A Particle Swarm Approach to Quadratic Assignment Problems

Particle Swarm Optimization (PSO) algorithm has exhibited good performance across a wide range of application problems. But research on the Quadratic Assignment Problem (QAP) has not much been investigated. In this paper, we introduce a novel approach based on PSO for QAPs. The representations of the position and velocity of the particles in the conventional PSO is extended from the real vectors to fuzzy matrices. A new mapping is proposed between the particles in the swarm and the problem space in an efficient way. We evaluate the performance of the proposed approach with Ant Colony Optimization (ACO) algorithm. Empirical results illustrate that the approach can be applied for solving quadratic assignment problems and it has outperforms ACO in the completion time.