Multistart tabu search and diversification strategies for the quadratic assignment problem

  • Authors:

  • Tabitha James;César Rego;Fred Glover

  • Affiliations:

  • Department of Business Information Technology, Pamplin College of Business, Virginia Polytechnic Institute and State University, Blacksburg, VA;School of Business, University of Mississippi, Oxford, MS;University of Colorado at Boulder, Boulder, CO and OptTek Systems, Inc., Boulder, CO

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

The quadratic assignment problem (QAP) is a wellknown combinatorial optimization problem with a wide variety of applications, prominently including the facility location problem. The acknowledged difficulty of the QAP has made it the focus of many metaheuristic solution approaches. In this paper, we show the benefit of utilizing strategic diversification within the tabu search (TS) framework for the QAP, by incorporating several diversification and multistart TS variants. Computational results for an extensive and challenging set of QAP benchmark test problems demonstrate the ability of our TS variants to improve on a classic TS approach that is one of the principal and most extensively used methods for the QAP. We also show that our new procedures are highly competitive with the best recently introduced methods from the literature, including more complex hybrid approaches that incorporate the classic TS method as a subroutine.