Determination of ORR Parameters from Fuel Cell Polarization Data Using Modified Simulated Annealing and Genetic Algorithms

2011 ◽  
Keyword(s):  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Fuel Cell ◽  
Cell Polarization ◽  
Polarization Data

scholarly journals Stochastic algorithms for buffer allocation in reliable production lines

2000 ◽  
Vol 5 (6) ◽  
pp. 441-458 ◽  
Author(s):  
Diomidis D. Spinellis ◽  
Chrissoleon T. Papadopoulos
Keyword(s):  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Decomposition Method ◽  
Manufacturing Systems ◽  
Optimization Problem ◽  
Allocation Problem ◽  
Stochastic Algorithms ◽  
Buffer Allocation ◽  
Production Lines

The allocation of buffers between workstations is a major optimization problem faced by manufacturing systems designers. It entails the determination of optimal buffer allocation plans in production lines with the objective of maximizing their throughput. We present and compare two stochastic approaches for solving the buffer allocation problem in large reliable production lines. The allocation plan is calculated subject to a given amount of total buffer slots using simulated annealing and genetic algorithms. The throughput is calculated utilizing a decomposition method.

scholarly journals Model Structure Optimization for Fuel Cell Polarization Curves

Computers ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 60 ◽  
Author(s):  
Markku Ohenoja ◽  
Aki Sorsa ◽  
Kauko Leiviskä
Keyword(s):  
Genetic Algorithms ◽  
Fuel Cell ◽  
Polarization Curve ◽  
Cell System ◽  
Cell Polarization ◽  
Operating Conditions ◽  
Model Complexity ◽  
Structure Identification ◽  
Model Parameters ◽  
Model Structure

The applications of evolutionary optimizers such as genetic algorithms, differential evolution, and various swarm optimizers to the parameter estimation of the fuel cell polarization curve models have increased. This study takes a novel approach on utilizing evolutionary optimization in fuel cell modeling. Model structure identification is performed with genetic algorithms in order to determine an optimized representation of a polarization curve model with linear model parameters. The optimization is repeated with a different set of input variables and varying model complexity. The resulted model can successfully be generalized for different fuel cells and varying operating conditions, and therefore be readily applicable to fuel cell system simulations.

Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists

2021 ◽  
Author(s):  
Holman Ospina-Mateus ◽  
Leonardo Augusto Quintana Jiménez ◽  
Francisco J. Lopez-Valdes ◽  
Shyrle Berrio Garcia ◽  
Lope H. Barrero ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Traffic Accidents ◽  
Decision Rules

Computing the Medical Image Registration Using Meta-Heuristics

2014 ◽  
Vol 643 ◽  
pp. 237-242 ◽  
Author(s):  
Tahari Abdou El Karim ◽  
Bendakmousse Abdeslam ◽  
Ait Aoudia Samy
Keyword(s):  
Image Processing ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Image Registration ◽  
Cost Function ◽  
Population Genetic ◽  
Medical Image ◽  
Medical Image Registration ◽  
Anatomical Structures

The image registration is a very important task in image processing. In the field of medical imaging, it is used to compare the anatomical structures of two or more images taken at different time to track for example the evolution of a disease. Intensity-based techniques are widely used in the multi-modal registration. To have the best registration, a cost function expressing the similarity between these images is maximized. The registration problem is reduced to the optimization of a cost function. We propose to use neighborhood meta-heuristics (tabu search, simulated annealing) and a meta-heuristic population (genetic algorithms). An evaluation step is necessary to estimate the quality of registration obtained. In this paper we present some results of medical image registration

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