Multiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

由于选择了不合适的领导者和效率低下的进化策略，多目标粒子群算法（MOPSO）面临着过早的困难和多样性不足的问题。因此，为了避免MOPSO中人口多样性的快速丧失和过早收敛，本文提出了一种使用融合学习策略（KGMOPSO）的知识导向的多目标粒子群优化算法，其中提出了一种基于知识利用的改进型领导者选择策略。适当的全局领导者，以提高算法的收敛能力。此外，动态地测量了不同个体之间的相似性，以检测当前人群的多样性，提出了一种增强多样性的学习策略，以防止人口多样性的迅速丧失。另外，采用最大和最小拥挤距离策略来获得出色的非支配解决方案。通过与ZDT和DTLZ测试实例上现有的最新多目标优化算法进行比较，对提出的KGMOPSO算法进行了评估。实验结果表明，在解决方案质量和多样性维护方面，KGMOPSO优于其他多目标算法。