The Quadratic Assignment Problem (QAP) has attracted considerable research efforts due to its importance for a number of real life problems, in addition to its acknowledged difficulty. Almost all of the well-known nature-mimicking algorithms have been applied to solve the QAP. However, the Particle Swarm Optimization (PSO), which has proven to be very effective in various applications, has received little attention at this front. The reason can be ascribed to the Euclidian-distance based learning concept (at the core of the algorithm) which makes PSO, in its present form, unsuitable for combinatorial optimization problems. In this article, a new probability-based approach is proposed for the learning in PSO. Based on this learning concept, a generic framework is developed to discretize PSO and its variants, to make them suitable for combinatorial optimization. Five well-known PSO variants are discretized based on this proposed framework. A comparative study of all discretized PSO variants is also included. Moreover, the proposed framework is compared to other attempts to discretize PSO, in addition to three other meta-heuristic approaches. The comparison revealed that the proposed technique is more effective. (C) 2015 Elsevier Ltd. All rights reserved.