Network Visualization of Population Dynamics in the Differential Evolution

The dynamics of populational metaheuristic algorithms, such as the differential evolution, can be represented by evolving complex networks. The differential evolution is a widely-used real parameter optimization method with excellent results and many real-world applications. The search for hidden relationships, behaviors, and patterns in complex networks representing populational metaheuristics can provide an interesting information about the underlying optimization processes. Various methods for visual network investigation and mining became very popular in the last decade and represent a natural set of tools for such analyses. Here, we introduce a new approach for the visual analysis of such network with a special emphasis on network readability. The proposed method is universal and can be applied to any type of complex network modelling any algorithm applied to any problem.