Randomization and Complex Networks for Meta-Heuristic Algorithms

Abstract

This chapter deals with the hybridization of the chaos driven heuristics concept and complex networks framework for meta-heuristic. This research aims on the experimental investigations on the time development and influence of different randomization types, different strategies for Differential Evolution (DE) through the analysis of complex network as a record of population dynamics and indices selection. The population is visualized as an evolving complex network, which exhibits non-trivial features such as adjacency graph, centralities, clustering coefficient and other attributes showing efficiency of the network. Experiments were performed for different DE strategies, several different randomization types and simple test functions.