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