Abstract

Traditional performance evaluation metrics for metaheuristics focus primarily on the best solution obtained or the computational resources required to solve a problem. However, further valuable insights into algorithmic components and design can be extracted during the search process. This information enhances understanding of algorithmic design, supports algorithm configuration, and informs methodological advancements. This study presents MHMetLib, a unified indicators library for analyzing both trajectory-based and population-based metaheuristics. The library offers indicators for convergence, diversity, and operator behavior. This enables comprehensive behavioral analysis beyond traditional scalar measures. Each indicator is formally defined and details its objective, computational requirements, and application domain. MHMetLib was implemented and evaluated within the IOHProfiler platform. This ensures consistent evaluation of algorithmic behavior across different metaheuristic families and supports comparison, diagnostics, and configuration. MHMetLib is also compatible with other metaheuristic frameworks or custom algorithmic implementations. Its effectiveness is demonstrated using standard benchmark problems such as OneMax, IsingTorus, and NQueens on evolutionary algorithms and simulated annealing. The results highlight the indicators' ability to reveal significant dynamics in metaheuristic search processes.