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Published in:
2008 | OriginalPaper | Chapter
Generalized Extremal Optimization for Solving Multiprocessor Task Scheduling Problem
Authors : Piotr Switalski, Franciszek Seredynski
Published in: Simulated Evolution and Learning
Publisher: Springer Berlin Heidelberg
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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.