Complex network analysis of differential evolution algorithm applied to flowshop with no-wait problem

Abstract

This paper analyses the attributes of population dynamics of Differential Evolution algorithm using Complex Network Analysis tools. The population is visualised as an evolving complex network, which exhibits non-trivial features. Complex network attributes such as adjacency graph gives interconnectivity, centralities give the overview of convergence and stagnation, whereas cliques outlines the depth of interconnection and subgraphs within the population. The community graph plot gives an overview of the hierarchical grouping of the individuals in the population. These attributes give a clear description of the population during evaluation and can be utilised for adaptive population and parameter control.