Using Entropy for Evaluating Swarm Intelligence Algorithms

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.