Optimizers derived from human opinion formation

摘要

The human opinion formation can be understood as a social approach to optimization. In the real world, the opinions encode a candidate solution, which is evaluated by a complex and unknown fitness function. The computer models of such processes can be slightly modified by introducing a fitness value, which leads to novel family of optimization techniques. This paper demonstrates how the novel algorithms can be derived from opinion formation models and empirically proves their usability in the area of binary optimization. Particularly, it introduces a general SITO algorithmic framework and describes three algorithms based on this general framework - the previously proposed original distance-based (oSITO), the simplified (sSITO) and the Galam inspired (gSITO) algorithm.