For many-objective problems, how to maintain the diversity and convergence of the distribution of the solution set over Pareto front (PF) has always been the research emphasis. In the iteration process, the state of population is critical to improve the level of evolution. Therefore, this paper will use two convergence and diversity indicators to further strengthen the usage of evolutionary state information and propose a dynamic learning strategy. In addition, a dynamic learning strategy based many-objective evolutionary algorithm (MaOEA) is proposed, called dynamic learning evolution algorithm (DLEA), which continuously changes the direction of learning: convergence and diversity in the iteration process. The purpose is to make the algorithm prefer to convergence in the early iteration and prefer to diversity when it is close to PF in the late iteration, so that the convergence and diversity of the final solution set can be well maintained. And then, the performance of DLEA is measured by two indicators. Meanwhile, DLEA will be compared with four state-of-the-art algorithms on the DTLZ and MaF, and its performance will be verified on a many-objective combinatorial problem. And the experimental results and Friedman test show that DLEA has great advantages.

对于多目标问题，如何在帕累托前沿（PF）上保持解集分布的多样性和收敛性一直是研究的重点。在迭代过程中，种群状态对于提高进化水平至关重要。因此，本文将使用两个收敛性和多样性指标来进一步加强进化状态信息的使用，并提出动态学习策略。此外，提出了一种基于动态学习策略的多目标进化算法（MaOEA），称为动态学习进化算法（DLEA），在迭代过程中不断改变学习方向：收敛性和多样性。目的是使算法在迭代早期更倾向于收敛，在迭代后期接近PF时更倾向于多样性，从而能够很好地保持最终解集的收敛性和多样性。然后，DLEA 的性能由两个指标来衡量。同时，DLEA 将在 DTLZ 和 MaF 上与四种最先进的算法进行比较，并在多目标组合问题上验证其性能。并且实验结果和弗里德曼检验表明DLEA具有很大的优势。其性能将在多目标组合问题上得到验证。并且实验结果和弗里德曼检验表明DLEA具有很大的优势。其性能将在多目标组合问题上得到验证。并且实验结果和弗里德曼检验表明DLEA具有很大的优势。