AN EXTENSION OF HYBRID GENETIC ALGORITHM FOR THE QUADRATIC ASSIGNMENT PROBLEM

Abstract

Genetic algorithms (GAs) are modern population based heuristic approaches. Recently, GAs have be-come very popular by solving various optimization problems. In this paper, we discuss an extension of a hybrid genetic algorithm for the well-known combinatorial optimization problem, the quadratic assignment problem. This extension is based on a promising genetic-tabu search policy. An enhanced tabu search is used in the role of the local improvement of solutions, whereas a robust mutation (reconstruction) strategy is "responsible" for maintaining a high degree of the diversity within the population and for avoiding a premature convergence of GA. We tested our algorithm on a set of the QAP instances. The results obtained show the outstanding performance of the proposed algorithm.