Reactive memory model for ant colony optimization and its application to TSP

Ant colony optimization is one of the most successful examples of swarm intelligent systems. The exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is a framework for automating the exploration and exploitation in stochastic algorithms. Restarting the search with the aid of memorizing the search history is the soul of reaction. It is to increase the exploration only when needed. This paper proposes a reactive memory model to overcome the limitation of the random exploration after restart because of losing the previous history of search. The proposed model is utilized to record the previous search regions to be used as reference for ants after restart. The performances of six (6) ant colony optimization variants were evaluated to select the base for the proposed model. Based on the results, Max-Min Ant System has been chosen as the base for the modification. The modified algorithm called RMMAS, was applied to TSPLIB95 data and results showed that RMMAS outperformed the standard MMAS.