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Generalized Extremal Optimization for Solving Multiprocessor Task Scheduling Problem

  • Conference paper
Simulated Evolution and Learning (SEAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5361))

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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.

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Author information

Authors and Affiliations

  1. Computer Science Department, The University of Podlasie, 3 Maja 54, 08-110, Siedlce, Poland

    Piotr Switalski

  2. Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008, Warsaw, Poland

    Franciszek Seredynski

  3. Institute of Computer Science, Polish Academy of Sciences, Ordona 21, 01-237, Warsaw, Poland

    Franciszek Seredynski

Authors
  1. Piotr Switalski
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  2. Franciszek Seredynski
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Editor information

Editors and Affiliations

  1. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  2. Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, VIC 3001, Melbourne, Australia

    Michael Kirley

  3. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, 6140, Wellington, New Zealand

    Mengjie Zhang

  4. Faculty of Information Technology, Monash University, Clayton Campus, VIC 3800, Australia

    David Green

  5. School of Computer Science and Information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Vic Ciesielski

  6. School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia

    Hussein Abbass

  7. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

  8. Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

    Tim Hendtlass

  9. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  10. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

  11. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  12. Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

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© 2008 Springer-Verlag Berlin Heidelberg

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Cite this paper

Switalski, P., Seredynski, F. (2008). Generalized Extremal Optimization for Solving Multiprocessor Task Scheduling Problem. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_17

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  • DOI: https://doi.org/10.1007/978-3-540-89694-4_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89693-7

  • Online ISBN: 978-3-540-89694-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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