Biological computation of the solution to the quadratic assignment problem

Biological computing provides a promising approach to attacking computationally intractable problems. The quadratic assignment problem (QAP) is a well-known NP-hard combinatorial optimization problem. This paper addresses the problem of how to solve QAP under the Adleman–Lipton-sticker model. A theoretically efficient DNA algorithm for solving QAP is proposed, which is executed by performing O(Kn⁴) operations on test tubes of DNA molecular strands with n² + K + 1 bit regions, where n is the number of facilities, and K is the length of the binary representation of an upper bound on the objective function. With the rapid progress of molecular biology techniques, the proposed algorithm might be of practical use in treating medium-sized instances of QAP.     

Autocorrelation measures for the quadratic assignment problem

In this article we provide an exact expression for computing the autocorrelation coefficient ξ and the autocorrelation length ? of any arbitrary instance of the Quadratic Assignment Problem (QAP) in polynomial time using its elementary landscape decomposition. We also provide empirical evidence of the autocorrelation length conjecture in QAP and compute the parameters ξ and ? for the 137 instances of the QAPLIB. Our goal is to better characterize the difficulty of this important class of problems to ease the future definition of new optimization methods. Also, the advance that this represents helps to consolidate QAP as an interesting and now better understood problem.     

Ant colonies for the quadratic assignment problem

This paper presents HAS–QAP, a hybrid ant colony system coupled with a local search, applied to the quadratic assignment problem. HAS–QAP uses pheromone trail information to perform modifications on QAP solutions, unlike more traditional ant systems that use pheromone trail information to construct complete solutions. HAS–QAP is analysed and compared with some of the best heuristics available for the QAP: two versions of tabu search, namely, robust and reactive tabu search, hybrid genetic algorithm, and a simulated annealing method. Experimental results show that HAS–QAP and the hybrid genetic algorithm perform best on real world, irregular and structured problems due to their ability to find the structure of good solutions, while HAS–QAP performance is less competitive on random, regular and unstructured problems.     

Lower bounds for the quadratic assignment problem

We investigate the classical Gilmore-Lawler lower bound for the quadratic assignment problem. We provide evidence of the difficulty of improving the Gilmore-Lawler bound and develop new bounds by means of optimal reduction schemes. Computational results are reported indicating that the new lower bounds have advantages over previous bounds and can be used in a branch-and-bound type algorithm for the quadratic assignment problem.     

A survey for the quadratic assignment problem

The quadratic assignment problem (QAP), one of the most difficult problems in the NP-hard class, models many real-life problems in several areas such as facilities location, parallel and distributed computing, and combinatorial data analysis. Combinatorial optimization problems, such as the traveling salesman problem, maximal clique and graph partitioning can be formulated as a QAP. In this paper, we present some of the most important QAP formulations and classify them according to their mathematical sources. We also present a discussion on the theoretical resources used to define lower bounds for exact and heuristic algorithms. We then give a detailed discussion of the progress made in both exact and heuristic solution methods, including those formulated according to metaheuristic strategies. Finally, we analyze the contributions brought about by the study of different approaches.     

Gilmore-Lawler bound of quadratic assignment problem

The Gilmore-Lawler bound (GLB) is one of the well-known lower bound of quadratic assignment problem (QAP). Checking whether GLB is tight is an NP-complete problem. In this article, based on Xia and Yuan linearization technique, we provide an upper bound of the complexity of this problem, which makes it pseudo-polynomial solvable. We also pseudopolynomially solve a class of QAP whose GLB is equal to the optimal objective function value, which was shown to remain NP-hard.      

The Wiener maximum quadratic assignment problem

We investigate a special case of the maximum quadratic assignment problem where one matrix is a product matrix and the other matrix is the distance matrix of a one-dimensional point set. We show that this special case, which we call the Wiener maximum quadratic assignment problem, is NP-hard in the ordinary sense and solvable in pseudo-polynomial time.Our approach also yields a polynomial time solution for the following problem from chemical graph theory: find a tree that maximizes the Wiener index among all trees with a prescribed degree sequence. This settles an open problem from the literature.     

Compounded genetic algorithms for the quadratic assignment problem

We introduce the compounded genetic algorithm. We propose to run a quick genetic algorithm several times as Phase 1, and compile the best solutions in each run to create a starting population for Phase 2. This new approach was tested on the quadratic assignment problem with very good results.     

An algorithm for the generalized quadratic assignment problem

This paper reports on a new algorithm for the Generalized Quadratic Assignment problem (GQAP). The GQAP describes a broad class of quadratic integer programming problems, wherein M pair-wise related entities are assigned to N destinations constrained by the destinations’ ability to accommodate them. This new algorithm is based on a Reformulation Linearization Technique (RLT) dual ascent procedure. Experimental results show that the runtime of this algorithm is as good or better than other known exact solution methods for problems as large as M=20 and N=15. Current address of P.M. Hahn: 2127 Tryon Street, Philadelphia, PA 19146-1228, USA.     

Ranking scalar products to improve bounds for the quadratic assignment problem

The eigenvalue bound for the quadratic assignment problem (QAP) is successively improved by considering a set of k-best scalar products, related to the QAP. An efficient procedure is proposedto find such a set of k-best scalar products. A class of QAPs is described for which this procedure in general improves existing lower bounds and at the same time generates good suboptimal solutions. The method leaves the user with a large flexibility in controlling the quality of the bound. However, since the method is sensitive to input data it should only be used in combination with other bounding rules.     

GRASP with path-relinking for the generalized quadratic assignment problem

The generalized quadratic assignment problem (GQAP) is a generalization of the NP-hard quadratic assignment problem (QAP) that allows multiple facilities to be assigned to a single location as long as the capacity of the location allows. The GQAP has numerous applications, including facility design, scheduling, and network design. In this paper, we propose several GRASP with path-relinking heuristics for the GQAP using different construction, local search, and path-relinking procedures. We introduce a novel approximate local search scheme, as well as a new variant of path-relinking that deals with infeasibilities. Extensive experiments on a large set of test instances show that the best of the proposed variants is both effective and efficient.     

A quadratic assignment formulation of the molecular conformation problem

The molecular conformation problem is discussed, and a concave quadratic global minimization approach for solving it is described. This approach is based on a quadratic assignment formulation of a discrete approximation to the original problem.     

Copositive and semidefinite relaxations of the quadratic assignment problem

Semidefinite relaxations of the quadratic assignment problem ($QAP$) have recently turned out to provide good approximations to the optimal value of $QAP$. We take a systematic look at various conic relaxations of $QAP$. We first show that $QAP$ can equivalently be formulated as a linear program over the cone of completely positive matrices. Since it is hard to optimize over this cone, we also look at tractable approximations and compare with several relaxations from the literature. We show that several of the well-studied models are in fact equivalent. It is still a challenging task to solve the strongest of these models to reasonable accuracy on instances of moderate size.     

Massively parallel tabu search for the quadratic assignment problem

A new heuristic algorithm to perform tabu search on the Quadratic Assignment Problem (QAP) is developed. A massively parallel implementation of the algorithm on the Connection Machine CM-2 is provided. The implementation usesn ² processors, wheren is the size of the problem. The elements of the algorithm, calledPar_tabu, include dynamically changing tabu list sizes, aspiration criterion and long term memory. A new intensification strategy based on intermediate term memory is proposed and shown to be promising especially while solving large QAPs. The combination of all these elements gives a very efficient heuristic for the QAP: the best known or improved solutions are obtained in a significantly smaller number of iterations than in other comparative studies. Combined with the implementation on CM-2, this approach provides suboptimal solutions to QAPs of bigger dimensions in reasonable time.     

A new greedy algorithm for the quadratic assignment problem

The classical greedy algorithm for discrete optimization problems where the optimal solution is a maximal independent subset of a finite ground set of weighted elements, can be defined in two ways which are dual to each other. The Greedy-In where a solution is constructed from the empty set by adding the next best element, one at a time, until we reach infeasibility, and the Greedy-Out where starting from the ground set we delete the next worst element, one at a time, until feasibility is reached. It is known that while the former provides an approximation ratio for maximization problems, its worst case performance is not bounded for minimization problems, and vice-versa for the later. However the Greedy-Out algorithm requires an oracle for checking the existence of a maximal independent subset which for most discrete optimization problems is a difficult task. In this work we present a Greedy-Out algorithm for the quadratic assignment problem by providing a combinatorial characterization of its solutions.     

A heuristic procedure for solving the quadratic assignment problem

An heuristic approach to the solution of the quadratic assignment problem is presented. A simple procedure is used to get a good feasible starting point, then the problem is solved as a nonlinear program (ignoring the integrality conditions) using MINOS, and lastly the near integer solution is converted into an integer feasible solution using an heuristic procedure. The results compare favourably with other procedures in the literature. A superior solution to the 19 × 19 hospital layout problem is found.     

Applications of parametric programming and eigenvalue maximization to the quadratic assignment problem

We investigate new bounding strategies based on different relaxations of the quadratic assignment problem. In particular, we improve the lower bound found by using an eigenvalue decomposition of the quadratic part and by solving a linear program for the linear part. The improvement is accomplished by applying a steepest ascent algorithm to the sum of the two bounds.Both authors would like to thank the Natural Sciences and Engineering Research Council Canada and the Austrian Government for their support.This author would like to acknowledge partial support from the Department of Combinatorics and Optimization at the University of Waterloo.Research partially supported by Natural Sciences and Engineering Research Council Canada.     

A new bound for the quadratic assignment problem based on convex quadratic programming

We describe a new convex quadratic programming bound for the quadratic assignment problem (QAP). The construction of the bound uses a semidefinite programming representation of a basic eigenvalue bound for QAP. The new bound dominates the well-known projected eigenvalue bound, and appears to be competitive with existing bounds in the trade-off between bound quality and computational effort. Received: February 2000 / Accepted: November 2000?Published online January 17, 2001     

Bounds for the quadratic assignment problem using the bundle method

Semidefinite programming (SDP) has recently turned out to be a very powerful tool for approximating some NP-hard problems. The nature of the quadratic assignment problem (QAP) suggests SDP as a way to derive tractable relaxations. We recall some SDP relaxations of QAP and solve them approximately using a dynamic version of the bundle method. The computational results demonstrate the efficiency of the approach. Our bounds are currently among the strongest ones available for QAP. We investigate their potential for branch and bound settings by looking also at the bounds in the first levels of the branching tree.      

A note on asymptotic properties of the quadratic assignment problem

We sharpen and simplify previous work of Frenk, Van Houweninge and Rinnooy Kan on the stochastic version of the quadratic assignment problem.