Generalized Extremal Optimization for Solving Multiprocessor Task Scheduling Problem

Authors:
Piotr Switalski;Franciszek Seredynski
Affiliations:
Computer Science Department, The University of Podlasie, Siedlce, Poland 08-110;Polish-Japanese Institute of Information Technology, Warsaw, Poland 02-008 and Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland 01-237
Venue:
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Year:
2008

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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.