A hybrid Big Bang-Big Crunch algorithm for energy optimization on heterogeneous distributed system

Abstract

A hybrid algorithm is presented for global optimization of the energy consumption rather than makespan of the system. The cost profile is optimized by extending the execution time of the tasks on dynamic voltage scalable processing elements in embedded environment with meeting the execution constraints. The modified Big Bang Big Crunch (BBBC) method improves the scheduling efficiency of the tasks by simulating one of the theories of the evolution of the universe. BBBC algorithm generates disorder data points inspired by energy dissipation procedure of the Big Bang phase, and moves those data points to a single representative data point inspired by a center of mass cost approach in the Big Crunch phase. The Logistic and Sinusoidal chaotic maps are investigated and utilized to improve the movement step of the BBBC algorithm. The proposed hybrid algorithm is tested on several benchmark data sets and its performance is compared with those of Ant Colony Optimization and HEFT strategies. The simulation experiment shows that the presented evolutionary optimization algorithm is robust and suitable for energy saving.