GAACO: A GA + ACO Hybrid for Faster and Better Search Capability

Abstract

Considering the similarities and characteristics differences between ant colony optimization (ACO) and evolutionary genetic algorithms (GAs), a novel hybrid algorithm combining the search capabilities of the two metaheuristics, for faster and better search capabilities, is introduced. In the GAACO approach, ACO and GAs use identical problem representations and they run in parallel. Migration occurs between the two algorithms whenever any of the them finds an improved potential solution after an iteration. Migration provides further intensification capabilities to both of the algorithms other than their own search mechanisms. In this respect, GAs support ACO by strengthening potential search alternatives for artificial ants and ACO supports GAs by exporting promising potential solutions into its population. The developed algorithm is tested on the solution of two NP-hard combinatorial optimization problems, the obtained results outperform those obtained by both of the individual algorithms when applied alone.