The exploration and exploitation (E&E) of a search space are fundamental processes in many fields of artificial intelligence. Indeed, it is important to ensure that many regions of the search space are examined, so as not to get trapped in a local optimum, but also that the promising regions are examined more in depth in order to find good local optima. Metaheuristics (MHs) are popular to perform E&E tasks. Explaining the dynamics of both processes is a repeated demand of researchers and practitioners, but remains a challenge. This paper presents an approach giving information about how a MH explores and exploits the search space. This approach is based on a way of representing transitions through presence variation vectors and associated proposed indicators, used to make transitions between E&E explicit and intelligible. Experimentations show the relevance of the proposed indicators.