Abstract
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.
Highlights
Quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem and it has been applied to many applications such as campus planning, hospital distribution, integrated circuits arrangement, job-shop scheduling and the selection of cooperative partners in virtual manufacturing circumstances [1]
Thomas presents a maxmin ant system based on ant colony optimization, which exploits more strongly the best solutions found during the search, directs the ants’ search towards solutions in high quality and avoids premature convergence of ants’ search [6]
It consisted of virtually all QAP instances that are accessible to authors at any time
Summary
QAP is an NP-hard combinatorial optimization problem and it has been applied to many applications such as campus planning, hospital distribution, integrated circuits arrangement, job-shop scheduling and the selection of cooperative partners in virtual manufacturing circumstances [1]. Bullnheimer proposes a parallel ant colony optimization algorithm where an information exchange between several colonies of ants is done every k generations [7]. Through simulation experiments, he shows how much the running. Particle swarm optimization (PSO) is firstly intended for simulating social behaviors that a bird flock search for foods and it is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality [10]. A hybrid ant colony algorithm is proposed for solving QAP.
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