Sensitive stigmergic agent systems - A hybrid approach to combinatorial optimization

Abstract

Systems composed of several interacting autonomous agents have a huge potential to efficiently address complex real-world problems. Agents communicate by directly exchanging information and knowledge about the environment. To cope with complex combinatorial problems, agents of the proposed model are endowed with stigmergic behaviour. Agents are able to indirectly communicate by producing and being influenced by pheromone trails. Each stigmergic agent has a certain level of sensitivity to the pheromone allowing various types of reactions to a changing environment. Resulting computational metaheuristic combines sensitive stigmergic behaviour and direct agent communication with the aim of better addressing combinatorial optimization NP-hard problems. The proposed model is tested for solving various instances of the Generalized Traveling Salesman Problem. Numerical experiments indicate the robustness and potential of the new metaheuristic. © 2007 Springer-Verlag Berlin Heidelberg.