Abstract
In the last few years, the bio-inspired community has experienced a growing interest in the field of Swarm Intelligence algorithms applied to real world problems. In spite of the large number of algorithms using this approach, a few methodologies exist for evaluating the properties of self-organizing and the effectiveness in using these kinds of algorithm. This paper presents an entropy-based model that can be used to evaluate self-organizing properties of Swarm Intelligence algorithms and its application to SPARROW-SNN, an adaptive flocking algorithm used for performing approximate clustering. Preliminary experiments, performed on a synthetic and a real-world data set confirm the presence of self-organizing characteristics differently from the classical flocking algorithm.
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Folino, G., Forestiero, A. (2010). Using Entropy for Evaluating Swarm Intelligence Algorithms. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol 284. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12538-6_28
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DOI: https://doi.org/10.1007/978-3-642-12538-6_28
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