In this paper, we show that the quadratic assignment problem (QAP) can be reformulated to an equivalent rank constrained doubly nonnegative (DNN) problem. Under framework of difference convex functions (DC) approach, a semi-proximal DC algorithm is proposed for solving relaxation DNN whose subproblems solved by augmented Lagrangian method. We generated sequence converges stationary point corres...

The Antlion Optimization (ALO) algorithm is a meta-heuristic optimization based on the hunting of ants by antlions. basic inadequacy this that it has long run time because random walk model used for ant's movement. We improved some mechanisms in ALO algorithm, such as ants' walking, reproduction, sliding towards antlion, elitism, and selection procedure. This proposed called Improved (IALO) alg...

A greedy randomized adaptive search procedure (GRASP) is a randomized heuristic that has been shown to quickly produce good quality solutions for a wide variety of combinatorial optimization problems. In this paper, we describe a GRASP for the quadratic assignment problem. We review basic concepts of GRASP: construction and local search algorithms. The implementation of GRASP for the quadratic ...

We describe a branch and bound algorithm for the quadratic assignment problem QAP that uses a convex quadratic programming QP relaxation to obtain a bound at each node The QP subproblems are approximately solved using the Frank Wolfe algorithm which in this case requires the solution of a linear assignment problem on each iteration Our branching strategy makes extensive use of dual information ...

In this paper we present an extension of MAX{MIN Ant System applying it to Traveling Salesman Problems and Quadratic Assignment Problems. The extension involves the use of a modiied choice rule and a hybrid scheme allowing ants to improve their solution by local search. The computational results show that this algorithm can be used to eeciently nd near optimal solutions to hard combinatorial op...