Team Algorithms Based on Ant Colony Optimization – A New Multi-Objective Optimization Approach

  • Christian Lezcano
  • Diego Pinto
  • Benjamín Barán
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


This paper proposes a novel Team Algorithm (TA) approach based on Ant Colony Optimization (ACO) for multi-objective optimization problems. The proposed method has shown a significant cooperative effect of different algorithms combined in a team of algorithms, achieving robustness in the resolution of a set of various combinatorial problems. Experimentally, the proposed approach has verified a balance on different performance measures in problems as the Traveling Salesman Problem (TSP), the Quadratic Assignment Problem (QAP) and the Vehicle Routing Problem with Time Windows (VRPTW). Robustness and balance are achieved due to a novel classification and selection of the algorithms to be used by the team, considering Pareto concept.


Team Algorithms (TA) Ant Colony Optimization (ACO) and Multi-objective Optimization Problem (MOP) 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Christian Lezcano
    • 1
  • Diego Pinto
    • 1
  • Benjamín Barán
    • 1
  1. 1.Polytechnical SchoolNational University of AsunciónParaguay

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