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Diversity control in ant colony optimization

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Abstract

Optimization inspired by cooperative food retrieval in ants has been unexpectedly successful and has been known as ant colony optimization (ACO) in recent years. One of the most important factors to improve the performance of the ACO algorithms is the complex trade-off between intensification and diversification. This article investigates the effects of controlling the diversity by adopting a simple mechanism for random selection in ACO. The results of computer experiments have shown that it can generate better solutions stably for the traveling salesmen problem than ASrank which is known as one of the newest and best ACO algorithms by utilizing two types of diversity.

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Authors and Affiliations

  1. Graduate School of Human Informatics, Nagoya University, Furo-cho, Chikusa-ku, 464-8601, Nagoya, Japan

    Yoshiyuki Nakamichi

  2. Graduate School of Information Science, Nagoya University, Nagoya, Japan

    Takaya Arita

Authors
  1. Yoshiyuki Nakamichi
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  2. Takaya Arita
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Corresponding author

Correspondence to Yoshiyuki Nakamichi.

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Nakamichi, Y., Arita, T. Diversity control in ant colony optimization. Artificial Life and Robotics 7, 198–204 (2004). https://doi.org/10.1007/BF02471207

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  • DOI: https://doi.org/10.1007/BF02471207

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