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A Bipartite Genetic Algorithm for Multi-processor Task Scheduling

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Abstract

Until now, several methods have been presented to optimally solve the multiprocessor task scheduling problem that is an NP-hard one. In this paper, a genetic-based algorithm has been presented to solve this problem with better results in comparison with related methods. The proposed method is a bipartite algorithm in a way that each part is based on different genetic schemes, such as genome presentation and genetic operators. In the first part, it uses a genetic method to find an adequate sequence of tasks and in the second one, it finds the best match processors. To evaluate the proposed method, we applied it on several benchmarks and the results were compared with well known algorithms. The experimental results were satisfactory and in most cases the presented method had a better makespan with at least 10% less iterations compared to related works.

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, Shahid Beheshti University G.C., Velanjak Ave, Tehran, Iran

    Mohammad Reza Bonyadi & Mohsen Ebrahimi Moghaddam

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  1. Mohammad Reza Bonyadi
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  2. Mohsen Ebrahimi Moghaddam
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Correspondence to Mohsen Ebrahimi Moghaddam.

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Bonyadi, M.R., Ebrahimi Moghaddam, M. A Bipartite Genetic Algorithm for Multi-processor Task Scheduling. Int J Parallel Prog 37, 462–487 (2009). https://doi.org/10.1007/s10766-009-0107-8

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