<p>The application of complex network theory has emerged as a powerful tool for enhancing the understanding and performance of evolutionary algorithms (EAs). This work provides a comprehensive overview of complex network theory’s role in advancing EAs. By modeling the population structures and interactions within various algorithms as networks, deeper insights into their dynamics and behaviors have been gained, leading to more robust and efficient methodologies. The review covers key concepts in complex network theory and their applications in analyzing and improving EAs. Furthermore, the paper discusses the latest advancements in this interdisciplinary field and highlights significant findings and potential future research directions. The importance of integrating complex network theory into the study of EAs is underscored to foster the continuous enhancement of advanced optimization methodologies.</p>