Incorporating domain-specific heuristics in a particle swarm optimization approach to the quadratic assignment problem

We propose two variations on particle swarm optimization (PSO): the use of a heuristic function as an additional biasing term in PSO solution construction; and the use of a local search step in the PSO algorithm. We apply these variations to the hierarchical PSO model and evaluate them on the quadratic assignment problem (QAP). We compare the performance of our method to diversified-restart robust tabu search (DivTS), one of the leading approaches at present for the QAP. Our experimental results, using instances from the QAPLIB instance library, indicate that our approach performs competitively with DivTS.