Visualizing the population of meta-heuristics during the optimization process using self-organizing maps

This study proposes a novel Visual Data Mining technique based on Self-Organizing Maps (SOM) to visualize the population points of metaheuristic algorithms while they execute their search process. The SOM is used to divide the search space of the optimization function into bi-dimensional regions, allowing one to perform a visual analysis by mapping the points into the 2-dimensional space, in order to compare various executions of the functions performed with different parameter configurations. The use of these maps as a Visual Data Mining tool aims to visually process the resulting data and identify behavioral patterns of the meta-heuristic instances.