Reduction of Energy Consumption in Embedded Systems: A Hybrid Evolutionary Algorithm

Maha Idrissi Aouad; Lhassane Idoumghar; René Schott; Olivier Zendra

inria-00524975, version 1

Reduction of Energy Consumption in Embedded Systems: A Hybrid Evolutionary Algorithm

Maha Idrissi Aouad ( Auteur à contacter de préférence ) ¹, Lhassane Idoumghar () ², René Schott () ^3, 4, Olivier Zendra () ¹

META'10 - 3rd International Conference on Metaheuristics and Nature Inspired Computing 95 (2010)

Résumé : In this paper, we propose a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and on Simulated Annealing (SA) for reducing memory energy consumption in embedded systems. Our hybrid algorithm outperforms the Tabu Search (TS) approach. In fact, nearly from 76% up to 98% less energy consumption is recorded.

1 : TRIO (INRIA Lorraine - LORIA)
INRIA – CNRS : UMR7503 – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)
2 : Laboratoire de Mathématiques Informatique et Applications (LMIA)
Université de Haute Alsace - Mulhouse
3 : Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA)
INRIA – CNRS : UMR7503 – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)
4 : Institut Elie Cartan Nancy (IECN)
CNRS : UMR7502 – INRIA – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)

inria-00524975, version 1
http://hal.inria.fr/inria-00524975
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Contributeur : Maha Idrissi Aouad <>
Soumis le : Dimanche 10 Octobre 2010, 01:39:44
Dernière modification le : Mardi 2 Novembre 2010, 16:10:18

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%F inria-00524975, version 1
%0 Conference Proceedings
%U http://hal.inria.fr/inria-00524975
%2 INFO:INFO_ES;INFO:INFO_AI;INFO:INFO_PL;MATH:MATH_OC;INFO:INFO_MO
%T Reduction of Energy Consumption in Embedded Systems: A Hybrid Evolutionary Algorithm
%A Idrissi Aouad, Maha
%A Idoumghar, Lhassane
%A Schott, René
%A Zendra, Olivier
%+ TRIO - INRIA Lorraine - LORIA , Laboratoire de Mathématiques Informatique et Applications - LMIA , Laboratoire Lorrain de Recherche en Informatique et ses Applications - LORIA , Institut Elie Cartan Nancy - IECN
%X In this paper, we propose a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and on Simulated Annealing (SA) for reducing memory energy consumption in embedded systems. Our hybrid algorithm outperforms the Tabu Search (TS) approach. In fact, nearly from 76% up to 98% less energy consumption is recorded.
%2 B.: Hardware/B.3: MEMORY STRUCTURES/B.3.2: Design Styles/B.3.2.1: Cache memories, B.: Hardware/B.3: MEMORY STRUCTURES/B.3.1: Semiconductor Memories/B.3.1.2: Static memory (SRAM), G.: Mathematics of Computing/G.2: DISCRETE MATHEMATICS/G.2.1: Combinatorics/G.2.1.0: Combinatorial algorithms, I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.8: Problem Solving, Control Methods, and Search/I.2.8.3: Graph and tree search strategies, G.: Mathematics of Computing/G.1: NUMERICAL ANALYSIS/G.1.6: Optimization/G.1.6.9: Simulated annealing, D.: Software/D.3: PROGRAMMING LANGUAGES/D.3.4: Processors/D.3.4.5: Memory management (garbage collection), I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.8: Problem Solving, Control Methods, and Search/I.2.8.4: Heuristic methods, D.: Software/D.2: SOFTWARE ENGINEERING/D.2.8: Metrics/D.2.8.1: Performance measures, D.: Software/D.1: PROGRAMMING TECHNIQUES/D.1.0: General
%G Anglais
%8 2010-10-00
%2 internationale
%B META'10 - 3rd International Conference on Metaheuristics and Nature Inspired Computing
%2 Djerba
%2 Tunisie
%8 2010-10-27
%8 2010-10-31
%V 95
%K Embedded systems, memory management, optimization, particle swarm optimization, simulated annealing.
%2 1054

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