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

TL;DR

This paper evaluates the effectiveness of existing multimodal multiobjective optimization algorithms on real-world problems in feature and location selection, using newly constructed datasets, revealing their strengths and limitations in practical scenarios.

Contribution

It formulates two real-world multimodal multiobjective problems and assesses seven MMOAs on these problems, providing insights into their practical applicability beyond benchmark functions.

Findings

Seven MMOAs show varied performance on real-world problems.

Certain MMOAs are more suitable for feature selection tasks.

Insights aid in selecting appropriate MMOAs for practical applications.

Abstract

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.