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

Tomàs Artés, Andrés Cencerrado, Ana Cortés, Tomàs Margalef

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)

Abstract

© 2014, Springer Science+Business Media New York. 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.
Original languageEnglish
Pages (from-to)1869-1881
JournalJournal of Supercomputing
Volume71
DOIs
Publication statusPublished - 1 May 2015

Keywords

  • Core allocation
  • Efficiency
  • Forest fire spread prediction
  • Hybrid MPI-OpenMP scheme
  • Multi-core platforms
  • Time-aware

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