Social impact theory based optimizer

Authors:
Martin Macaš;Lenka Lhotská
Affiliations:
Czech Technical University in Prague, Prague, Czech Republic;Czech Technical University in Prague, Prague, Czech Republic
Venue:
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Year:
2007

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Abstract

This paper introduces a novel stochastic and population-based binary optimization method inspired by social psychology. It is called Social Impact Theory based Optimization (SITO). The method has been developed with the use of some simple modifications of simulations of Latané's Dynamic Social Impact Theory. The usability of the algorithm is demonstrated via experimental testing on some test problems. The results showed that the initial version of SITO performs comparably to the simple Genetic Algorithm (GA) and the binary Particle Swarm Optimization (bPSO).