BibTeX
@MISC{Parallel02real-,
author = {Multiscale Master-Slave Parallel and Meghna Babbar},
title = {Real - World Applications},
year = {2002}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Numerical models are an integral part of many optimal engineering design problems. The accuracy of fitness function evaluations for these applications depends on the spatial grid sizes used in the numerical models. Fine grids usually improve the accuracy of the solutions, but they can also pose major bottlenecks in the computational efficiency of the algorithms. The need to carefully select a grid size that can maintain the numerical accuracy of the solutions, along with being computationally less exhaustive, is very crucial for applications that use genetic algorithms. In this work, we present a multiscale parallel genetic algorithm that can be used to improve the performance of engineering design problems that use spatial grids. The algorithm's efficacy is tested using a groundwater remediation design case study.
Keyphrases
real world application numerical model fine grid many optimal engineering design problem fitness function evaluation genetic algorithm spatial grid size numerical accuracy spatial grid grid size integral part major bottleneck computational efficiency groundwater remediation design case study engineering design problem multiscale parallel genetic algorithm
