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Generalized Extremal Optimization for Solving Multiprocessor Task Scheduling Problem

  • Piotr Switalski
  • Franciszek Seredynski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)

Abstract

In this paper we propose a solution of a multiprocessor task scheduling problem with use of a new meta-heuristic inspired by a model of natural evolution called Generalized Extremal Optimization (GEO). It is inspired by a simple co-evolutionary model based on a Bak-Sneppen model. One of advantages of the model is a simple implementation of potential optimization problems and only one free parameter to adjust. The idea of GEO metaheuristic and the way of applying it to the multi-processor scheduling problem are presented in the paper. In this problem the tasks of a program graph are allocated into multiprocessor system graph where the program completion time is minimized. The problem is know to be a NP-complete problem. In this paper we show that GEO is to able to solve this problem with better performance than genetic algorithm.

Keywords

multiprocessor task scheduling problem Generalized Extremal Optimization GEO genetic algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Piotr Switalski
    • 1
  • Franciszek Seredynski
    • 2
    • 3
  1. 1.Computer Science DepartmentThe University of PodlasieSiedlcePoland
  2. 2.Polish-Japanese Institute of Information TechnologyWarsawPoland
  3. 3.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

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