A New Simulated Annealing Technique for Non-Linear Optimization

1997 ◽  
Author(s):  
K.C. Mundim ◽  
T.J. Lemaire ◽  
A. Bassrei
Keyword(s):  
Simulated Annealing ◽  
Linear Optimization ◽  
Non Linear

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

The simplex-simulated annealing approach to continuous non-linear optimization

1996 ◽  
Vol 20 (9) ◽  
pp. 1065-1080 ◽  
Author(s):  
Margarida F. Cardoso ◽  
R.L. Salcedo ◽  
S. Feyo de Azevedo
Keyword(s):  
Simulated Annealing ◽  
Linear Optimization ◽  
Non Linear

Can speakers’ virtual lexical richness be calculated?

2016 ◽  
Vol 13 (2) ◽  
pp. 285-307
Author(s):  
Antonio Manuel Ávila Muñoz
Keyword(s):  
Simulated Annealing ◽  
Pilot Study ◽  
Linear Optimization ◽  
Randomized Algorithm ◽  
Original Method ◽  
New Model ◽  
Lexical Variation ◽  
Lexical Richness ◽  
Non Linear

The aim of this paper is to show an original method of calculating individual lexical richness. This method leads to a non-linear optimization. A randomized algorithm, Simulated Annealing, is used in order to carry out the optimization. This procedure has allowed us to represent a function from which we obtain a reliable pattern for lexical estimation. Furthermore, this method is compatible with other traditional procedures used for the estimation of lexical richness. Therefore, in this work we have taken an alternative and more general approach: we wish to calculate the virtual richness of the individual’s vocabulary. In order to validate the new model that calculates lexical richness, we carried out a pilot study based on the subjects’ sociolinguistic patterns that govern their lexical richness. We have explored the lexical variation of the Spanish system that occurs during the oral exchanges of 86 speakers born in Malaga.

Determination of Total Energy Tight Binding Parameters from First Principles Calculations Using Adaptive Simulated Annealing

2001 ◽  
Vol 700 ◽  
Author(s):  
Anders G. Froseth ◽  
Peter Derlet ◽  
Ragnvald Hoier
Keyword(s):  
Simulated Annealing ◽  
Total Energy ◽  
First Principles ◽  
Linear Optimization ◽  
Tight Binding ◽  
Accurate Method ◽  
Optimization Method ◽  
Binding Parameters ◽  
Non Linear

AbstractEmpirical Total Energy Tight Binding (TETB) has proven to be a fast and accurate method for calculating materials properties for various system, including bulk, surface and amorphous structures. The determination of the tight binding parameters from first-principles results is a multivariate, non-linear optimization problem with multiple local minima. Simulated annealing is an optimization method which is flexible and “guaranteed” to find a global minimum, opposed to classical methods like non-linear least squares algorithms. As an example results are presented for a nonorthogonal s,p parameterization for Silicon based on the NRL tight binding formalism.

scholarly journals Design of a New Synthetic Acceptance Sampling Plan

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 653 ◽  
Author(s):  
Saeed Dobbah ◽  
Muhammad Aslam ◽  
Khushnoor Khan
Keyword(s):  
Standard Deviation ◽  
Normal Distribution ◽  
Operating Characteristic ◽  
Linear Optimization ◽  
Sampling Plan ◽  
Quality Characteristic ◽  
Acceptance Sampling ◽  
Acceptance Sampling Plan ◽  
Non Linear

In this paper, we propose a new synthetic sampling plan assuming that the quality characteristic follows the normal distribution with known and unknown standard deviation. The proposed plan is given and the operating characteristic (OC) function is derived to measure the performance of the proposed sampling plan for some fixed parameters. The parameters of the proposed sampling plan are determined using non-linear optimization solution. A real example is added to explain the use of the proposed plan by industry.

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