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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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https://hal.inria.fr/inria-00524974
Contributor : Maha Idrissi Aouad <>
Submitted on : Sunday, October 10, 2010 - 1:18:40 AM
Last modification on : Monday, January 6, 2020 - 10:42:03 PM

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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. ⟨inria-00524974⟩

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