Stigmergic optimization in dynamic binary landscapes
Carlos M. C. Fernandes, Vitorino Ramos, Agostinho C. Rosa · 2007
Hereafter we introduce a novel algorithm for optimization in dynamic binary landscapes. The Binary Ant Algorithm (BAA) mimics some aspects of real social insects' behavior. Like Ant Colony Optimization (ACO), BAA acts by building pheromone maps over a grid of possible trails that represent solutions to an optimization problem. Main differences rely on the way this search space is represented and provided to the colony in order to explore/exploit it. Then, by a process of pheromone reinforcement and evaporation the artificial insect trails converge to regions near the problem solution or extrema. The negative feedback granted by the evaporation mechanism provides the self-organized system with population diversity and self-adaptive characteristics, allowing BAA to be particularly suitable for hard Dynamic Optimization Problems (DOP), where extrema continuously changes at severe speeds.