Modelling the Social Interactions in Grey Wolf Optimizer

Abstract

Swarm Intelligence has been successfully used for solving high-dimensional and multimodal optimization problems. However, the wide range of swarm-based techniques, operators, and parameters requires prior knowledge before applying them to real-world problems. Because of this, we have been studying the meso-level characteristics that emerge from the social interactions within the swarm to understand each swarm-based technique’s unique characteristics. In this paper, we model and study the interaction network of the Grey Wolf Optimizer (GWO) to capture its social behaviour. We used Portrait divergence to compare the similarity between network structures over experiments, simulations and iterations of the GWO. We also used Kullback divergence to compare the probability distributions of the network flows varying over experiments, simulations and iterations of the GWO. Furthermore, we discovered we could identify the GWO convergence using the interaction network approach. Comparing different simulations, we found that the wolves communicate using a stable network structure but not necessarily a stable network flow indicating variance in the number of highly influential wolves. We also point out patterns found in GWO that appears to be similar to other swarm-based algorithms (GPSO and FSS).