In recent years, many efficient evolutionary multitasking (EMT) algorithms have been proposed to solve multi-objective multi-task optimization problems. However, EMT algorithms often face negative transfer problems. In this paper, a novel multi-objective evolution strategy, called CT-EMT-MOES, is proposed based on a cultural transmission theory for solving multi-objective multitask optimization problems. First, two evolutionary operators inspired by cultural transmission theory are proposed. The elite-guided variation strategy can transfer the information from the current Pareto front to all individuals and guide the population to quickly converge. The horizontal cultural transmission strategy can efficiently transfer information from the source task to the target task. Second, to solve the negative transfer problem, an adaptive information transfer strategy is proposed to adaptively adjust the probability of an information transfer. Third, the proposed algorithm can gain a Pareto front with good convergence and diversity by utilizing a smaller population size and fewer computing resources. As a result, the proposed algorithm can effectively utilize the implicit similarity and complementarity between simultaneous optimized tasks to improve the overall convergence efficiency and reduce a negative transfer. Finally, comprehensive experimental results show that the proposed algorithm can achieve a better performance compared with previous state-of-the-art multi-objective EMT algorithms.

近年来，已经提出了许多有效的进化多任务（EMT）算法来解决多目标多任务优化问题。然而，EMT 算法经常面临负迁移问题。在本文中，基于文化传输理论提出了一种新的多目标进化策略，称为 CT-EMT-MOES，用于解决多目标多任务优化问题。首先，提出了两个受文化传播理论启发的进化算子。精英引导的变异策略可以将当前帕累托前沿的信息传递给所有个体，引导种群快速收敛。横向文化传播策略可以有效地将信息从源任务传递到目标任务。其次，解决负迁移问题，提出了一种自适应信息传递策略来自适应地调整信息传递的概率。第三，该算法利用较小的种群规模和较少的计算资源，可以获得具有良好收敛性和多样性的帕累托前沿。因此，所提出的算法可以有效地利用同时优化任务之间的隐含相似性和互补性来提高整体收敛效率并减少负迁移。最后，综合实验结果表明，与以前最先进的多目标 EMT 算法相比，所提出的算法可以获得更好的性能。该算法利用较小的种群规模和较少的计算资源，可以获得具有良好收敛性和多样性的帕累托前沿。因此，所提出的算法可以有效地利用同时优化任务之间的隐含相似性和互补性来提高整体收敛效率并减少负迁移。最后，综合实验结果表明，与以前最先进的多目标 EMT 算法相比，所提出的算法可以获得更好的性能。该算法利用较小的种群规模和较少的计算资源，可以获得具有良好收敛性和多样性的帕累托前沿。因此，所提出的算法可以有效地利用同时优化任务之间的隐含相似性和互补性来提高整体收敛效率并减少负迁移。最后，综合实验结果表明，与以前最先进的多目标 EMT 算法相比，所提出的算法可以获得更好的性能。