An Inverse Method Using a Genetic Algorithm to Determine Spatial Temperature Distribution From Infrared Tranmissivity Measurements in a Gas

Volume 4 ◽  
2004 ◽  
Author(s):  
Keith A. Woodbury ◽  
Courtney Graham ◽  
John Baker ◽  
Charles Karr
Keyword(s):  
Genetic Algorithm ◽  
Temperature Distribution ◽  
Inverse Problems ◽  
Inverse Method ◽  
Biological Evolution ◽  
Optimization Method ◽  
Global Optimum ◽  
Simple Problem ◽  
Straight Line ◽  
Ill Posed

The ill-posed nature of inverse problems suggests that a solution be obtained through an optimization method. Genetic algorithms (GAs) effectively locate the global optimum, and are therefore an appealing technique to solve inverse problems. GAs mimic biological evolution, refining a set of solutions until the best solution is found. In this report, a genetic algorithm is developed and demonstrated based on a simple problem of determining the equation of a straight line. Then the GA is modified and implemented to estimate the temperature distribution in a gas based on the measured infrared tranmissivity distribution. The ulitimate task of this inverse method will be determination of the gas composition based on these transmissivity measurements.

An inverse method for prediction of the required temperature distribution in the creep forming process

1998 ◽  
Vol 212 (1) ◽  
pp. 7-11 ◽  
Author(s):  
P Moreau ◽  
D Lochegnies ◽  
J Oudin
Keyword(s):  
Finite Element ◽  
Temperature Distribution ◽  
Inverse Method ◽  
Numerical Procedure ◽  
Small Variation ◽  
Optimization Method ◽  
Glass Sheet ◽  
Finite Element Analyses ◽  
Forming Process ◽  
Identification Procedure

To achieve the creep forming of glass sheet from designer specifications, the manufacturer has to know the required temperature distribution in the glass sheet accurately: a small variation of the temperature produces great change in the viscosity, and therefore, in the final shape of the sheet. In order to find this distribution, the authors propose an inverse identification procedure based on an optimization method and finite element analyses. The inverse problem is solved using a modified Levenberg—Marquardt method to match the measured displacements to the finite element solutions which depend on the unknown forming parameters. The manufacture of recent rear automotive screens illustrates this efficient numerical procedure.

AUTOMOTIVE PARTS' LOADING OPTIMIZATION BASED ON IMPROVED QUADRATIC PARTICLE SWARM OPTIMIZATION

2010 ◽  
Vol 07 (04) ◽  
pp. 699-712
Author(s):  
JIAN LIU ◽  
CHUNYAN WU ◽  
XIANGYIN WANG ◽  
DEJIE YU
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Present Article ◽  
Particle Swarm ◽  
Optimization Method ◽  
Global Optimum ◽  
Swarm Optimization ◽  
Automotive Parts ◽  
Set Up ◽  
Decision Elements

The present article designed a genetic quadratic particle swarm optimization (GQPSO). Aiming at the low particle diversity at the early searching stage of quadratic particle swarm optimization (QPSO), the method adopts mutation and exchanging and regenerating mechanisms from genetic algorithm so as to avoid premature convergence and improves optimization. Meanwhile, the present article gave a comprehensive consideration to decision elements such as cost, resources, and service in the process of automotive parts' logistics, transportation, and loading; a model of automotive parts' logistics, transportation, and loading optimization was set up. GQPSO was introduced for solutions. Simulation examples show that GQPSO improves the computational efficiency, significantly, and there is a higher probability of searching the global optimum. It provides optimization method for the automobile parts' logistics and transportation plans.

scholarly journals Optimized resource-constrained method for project schedule compression

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Moaaz Elkabalawy ◽  
Osama Moselhi
Keyword(s):  
Genetic Algorithm ◽  
Resource Planning ◽  
Optimization Methods ◽  
Optimization Method ◽  
Global Optimum ◽  
Project Schedule ◽  
Nsga Ii ◽  
Content Type ◽  
Modeling Uncertainties ◽  
Weighted Optimization

PurposeThis paper aims to present an integrated method for optimized project duration and costs, considering the size and cost of crews assigned to project activities' execution modes.Design/methodology/approachThe proposed method utilizes fuzzy set theory (FSs) for modeling uncertainties associated with activities' duration and cost and genetic algorithm (GA) for optimizing project schedule. The method has four main modules that support two optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; and decision-support module. The first optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the second uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization methods are coded in python as a stand-alone automated computerized tool to facilitate the developed method's application.FindingsThe developed method is applied to a numerical example to demonstrate its use and illustrate its capabilities. The method was validated using a multi-layered comparative analysis that involves performance evaluation, statistical comparisons and stability evaluation. Results indicated that NSGA-II outperformed the weighted optimization method, resulting in a better global optimum solution, which avoided local minima entrapment. Moreover, the developed method was constructed under a deterministic scenario to evaluate its performance in finding optimal solutions against the previously developed literature methods. Results showed the developed method's superiority in finding a better optimal set of solutions in a reasonable processing time.Originality/valueThe novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting.

Development of Global Optimization Method for Design of Turbine Stages

2005 ◽  
Author(s):  
Hiroyuki Kawagishi ◽  
Kazuhiko Kudo
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Simulated Annealing ◽  
Optimum Design ◽  
Optimization Method ◽  
Global Optimum ◽  
Global Optimization Method ◽  
Optimum Solution ◽  
Global Optimum Solution ◽  
Number Of Iterations

A new optimization method which can search for the global optimum solution and decrease the number of iterations was developed. The performance of the new method was found to be effective in finding the optimum solution for single- and multi-peaked functions for which the global optimum solution was known in advance. According to the application of the method to the optimum design of turbine stages, it was shown that the method can search the global optimum solution at approximately one seventh of the iterations of GA (Genetic Algorithm) or SA (Simulated Annealing).

Methods of solving ill-posed inverse problems

1983 ◽  
Vol 45 (5) ◽  
pp. 1237-1245 ◽  
Author(s):  
O. M. Alifanov
Keyword(s):  
Inverse Problems ◽  
Ill Posed

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

scholarly journals Optimization of a 3D-Printed Permanent Magnet Coupling Using Genetic Algorithm and Taguchi Method

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 494
Author(s):  
Ekaterina Andriushchenko ◽  
Ants Kallaste ◽  
Anouar Belahcen ◽  
Toomas Vaimann ◽  
Anton Rassõlkin ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Taguchi Method ◽  
Permanent Magnet ◽  
Optimization Problems ◽  
Optimization Method ◽  
Taguchi Optimization ◽  
Torque Density ◽  
Electromagnetic Devices ◽  
3D Printed ◽  
Manufacturing Techniques

In recent decades, the genetic algorithm (GA) has been extensively used in the design optimization of electromagnetic devices. Despite the great merits possessed by the GA, its processing procedure is highly time-consuming. On the contrary, the widely applied Taguchi optimization method is faster with comparable effectiveness in certain optimization problems. This study explores the abilities of both methods within the optimization of a permanent magnet coupling, where the optimization objectives are the minimization of coupling volume and maximization of transmitted torque. The optimal geometry of the coupling and the obtained characteristics achieved by both methods are nearly identical. The magnetic torque density is enhanced by more than 20%, while the volume is reduced by 17%. Yet, the Taguchi method is found to be more time-efficient and effective within the considered optimization problem. Thanks to the additive manufacturing techniques, the initial design and the sophisticated geometry of the Taguchi optimal designs are precisely fabricated. The performances of the coupling designs are validated using an experimental setup.

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