Fast QAP Solving by ACO with 2-opt Local Search on a GPU

This paper proposes a parallel ant colony optimization (ACO) for solving quadratic assignment problems (QAPs) on a graphics processing unit (GPU) by combining fast, 2-opt local search in compute unified device architecture (CUDA). In 2-opt for QAP, 2-opt moves can be divided into two groups based on computing cost. In one group, the computing cost is O(1) and in the other group, the computing cost is O(n). We compute these groups of 2-opt moves in parallel by assigning the computations to threads of CUDA. In this assignment, we propose an efficient method that can reduce disabling time in each thread of CUDA. The results show GPU computation with 2-opt produces a speedup of x24.6 on average, compared to computation with CPU.