Ant colony optimization theory: a survey
Theoretical Computer Science
Stigmergic Optimization (Studies in Computational Intelligence)
Stigmergic Optimization (Studies in Computational Intelligence)
Varying the population size of artificial foraging swarms on time varying landscapes
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Introducing a binary ant colony optimization
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
The differential ant-stigmergy algorithm applied to dynamic optimization problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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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.