In recent years, multimodal multiobjective optimization algorithms (MMOAs) based on evolutionary computation have been widely studied. However, existing MMOAs are mainly tested on benchmark function sets such as the 2019 IEEE Congress on Evolutionary Computation test suite (CEC 2019), and their performance on real-world problems is neglected. In this paper, two types of real-world multimodal multiobjective optimization problems in feature selection and location selection respectively are formulated. Moreover, four real-world datasets of Guangzhou, China are constructed for location selection. An investigation is conducted to evaluate the performance of seven existing MMOAs in solving these two types of real-world problems. An analysis of the experimental results explores the characteristics of the tested MMOAs, providing insights for selecting suitable MMOAs in real-world applications.

翻译：近年来，基于进化计算的多模态多目标优化算法得到了广泛研究。然而，现有MMOA主要在基准函数集（如2019年IEEE进化计算大会测试集CEC 2019）上进行测试，其在实际问题中的性能常被忽视。本文分别针对特征选择和选址问题构建了两类实际多模态多目标优化问题模型，并基于中国广州市数据构建了四个选址问题实际数据集。通过实验评估了七种现有MMOA在求解这两类实际问题时的性能表现，并对实验结果进行分析以揭示各算法的特性，为实际应用中MMOA的选择提供参考依据。