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Optimizing Society: The Social Impact Theory Based Optimizer
Abstract
A novel binary optimization technique is introduced called Social Impact Theory based Optimizer (SITO), which is based on social psychology model of social interactions. The algorithm is based on society of individuals. Each individual holds a set of its attitudes, which encodes a candidate solution of a binary optimization problem. Individuals change their attitudes according to their spatial neighbors and their fitness, which leads to convergence to local (or global) optimum. This chapter also tries to demonstrate different aspects of the SITO’s behavior and to give some suggestions for potential user. Further, a comparison to similar techniques – genetic algorithm and binary particle swarm optimizer – is discussed and some possibilities of formal analysis are briefly presented.
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