A Performance Investigation of Multimodal Multiobjective Optimization Algorithms in Solving Two Types of Real-World Problems

Autor:	Chen, Zhiqiu, Chen, Zong-Gan, Jiang, Yuncheng, Zhan, Zhi-Hui
Rok vydání:	2024
Předmět:	Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	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. Comment: the 2024 International Annual Conference on Complex Systems and Intelligent Science,6 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.03013 Zobrazit plný text záznamu View this record from Arxiv