Adaptive genetic algorithm for energy-efficient task scheduling on asymmetric multiprocessor system-on-chip

Abstract This paper proposes a genetic algorithm (GA) based energy-efficient design-time task scheduling algorithm, AGATS, for an asymmetric multiprocessor system-on-chip. Unlike existing GA-based task scheduling algorithms, AGATS adaptively applies different generation strategies to solution candidates based on their completion time and energy consumption. For solution candidates to evolve intelligently, instead of using conventional genetic operators, AGATS uses three generation strategies: elitism, mutation of elites (MOE), and adaptive generation (AG). The first copies a small portion of elite solution candidates into the next generation to guarantee that solution quality does not decrease from the current to the next generation. The second mutates randomly selected elite solution candidates to maintain both the diversity of candidates and solution quality. Finally, the third adaptively evolves solution candidates toward better candidates based on their completion time and energy consumption. In experiments, AGATS reduced energy consumption by up to 29.3% compared to existing methods and outperformed them in most cases. Furthermore, it identified feasible solutions effectively, which was not the case with the existing methods under tight timing constraints.