandar
Keyword(s):  
Molecular Simulation ◽  
Energy Equation ◽  
Structural Parameters ◽  
Approximate Equation ◽  
Lattice Energy ◽  
Simulation Method ◽  
Perovskite Oxides ◽  
Perovskite Oxide ◽  
Simple Equation ◽  
X Ray Diffraction

Determination of lattice energy, U, is an important work for ionic compounds, due to it is the driving force in the compounds formation. The objective this work are twofold, firstly to determine lattice energy of perovskite oxide using an approximate equation and compare lattice energy from the resulted simple equation and molecular simulation results. The perovskite oxides used on this study were rare-earth orthoferite (LnFeO3) and aluminate (LnAlO3). These ionic terner compounds adopt space group Pbnm or R-3c. Lattice energy of perovskite oxides were calculated using Glasser, (G), Glasser-Jenkins, (GJ), and Yoder-Flora, (YF) equations. Plots of Born-Haber lattice energies (U(BHC)) vs. U(G), U(GJ) and U(YF) resulted in R2 = 0.8872, 0.5919, and 0.9982. Thus the obtained best fit was U(BHC) vs. U(YF). The simple equation describing the best plot wasUperovskite oxide =[1,0579U(YF) - 835,06] kJ/mol. Molecular simulation method has also been carried out to determine lattice energy and the obtained  results were compared with those obtained from simple lattice energy equation, good agreement were obtained in these two oxides series. The obtained structural parameters also agreed well with those obtained from X-ray diffraction studies.   Keywords: lattice energy, perovskite oxides, lattice energy equation, and molecular simulation

scholarly journals Quantum-Inspired Wolf Pack Algorithm to Solve the 0-1 Knapsack Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yangjun Gao ◽  
Fengming Zhang ◽  
Yu Zhao ◽  
Chao Li
Keyword(s):  
Knapsack Problem ◽  
Global Search ◽  
Knapsack Problems ◽  
High Dimensional ◽  
Second Step ◽  
Local Optima ◽  
Global Optima ◽  
High Level ◽  
Search Capability ◽  
Statistical Results

This paper proposes a Quantum-Inspired wolf pack algorithm (QWPA) based on quantum encoding to enhance the performance of the wolf pack algorithm (WPA) to solve the 0-1 knapsack problems. There are two important operations in QWPA: quantum rotation and quantum collapse. The first step enables the population to move to the global optima and the second step helps to avoid the trapping of individuals into local optima. Ten classical and four high-dimensional knapsack problems are employed to test the proposed algorithm, and the results are compared with other typical algorithms. The statistical results demonstrate the effectiveness and global search capability for knapsack problems, especially for high-level cases.

scholarly journals OPTIMASI ALGORITMA ALGA UNTUK MENINGKATKAN LAJU KONVERGENSI

2017 ◽  
Vol 2 (1) ◽  
pp. 68-82
Author(s):  
Hari Santoso ◽  
Lukman Fakih Lidimilah
Keyword(s):  
Convergence Rate ◽  
Design Optimization ◽  
Pressure Vessel ◽  
Complexity Analysis ◽  
Selection Process ◽  
Three Dimensional ◽  
Helical Motion ◽  
Algorithm Optimization ◽  
Local Optima ◽  
Global Optima

Artificial AlgaeAlgorithm (AAA) is an optimization algorithm that has advantages of swarm algorithm model and evolution model. AAA consists of three phases of helical movement phase, reproduction, and adaptation. Helical movement is a three-dimensional movement with the direction of x, y, and z which is very influential in the rate of convergence and diversity of solutions. Helical motion optimization aims to increase the convergence rate by moving the algae to the best colony in the population. Algae Algorithm Optimization (AAA ') was tested with 25 objective functions of CEC'05 and implemented in case of pressure vessel design optimization. The results of the CEC'05 function test show that there is an increase in convergence rate at AAA ', but at worst condition of AAA' becomes less stable and trapped in local optima. The complexity analysis shows that AAA has the complexity of O (M3N2O) and AAA 'has the complexity of O (M2N2O) with M is the number of colonies, N is the number of algae individuals, and O is the maximum of the evaluation function. The results of the implementation of pressure vessel design optimization show that AAA's execution time increased 1,103 times faster than AAA. The increase in speed is due to the tournament selection process in AAA performed before the helical motion, whereas in AAA 'is done if the solution after movement is no better than before. At its best, AAA 'found a solution 4.5921 times faster than AAA. At worst, AAA 'stuck on local optima because helical movement is too focused on global best that is not necessarily global optima.  

scholarly journals Big Data Classification Using Distributed Optimized Hoeffding Trees

2017 ◽  
Vol 2 (1) ◽  
pp. 14-20
Author(s):  
Sharmishta Suhas Desai ◽  
S. T. Patil
Keyword(s):  
Data Stream ◽  
Three Dimensions ◽  
Map Reduce ◽  
Time Data ◽  
Data Set ◽  
Local Optima ◽  
Hoeffding Tree ◽  
Leaf Level ◽  
Global Optima ◽  
Big Data Classification

Large usage of social media, online shopping or transactions gives birth to voluminous data. Visual representation and analysis of this large amount of data is one of the major research topics today. As this data is changing over the period of time, we need an approach which will take care of velocity of data as well as volume and variety. In this paper, author has proposed a distributed method which will handle three dimensions of data and gives good results as compared to other method.  Traditional algorithms are based on global optima which are basically memory resident programs. Our approach which is based on optimized hoeffding bound uses local optima method and distributed map-reduce architecture. It does not require copying whole data set onto a memory. As the model build is frequently updated on multiple nodes concurrently, it is more suitable for time varying data. Hoeffding bound is basically suitable for real time data stream. We have proposed very efficient distributed map-reduce architecture to implement hoeffding tree efficiently. We have used deep learning at leaf level to optimize the hoeffding tree. Drift detection is taken care by the architecture itself no separate provision is required for this. In this paper, with experimental results it is proved that our method takes less learning time with more accuracy. Also distributed algorithm for hoeffding tree implementation is proposed.

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