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Evolving Ant Colony Optimization

Author

Listed:
  • Hozefa M. Botee

    (Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA)

  • Eric Bonabeau

    (Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA)

Abstract

Ant Colony Optimization (ACO) is a promising new approach to combinatorial optimization. Here ACO is applied to the traveling salesman problem (TSP). Using a genetic algorithm (GA) to find the best set of parameters, we demonstrate the good performance of ACO in finding good solutions to the TSP.

Suggested Citation

  • Hozefa M. Botee & Eric Bonabeau, 1998. "Evolving Ant Colony Optimization," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 1(02n03), pages 149-159.
    • Handle: RePEc:wsi:acsxxx:v:01:y:1998:i:02n03:n:s0219525998000119
    DOI: 10.1142/S0219525998000119
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    Cited by:

    1. Vaisakh, K. & Srinivas, L.R., 2010. "A genetic evolving ant direction DE for OPF with non-smooth cost functions and statistical analysis," Energy, Elsevier, vol. 35(8), pages 3155-3171.
    2. Sabuncuoglu, Ihsan & Erel, Erdal & Alp, Arda, 2009. "Ant colony optimization for the single model U-type assembly line balancing problem," International Journal of Production Economics, Elsevier, vol. 120(2), pages 287-300, August.
    3. Scianna, Marco, 2024. "The AddACO: A bio-inspired modified version of the ant colony optimization algorithm to solve travel salesman problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 218(C), pages 357-382.

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