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

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 algor...

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Veröffentlicht in:Computers & operations research Jg. 34; H. 10; S. 3084 - 3098
Hauptverfasser: Yoo, Myungryun, Gen, Mitsuo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 01.10.2007
Elsevier Science
Pergamon Press Inc
Schlagworte:
Applied sciences
Computer science; control theory; systems
Computer simulation
Computer systems and distributed systems. User interface
Exact sciences and technology
Genetic algorithms
Heterogeneous multiprocessor
Multiobjective hybrid genetic algorithm
Multiprocessing
Operational research and scientific management
Operational research. Management science
Real time
Scheduling algorithms
Scheduling for real-time tasks
Scheduling, sequencing
Software
Studies
Multiobjective hybrid genetic algorithm
Heterogeneous multiprocessor
Scheduling for real-time tasks
Current information
Probabilistic approach
Useful information
Multiobjective programming
Makespan
Real time
Adaptive method
Delay
Multiprocessor
Processor scheduling
Genetic algorithm
NP hard problem
Simulated annealing
Numerical convergence
ISSN:0305-0548, 1873-765X, 0305-0548
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Zusammenfassung: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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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2005.11.016