Ant colony optimization with a genetic restart approach toward global optimization

Abstract

Ant Colony Optimization (ACO) a nature-inspired metaheuristic algorithm has been successfully applied in the traveling salesman problem (TSP) and a variety of combinatorial problems. In fact, ACO can effectively fit to discrete optimization problems and exploit pre-knowledge of the problems for a faster convergence. We present an improved version of ACO with a kind of Genetic semi-random-restart to solve Multiplicative Square Problem which is an ill-conditioned NP-hard combinatorial problem and demonstrate its ability to escape from local optimal solutions. The results show that our approach appears more efficient in time and cost than the solitary ACO algorithms. © 2008 Springer-Verlag.