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Abstract: 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.
Keywords: Ant algorithms, Stigmergy, Dynamic Optimization,
Self-Organization, Swarm Intelligence.
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