A novel ACO algorithm for dynamic binary chains based on changes in the system´s stability

In the last decade, Dynamic Optimization Problems (DOP) have received increasing attention. Changes in the problem structure pose a great challenge for the optimization techniques. The Ant Colony Optimization (ACO) metaheuristic has a number of potentials in this field due to its adaptability and flexibility. However their design and analysis are still critical issues. This is where research on formal methods can increase the reliability of these systems and improve the understanding of their dynamics in complex problems such as DOPs. This paper presents a novel ACO algorithm based on an analytical model describing the long-terms behaviours of the ACO systems in problems represented as binary chains, a type of DOP. These behaviours are described using modelling techniques already developed for studying dynamical systems. The algorithm developed takes advantage of new insights offered by this model to regulate the tradeoff of exploration/exploitation resulting in a ACO system able to adapt its long-term behaviours to the problem changes and to improve its performance due to the experiences learnt from the previous explorations. An empirical evaluation is used to validate the algorithm capabilities of adaptability and optimization.