Heterogeneous sensitive ant model for combinatorial optimization

A new metaheuristic called Sensitive Ant Model (SAM) for solving combinatorial optimization problems is proposed. SAM improves and extends the Ant Colony System approach by enhancing each agent of the model with properties that induce heterogeneity. SAM agents are endowed with different pheromone sensitivity levels. Highly-sensitive agents are essentially influenced in the decision making process by stigmergic information and thus likely to select strong pheromone-marked moves. Search intensification can be therefore sustained. Agents with low sensitivity are biased towards random search inducing diversity for exploration of the environment. A heterogeneous agent model has the potential to cope with complex and/or dynamic search spaces. Sensitive agents (or ants) allow many types of reactions to a changing environment facilitating an efficient balance between exploration and exploitation.