Enhancing computational efficiency on forest fire forecasting by time-aware Genetic Algorithms

TitleEnhancing computational efficiency on forest fire forecasting by time-aware Genetic Algorithms
Publication TypeJournal Article
Year of Publication2015
AuthorsArtés, T, Cencerrado, A, Cortés, A, Margalef, T
JournalThe Journal of Supercomputing
Volume71
Start Page1869 - 1881
Issue5
Date Published05/2015
ISSN1573-0484
Abstract

A way to overcome data input uncertainty when simulating forest fire propagation, consists of calibrating inaccurate input data by applying computational-intensive methods. Genetic Algorithms (GA) are powerful and robust optimization techniques. However, their main drawback is their overall run time, which can easily become unacceptable, especially when dealing with natural disasters forecast. The prediction system has been parallelized using a hybrid MPI-OpenMP approach where the number of cores allocated to each GA individual is based on a priori time-aware population classification, which allows to keep bounding the optimization process bound to a predetermined deadline. In this work, an efficient time-aware GA is introduced that estimates the required number of cores to keep the calibration process under imposed time limits and also takes into account an efficient use of the computational resources.

URLhttp://link.springer.com/article/10.1007/s11227-014-1365-9
DOI10.1007/s11227-014-1365-9
Campus d'excel·lència internacional U A B