Social impact and optimization

This paper presents a novel stochastic and population-based binary optimization method inspired by social psychology. It is called Social Impact Theory based Optimization (SITO). The method is developed using some simple modifications of simulations of Latané’s Dynamic Social Impact Theory. First, brief summary of some preliminary experiments is described. The results showed that the initial version of SITO performs comparably to the simple Genetic Algorithm and the Binary Particle Swarm Optimization. Furthermore, some interesting properties and aspects of the method are discussed and illustrated, namely the clustering phenomenon known from the dynamic theory of social impact or the introduction of random component into the simulation.