TABLE OF CONTENTS Sergio Alonso, University of Granada, Spain
Oscar Cord n, University of Granada, Spain
I aki Fern ndez de Viana, Universidad de Huelva, Spain
Francisco Herrera, University of Granada, Spain
This chapter introduces two different ways to integrate Evolutionary Computation Components in Ant Colony Optimization (ACO) Meta-heuristic. First of all, the ACO meta-heuristic is introduced and compared to Evolutionary Computation to notice their similarities and differences. Then two new models of ACO algorithms that include some Evolutionary Computation concepts (Best-Worst Ant System and exchange of memoristic information in parallel ACO algorithms) are presented with some empirical results that show improvements in the quality of the solutions when compared with more basic and classical approaches.
Complex combinatorial optimization problems arise in many different fields such as economy, commerce, engineering or industry. These problems are so complex that there is no algorithm known that solves them in polynomial time (Garey & Johnson, 1979). These kinds of problems are called NP-hard.
Still, many of these problems have to be solved in a huge number of practical settings and therefore a large number of algorithmic approaches were proposed to tackle them. The existing techniques can roughly be classified into exact and approximate algorithms. Exact algorithms try to find an optimal solution and to prove that the solution obtained is actually an optimal one. These algorithms include techniques such as backtracking, branch and bound, dynamic programming, and so forth (Brassard & Bratley, 1996; Papadimitriou & Steiglitz, 1982). Because exact algorithms show poor performance for many...
