  The field of evolutionary many-task optimization (EMaTO) is increasingly
recognized for its ability to streamline the resolution of optimization
challenges with repetitive characteristics, thereby conserving computational
resources. This paper tackles the challenge of crafting efficient knowledge
transfer mechanisms within EMaTO, a task complicated by the computational
demands of individual task evaluations. We introduce a novel framework that
employs a complex network to comprehensively analyze the dynamics of knowledge
transfer between tasks within EMaTO. By extracting and scrutinizing the
knowledge transfer network from existing EMaTO algorithms, we evaluate the
influence of network modifications on overall algorithmic efficacy. Our
findings indicate that these networks are diverse, displaying
community-structured directed graph characteristics, with their network density
adapting to different task sets. This research underscores the viability of
integrating complex network concepts into EMaTO to refine knowledge transfer
processes, paving the way for future advancements in the domain.
