Sequential and Distributed Hybrid GA-SA Algorithms for Energy Optimization in Embedded Systems

Abstract : Reducing memory energy consumption in embedded systems is crucial. In this paper, we propose new hybrid sequential and distributed algorithms based on Simulated Annealing (SA) and Genetic Algorithms (GA) in order to reduce memory energy consumption in embedded systems. Our algorithms outperform the Tabu Search (TS) approach. In fact, our hybrid algorithms manage to consume nearly from 76% up to 98% less memory energy than TS. Execution time savings for the distributed version (nearly from 72% up to 74% for a cluster of 4 PCs) are also recorded.
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Communication dans un congrès
the IADIS International Conference Applied Computing 2010, Oct 2010, Timisoara, Romania. pp.167-174, 2010
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https://hal.inria.fr/inria-00524974
Contributeur : Maha Idrissi Aouad <>
Soumis le : dimanche 10 octobre 2010 - 01:18:40
Dernière modification le : mercredi 14 mars 2018 - 16:40:45

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  • HAL Id : inria-00524974, version 1

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Maha Idrissi Aouad, Lhassane Idoumghar, René Schott, Olivier Zendra. Sequential and Distributed Hybrid GA-SA Algorithms for Energy Optimization in Embedded Systems. the IADIS International Conference Applied Computing 2010, Oct 2010, Timisoara, Romania. pp.167-174, 2010. 〈inria-00524974〉

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