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. © 2008 Springer Berlin Heidelberg.
CITATION STYLE
Switalski, P., & Seredynski, F. (2008). Generalized extremal optimization for solving multiprocessor task scheduling problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5361 LNAI, pp. 161–169). https://doi.org/10.1007/978-3-540-89694-4_17
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