In this paper, we show how an extended Guided Local Search (GLS) can be applied to the Quadratic Assignment Problem (QAP). GLS is a general, penalty-based meta-heuristic, which sits on top of local search algorithms, to help guide them out of local minima. We present empirical results of applying several extended versions of GLS to the QAP, and show that these extensions can improve the range o...

We investigate special cases of the quadratic assignment problem (QAP) where one of the two underlying matrices carries a simple block structure. For the special case where the second underlying matrix is a monotone anti-Monge matrix, we derive a polynomial time result for a certain class of cut problems. For the special case where the second underlying matrix is a product matrix, we identify t...

Quadratic assignment problem (QAP) is a well-known problem in the facility location and layout. It belongs to the NP-complete class. There are many heuristic and meta-heuristic methods, which are presented for QAP in the literature. In this paper, we applied 2-opt, greedy 2-opt, 3-opt, greedy 3-opt, and VNZ as heuristic methods and tabu search (TS), simulated annealing, and pa...

The quadratic assignment problem (QAP) is one of the most studied combinatorial optimization problems with various practical applications. In this paper, we present Breakout Local Search (BLS) for solving QAP. BLS explores the search space by a joint use of local search and adaptive perturbation strategies. Experimental evaluations on the set of QAPLIB benchmark instances show that the proposed...

Inspired by the idea of interacting intelligent agents of a multi-agent system, we introduce a multi-agent based optimization method applied to the quadratic assignment problem (MAOM-QAP). MAOM-QAP is composed of several agents (decision-maker agent, local search agents, crossover agents and perturbation agent) which are designed for the purpose of intensified and diversified search activities....

TAO HUANG: Continuous Optimization Methods for the Quadratic Assignment Problem (Under the direction of Jon W. Tolle) In this dissertation we have studied continuous optimization techniques as they are applied in nonlinear 0-1 programming. Specifically, the methods of relaxation with a penalty function have been carefully investigated. When the strong equivalence properties hold, we are guarant...

This paper discusses efficient parallel algorithms for obtaining strong lower bounds and exact solutions for large instances of the Quadratic Assignment Problem (QAP). Our parallel architecture is comprised of both multi-core processors and Compute Unified Device Architecture (CUDA) enabled NVIDIA Graphics Processing Units (GPUs) on the Blue Waters Supercomputing Facility at the University of I...

Matching one set of objects to another is a ubiquitous task in machine learning and computer vision that often reduces to some form of the quadratic assignment problem (QAP). The QAP is known to be notoriously hard, both in theory and in practice. Here, we investigate if this difficulty can be mitigated when some additional piece of information is available: (a) that all QAP instances of intere...

This paper discusses efficient parallel algorithms for obtaining strong lower bounds and exact solutions for large instances of the Quadratic Assignment Problem (QAP). Our parallel architecture is comprised of both multi-core processors and Compute Unified Device Architecture (CUDA) enabled NVIDIA Graphics Processing Units (GPUs) on the Blue Waters Supercomputing Facility at the University of I...

The inter-cell layout problem is discussed and a mathematical formulation for material flow between the cells is presented. The problem is modeled as a quadratic assignment problem (QAP). An ant algorithm is developed to solve the formulated problem. The performance of the proposed ant algorithm is compared to the facility layout algorithms such as H63, HC63-66, CRAFT and Bubble Search as well ...