关键词: 进化算法, 进化多任务优化（EMTO）, 迁移学习, 复杂优化问题

Abstract: Due to the escalating complexity of optimization problems，traditional evolutionary algorithms increasinglystruggle with high computational costs and limited adaptability. Evolutionary Multi-Task Optimization（EMTO）algorithms have emerged as a novel solution，leveraging knowledge transfer to tackle multiple optimization issues concurrently，thereby enhancing evolutionary algorithms’efficiency in complex scenarios. The current progression of evolutionary multi-task optimization research was summarized，and different research perspectives were explored by reviewing existing literature and highlighting the notable absence of optimization scenario analysis. By focusing on the application scenarios of optimization problems，the scenarios suitable for evolutionary multitasking optimization and their fundamental solution strategies were systematically outlined. This study thus could aid researchers in selecting the appropriate methods based on specific application needs. Moreover，an in-depth discussion on the current challenges and future directions of EMTO were also presented to provide guidance and insights for advancing research in this field.

Key words: evolutionary algorithm, Evolutionary Multi-Task Optimization (EMTO), transfer learning, complex optimization problem