Exploring Social Dynamics in a Reinforcement Learning-based Metaheuristic: A study using Improvement Frequency and Population Turnover

Resumo

Swarm Intelligence (SI) metaheuristics have been extensively employed in optimisation, yet their social dynamics analysis has not been explored sufficiently. This paper addresses this gap by investigating inner dynamics in an RL-based metaheuristic using two recently proposed metrics, Improvement Frequency and Population Turnover. The goal is to reveal the underlying dynamics that rule the agents of the metaheuristic compared with other well-known metaheuristics. It was observed that the search behaviours exhibited by the metaheuristics used were closely related to the specific benchmark problems used in the experiments. Furthermore, it was noted that the combination of diversified behaviours typically yields superior outcomes than a singular search behaviour in the simulation process.