Scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm in heterogeneous multiprocessors system

Scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm in heterogeneous multiprocessors system

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Article ID: iaor20083037
Country: United Kingdom
Volume: 34
Issue: 10
Start Page Number: 3084
End Page Number: 3098
Publication Date: Oct 2007
Journal: Computers and Operations Research
Authors: ,
Keywords: heuristics: genetic algorithms, optimization: simulated annealing
Abstract:

The scheduling problem for real-time tasks on multiprocessor is one of the NP-hard problems. This paper proposes a new scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm (mohGA) on heterogeneous multiprocessor environment. In solution algorithms, the genetic algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The proposed algorithm has a multiobjective to minimize the total tardiness and completion time simultaneously. For these conflicting objectives, this paper combines adaptive weight approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the results of the proposed algorithm are better than that of other algorithms.

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