Based on the dual-inheritance framework of cultural evolution, an improved multiobjective cultural algorithm (IMOCA) with a multistrategy knowledge base is presented in this paper. Inspired by the original versions of the cultural algorithm (CA), four basic types of knowledge sources, i.e., normative, situational, topographical and historical knowledge, are effectively utilized in the proposed IMOCA. Several modifications with the knowledge base of the IMOCA are made to tackle the characteristics of the multiobjective problem. Situational knowledge is used as an external repository for storing elite individuals, and the redesigned topographical knowledge functions as a search engine to broaden the expansion of the obtained solution set. The historical knowledge used in the IMOCA aims to select a productive knowledge source to generate new individuals. Furthermore, a simple mutation scheme is introduced into the knowledge base as an influence function for the purpose of fine tuning in the late stage of search. After configuring the parameters used in IMOCA, two classic benchmark suites, i.e., WFG and MaF, are used to assess the performance of the IMOCA in approaching the Pareto fronts (PFs) with accuracy and diversity. Nondominated solution sets obtained by the IMOCA are compared with 8 state-of-the-art multiobjective algorithms available in the literature. A statistical analysis is conducted, which reveals that, by modifying the basic knowledge structure of the CA, the proposed multiobjective cultural algorithm is competent enough to handle multiobjective problems with competitive performance.

基于文化进化的双重继承框架，提出了一种具有多策略知识库的改进的多目标文化算法（IMOCA）。受文化算法（CA）原始版本的启发，提议的IMOCA有效地利用了四种基本类型的知识源，即规范知识，情境知识，地形知识和历史知识。对IMOCA的知识库进行了一些修改，以解决多目标问题的特征。情境知识用作存储精英个人的外部存储库，重新设计的地形知识用作搜索引擎，以扩展获得的解决方案集的范围。IMOCA中使用的历史知识旨在选择一种生产性知识来源来培养新的个体。此外，为了在搜索的后期进行微调，将一个简单的变异方案作为影响函数引入知识库。配置了IMOCA中使用的参数后，使用两个经典的基准套件，即WFG和MaF，来评估IMOCA在准确度和多样性接近帕累托前沿（PF）方面的性能。将IMOCA获得的非支配解集与文献中提供的8种最新的多目标算法进行比较。进行了统计分析，结果表明，通过修改CA的基本知识结构，