Fast Hybrid BSA-DE-SA Algorithm on GPU

This paper introduces a hybridization of Backtracking Search Optimization Algorithm (BSA) with Differential Evolution (DE) and Simulated Annealing (SA) in order to improve the convergence speed of BSA. An experimental study, conducted on 20 benchmark problems, shows that this approach outperforms BSA and two other hybridizations [4, 18], in terms of solution quality and convergence speed. We also describe our CUDA implementation of this algorithm for graphics processing unit (GPU). Experimental results are reported for 10 high-dimensional benchmark problems, and it highlights that significant speedup can be achieved.