scholarly journals A simulated annealing-based algorithm for selecting balanced samples

2021 ◽  
Author(s):  
Roberto Benedetti ◽  
Maria Michela Dickson ◽  
Giuseppe Espa ◽  
Francesco Pantalone ◽  
Federica Piersimoni
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Sample Selection ◽  
Auxiliary Information ◽  
Real Data ◽  
Simulation Experiments ◽  
Balanced Sampling ◽  
Inclusion Probabilities ◽  
Random Method ◽  
Annealing Algorithms

AbstractBalanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

The Optimal Research of Force Increasing Mechanism of Injection Machine based on Genetic Simulated Annealing Algorithms

2011 ◽  
Vol 338 ◽  
pp. 505-512
Author(s):  
Tie Jun Li ◽  
Xin Li ◽  
Li Qun Yan ◽  
Cheng Shi Zhu ◽  
Yong Ying Du
Keyword(s):  
Simulated Annealing ◽  
Performance Measures ◽  
Optimization Design ◽  
Optimization Problem ◽  
Design Parameters ◽  
Performance Parameters ◽  
Total Length ◽  
Complete Procedure ◽  
Annealing Algorithms ◽  
Analysis Of Motion

The analysis of motion and mechanics property has been studied on the five hinged incline arranged and double elbowed force increasing mechanism of injection machine in this paper. An optimization design is proposed on the force increasing mechanism by use of genetic simulated annealing algorithms. A complete procedure of optimal design is introduced so as to increase the stroke ratio and the amplification of the force, and to decrease the total length of mechanism, which belongs to multi-object optimization problem. Compared with the traditional methods, the result shows that the stroke ratio is increased, the amplification of the force is increased and the total length of mechanism is decreased. Moreover a sensitivity analysis of design parameters has been performed to see changes in injection performance parameters, and results show that the length of back elbowed bar and the length of connected bar have a significant impact on the performance measures. And the results recommend that the close clearance of the length of back elbowed bar and the length of connected bar must be maintained.

scholarly journals Enhanced cube implementation for highly stratified population

2021 ◽  
Author(s):  
Raphaël Jauslin ◽  
Esther Eustache ◽  
Yves Tillé
Keyword(s):  
Sampling Design ◽  
Auxiliary Information ◽  
New Method ◽  
Balanced Sampling ◽  
Sampling Process ◽  
Inclusion Probabilities ◽  
Stratified Structure ◽  
Selection Of

AbstractA balanced sampling design should always be the adopted strategy if auxiliary information is available. In addition, integrating a stratified structure of the population in the sampling process can considerably reduce the variance of the estimators. We propose here a new method to handle the selection of a balanced sample in a highly stratified population. The method improves substantially the commonly used sampling designs and reduces the time-consuming problem that could arise if inclusion probabilities within strata do not sum to an integer.

scholarly journals Distributional Robustness of K-class Estimators and the PULSE

2021 ◽  
Author(s):  
Martin Emil Jakobsen ◽  
Jonas Peters
Keyword(s):  
Instrumental Variable ◽  
Optimization Problem ◽  
Confidence Region ◽  
Real Data ◽  
Data Driven ◽  
Simulation Experiments ◽  
Distributional Robustness ◽  
Mean Squared Prediction Error ◽  
Squared Prediction Error ◽  
The Mean

Abstract While causal models are robust in that they are prediction optimal under arbitrarily strong interventions, they may not be optimal when the interventions are bounded. We prove that the classical K-class estimator satisfies such optimality by establishing a connection between K-class estimators and anchor regression. This connection further motivates a novel estimator in instrumental variable settings that minimizes the mean squared prediction error subject to the constraint that the estimator lies in an asymptotically valid confidence region of the causal coefficient. We call this estimator PULSE (p-uncorrelated least squares estimator), relate it to work on invariance, show that it can be computed efficiently as a data-driven K-class estimator, even though the underlying optimization problem is non-convex, and prove consistency. We evaluate the estimators on real data and perform simulation experiments illustrating that PULSE suffers from less variability. There are several settings including weak instrument settings, where it outperforms other estimators.

scholarly journals GRSA Enhanced for Protein Folding Problem in the Case of Peptides

Axioms ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 136
Author(s):  
Juan Frausto-Solís ◽  
Juan Paulo Sánchez-Hernández ◽  
Fanny G. Maldonado-Nava ◽  
Juan J. González-Barbosa
Keyword(s):  
Amino Acids ◽  
Protein Folding ◽  
Simulated Annealing ◽  
Optimization Problem ◽  
Golden Ratio ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
First Case ◽  
Hybrid Simulated Annealing ◽  
Annealing Algorithms

Protein folding problem (PFP) consists of determining the functional three-dimensional structure of a target protein. PFP is an optimization problem where the objective is to find the structure with the lowest Gibbs free energy. It is significant to solve PFP for use in medical and pharmaceutical applications. Hybrid simulated annealing algorithms (HSA) use a kind of simulated annealing or Monte Carlo method, and they are among the most efficient for PFP. The instances of PFP can be classified as follows: (a) Proteins with a large number of amino acids and (b) peptides with a small number of amino acids. Several HSA have been positively applied for the first case, where I-Tasser has been one of the most successful in the CASP competition. PEP-FOLD3 and golden ratio simulated annealing (GRSA) are also two of these algorithms successfully applied to peptides. This paper presents an enhanced golden simulated annealing (GRSA2) where soft perturbations (collision operators), named “on-wall ineffective collision” and “intermolecular ineffective collision”, are applied to generate new solutions in the metropolis cycle. GRSA2 is tested with a dataset for peptides previously proposed, and a comparison with PEP-FOLD3 and I-Tasser is presented. According to the experimentation, GRSA2 has an equivalent performance to those algorithms.

Exergy and sensibility analysis of each individual effect in a kraft multiple effect evaporator

TAPPI Journal ◽  
2019 ◽  
Vol 18 (10) ◽  
pp. 607-618
Author(s):  
JÉSSICA MOREIRA ◽  
BRUNO LACERDA DE OLIVEIRA CAMPOS ◽  
ESLY FERREIRA DA COSTA JUNIOR ◽  
ANDRÉA OLIVEIRA SOUZA DA COSTA
Keyword(s):  
Optimization Problem ◽  
Real Data ◽  
Kraft Pulping ◽  
Steam Temperature ◽  
Input Flow ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients ◽  
Multiple Effect ◽  
Sensibility Analysis ◽  
Exergetic Analysis

The multiple effect evaporator (MEE) is an energy intensive step in the kraft pulping process. The exergetic analysis can be useful for locating irreversibilities in the process and pointing out which equipment is less efficient, and it could also be the object of optimization studies. In the present work, each evaporator of a real kraft system has been individually described using mass balance and thermodynamics principles (the first and the second laws). Real data from a kraft MEE were collected from a Brazilian plant and were used for the estimation of heat transfer coefficients in a nonlinear optimization problem, as well as for the validation of the model. An exergetic analysis was made for each effect individually, which resulted in effects 1A and 1B being the least efficient, and therefore having the greatest potential for improvement. A sensibility analysis was also performed, showing that steam temperature and liquor input flow rate are sensible parameters.

CKMSA: an Anomaly Detection Process Based on K-Means and Simulated Annealing Algorithms

2016 ◽  
Vol 11 (4) ◽  
pp. 373
Author(s):  
Hamza Kamal Idrissi ◽  
Zaid Kartit ◽  
Ali Kartit ◽  
Mohamed El Marraki
Keyword(s):  
Simulated Annealing ◽  
Anomaly Detection ◽  
Detection Process ◽  
Annealing Algorithms

CKMSA: an Anomaly Detection Process Based on K-Means and Simulated Annealing Algorithms

2016 ◽  
Vol 11 (1) ◽  
pp. 42 ◽  
Author(s):  
Hamza Kamal Idrissi ◽  
Zaid Kartit ◽  
Ali Kartit ◽  
Mohamed El Marraki
Keyword(s):  
Simulated Annealing ◽  
Anomaly Detection ◽  
Detection Process ◽  
Annealing Algorithms

Trajectory Optimization Applied to Space Rendezvous

2013 ◽  
Vol 756-759 ◽  
pp. 3466-3470
Author(s):  
Xu Min Song ◽  
Qi Lin
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Optimization Method ◽  
Nonlinear Constraints ◽  
Design Variables ◽  
Adaptive Simulated Annealing ◽  
Simulation Results

The trajcetory plan problem of spece reandezvous mission was studied in this paper using nolinear optimization method. The optimization model was built based on the Hills equations. And by analysis property of the design variables, a transform was put forward , which eliminated the equation and nonlinear constraints as well as decreaseing the problem dimensions. The optimization problem was solved using Adaptive Simulated Annealing (ASA) method, and the rendezvous trajectory was designed.The method was validated by simulation results.

Sign in / Sign up

Export Citation Format

Share Document