Multi-colony ant colony optimization for the node placement problem



In this paper we propose a multi-colony ant colony optimization architecture. In this approach several colonies try to simultaneously solve a problem. Each colony has its own trail and set of control parameters, ensuring that different search strategies are used throughout the optimization. Moreover, the most successful colonies contribute with information that help others with poor behavior to enhance their performance.

A first version of the multi-colony approach was applied to the optimal node placement, an important optimization problem from the network communication field. We report some preliminary results obtained with several benchmark instances. They show that the proposed algorithm is promising, as it was able to outperform a single-colony approach. We conclude this paper with a discussion about research directions for the near future, which are related to a more detailed analysis of the existing architecture and to a number of extensions we are planning to implement and analyze.


ant colony optimization, multi-colony, node placement problem


Evolutionary Optimization


2009 Genetic and Evolutionary Computation Conference (GECCO 2009), July 2009

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Year 2010 : 1 citations

 Courtney, Joshua (2010) Using Ant Colonization Optimization to Control Difficulty in Video Game AI. Undergraduate Honors thesis, East Tennessee State University.