A practical tutorial on solving optimization problems via PlatEMO

• Published in: Neurocomputing (Amsterdam) Vol. 518; pp. 190 - 205
• Main Authors: Tian, Ye, Zhu, Weijian, Zhang, Xingyi, Jin, Yaochu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 21.01.2023
• Subjects: Evolutionary computation; Metaheuristics; Optimization; PlatEMO; Problem definition; Swarm intelligence; PlatEMO; Swarm intelligence; Problem definition; Metaheuristics; Optimization; Evolutionary computation
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2022.10.075

•This paper presents a practical tutorial on solving optimization problems via PlatEMO, by means of abundant examples and source codes.•This paper is the first tutorial for the newest version PlatEMO v4.0.•This paper does not go deep into the technical details of algorithms, but aims to enable beginners to use PlatEMO at a low cost, which is much easier to be understood than the user manual of PlatEMO.•This paper is written according to many questions raised by users in the last five years. PlatEMO is an open-source platform for solving complex optimization problems, which provides a variety of metaheuristics including evolutionary algorithms, swarm intelligence algorithms, multi-objective optimization algorithms, surrogate-assisted optimization algorithms, and many others. Due to the problem-independent nature of most metaheuristics, they are versatile for solving problems with various difficulties such as multimodal landscapes, discrete search spaces, multiple objectives, strict constraints, and expensive evaluations, regardless of the fields the problems belong to. Since PlatEMO was published in 2017, it has been used by many researchers from both academia and industry in the computational intelligence community. However, the basic terms and concepts about optimization may confuse practitioners and junior researchers new to metaheuristics. Hence, this paper presents a practical introduction to the use of PlatEMO 4.0, focusing on the procedures of defining problems, selecting suitable metaheuristics, and collecting results. Note, however, that a description of the technical details of metaheuristics is beyond the scope of this paper and interested readers may refer to the cited references.