Informatica

In this paper, an efficient hybrid genetic algorithm (HGA) and its variants for the well-known combinatorial optimization problem, the quadratic assignment problem (QAP) are discussed. In particular, we tested our algorithms on a special type of QAPs, the structured quadratic assignment problems. The results from the computational experiments on this class of problems demonstrate that HGAs allow to achieve near-optimal and (pseudo-)optimal solutions at very reasonable computation times. The obtained results also confirm that the hybrid genetic algorithms are among the most suitable heuristic approaches for this type of QAPs.

In this paper we present an algorithm for generating quadratic assignment problem (QAP) instances with known provably optimal solution. The flow matrix of such instances is constructed from the matrices corresponding to special graphs whose size may reach the dimension of the problem. In this respect, the algorithm generalizes some existing algorithms based on the iterative selection of triangles only. The set of instances which can be produced by the algorithm is NP-hard. Using multi-start descent heuristic for the QAP, we compare experimentally such test cases against those created by several existing generators and against Nugent-type problems from the QAPLIB as well.

Many heuristics, such as simulated annealing, genetic algorithms, greedy randomized adaptive search procedures are stochastic. In this paper, we propose a deterministic heuristic algorithm, which is applied to the quadratic assignment problem. We refer this algorithm to as intensive search algorithm (or briefly intensive search). We tested our algorithm on the various instances from the library of the QAP instances – QAPLIB. The results obtained from the experiments show that the proposed algorithm appears superior, in many cases, to the well-known algorithm – simulated annealing.

In this paper we are concerned with global optimization, which can be defined as the problem of finding points on a bounded subset of R^m, in which some real-valued function f(x) assumes its optimal value. We consider here a global optimization algorithm. We present a stochastic approach, which is based on the simulated annealing algorithm. The optimization function f(x) here is discrete and with noise.

It is well known that, in general, exact algorithms for the Quadratic Assignment Problem (QAP) cannot solve problems of size N>15. Therefore, it is necessary to use heuristic approaches for solving large-scale QAPs. In this paper, we consider a class of heuristic approaches based on local search criteria. In particular, we selected four algorithms; CRAFT, Simulated Annealing, TABU search and the Graph Partitioning (GP) approach and studied their relative performance in terms of the quality of solutions and CPU times. All of these algorithms performed roughly the same, based on the results of two sets of test problems executed on an IBM ES/3090-600S computer.