Analysis of Algorithm Comparison Results on Real-World Multi-Objective Problems

Lie Meng Pang; Hisao Ishibuchi; Ke Shang

10.1109/CEC60901.2024.10612187

2024-07-05

979-8-3503-0837-2

2024 IEEE Congress on Evolutionary Computation (CEC)

30 June-5 July 2024

Yokohama, Japan

Recently, several real-world multi-objective optimization problem suites have been proposed to facilitate the evaluation of the performance of evolutionary multi-objective optimization (EMO) algorithms. In spite of the importance of using real-world problems to evaluate EMO algorithms, their characteristics are not well understood compared to artificial test problems. Thus, there is a need to examine and understand the challenges posed by these real-world problems. In this study, we attempt to explore the characteristics of the most recently proposed real-world application suite (i.e., RWA suite). Six EMO algorithms are evaluated on the RWA suite, including three classic algorithms and three recently-proposed algorithms. Based on the performance comparison results, we systematically analyze the RWA suite in terms of convergence and diversity difficulties.

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EI

Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China

Lie Meng Pang,Hisao Ishibuchi,Ke Shang. Analysis of Algorithm Comparison Results on Real-World Multi-Objective Problems[C],2024.