Quadratic assignment problem (QAP) is one of fundamental combinatorial optimization problems in many
fields. Many real world applications such as backboard wiring, typewriter keyboard design and scheduling can be formulated
as QAPs. Ant colony algorithm is a multi-agent system inspired by behaviors of real ant colonies to solve optimization
problems. Ant colony optimization (ACO) is one of new bionic optimization algorithms and it has some characteristics
such as parallel, positive feedback and better performances. ACO has achieved in solving quadratic assignment problems.
However, its solution quality and its computation performance need be improved for a large scale QAP. In this paper,
a hybrid ant colony optimization (HACO) has been proposed based on ACO and particle swarm optimization (PSO)
for a large scale QAP. PSO algorithm is combined with ACO algorithm to improve the quality of optimal solutions. Simulation
experiments on QAP standard test data show that optimal solutions of HACO are better than those of ACO for
QAP.
A Hybrid Ant Colony Algorithm for Quadratic Assignment Problem
