Hybrid Artificial Intelligent Systems

Volume 8073 of the series Lecture Notes in Computer Science pp 411-420

Second Order Swarm Intelligence

  • Vitorino RamosAffiliated withLaSEEB – Evolutionary Systems and Biomedical Eng. Lab., ISR – Robotic and Systems Institute, Technical University of Lisbon (IST)
  • , David M. S. RodriguesAffiliated withThe Open UniversityThe Observatorium - ISCTE-IUL, Lisbon University Institute (IUL)
  • , Jorge LouçãAffiliated withThe Observatorium - ISCTE-IUL, Lisbon University Institute (IUL)

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An artificial Ant Colony System (ACS) algorithm to solve general-purpose combinatorial Optimization Problems (COP) that extends previous AC models [21] by the inclusion of a negative pheromone, is here described. Several Traveling Salesman Problem (TSP) were used as benchmark. We show that by using two different sets of pheromones, a second-order coevolved compromise between positive and negative feedbacks achieves better results than single positive feedback systems. The algorithm was tested against known NP-complete combinatorial Optimization Problems, running on symmetrical TSPs. We show that the new algorithm compares favorably against these benchmarks, accordingly to recent biological findings by Robinson [26,27], and Grüter [28] where “No entry” signals and negative feedback allows a colony to quickly reallocate the majority of its foragers to superior food patches. This is the first time an extended ACS algorithm is implemented with these successful characteristics.


Self-Organization Stigmergy Co-Evolution Swarm Intelligence Dynamic Optimization Foraging Cooperative Learning Combinatorial Optimization problems Symmetrical Traveling Salesman Problems (TSP)