Abstract

AbstractThe behavior of Ant Colony Optimization (ACO) algorithms is based on a collective learning process defined by a pheromone mechanism. This process is difficult to understand due to the scheduling of pheromone deposition and evaporation, the influence of parameter values, the bias in solution construction, and the problem instance size. This work aims to extend Local Optima Networks (LONs) to understand the path traversed by the pheromone learning mechanism in the fitness landscape described by ACO algorithms. Our ant-based LONs incorporate a definition of network edges that expresses the persistence of the pheromone influence in the search process. Also, we study a simplified network, Deposit LON, that contains only nodes that deposit pheromones. We aim to analyze a population-based search algorithm’s exploitation and exploration behavior through its network features. We evaluate our …