Second Order Swarm Intelligence

  • Vitorino Ramos
  • David M. S. Rodrigues
  • Jorge Louçã
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)

Abstract

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.

Keywords

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

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Vitorino Ramos
    • 1
  • David M. S. Rodrigues
    • 2
    • 3
  • Jorge Louçã
    • 3
  1. 1.LaSEEB – Evolutionary Systems and Biomedical Eng. Lab., ISR – Robotic and Systems InstituteTechnical University of Lisbon (IST)LisbonPortugal
  2. 2.The Open UniversityMilton KeynesUnited Kingdom
  3. 3.The Observatorium - ISCTE-IULLisbon University Institute (IUL)LisbonPortugal

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