PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization. Over the last three decades, a large number of evolutionary algorithms have been developed for solving multiobjective optimization problems. However, there lacks an up-to-date and comprehensive software platform for researchers to properly benchmark existing algorithms and for practitioners to apply selected algorithms to solve their real-world problems. The demand of such a common tool becomes even more urgent, when the source code of many proposed algorithms has not been made publicly available. To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multi-objective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators. With a user-friendly graphical user interface, PlatEMO enables users to easily compare several evolutionary algorithms at one time and collect statistical results in Excel or LaTeX files. More importantly, PlatEMO is completely open source, such that users are able to develop new algorithms on the basis of it. This paper introduces the main features of PlatEMO and illustrates how to use it for performing comparative experiments, embedding new algorithms, creating new test problems, and developing performance indicators. Source code of PlatEMO is now available at:

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  1. Hansen, Nikolaus; Auger, Anne; Ros, Raymond; Mersmann, Olaf; TuĊĦar, Tea; Brockhoff, Dimo: COCO: a platform for comparing continuous optimizers in a black-box setting (2021)
  2. Zhang, Maoqing; Wang, Lei; Guo, Weian; Li, Wuzhao; Li, Dongyang; Hu, Bo; Wu, Qidi: Many-objective evolutionary algorithm based on relative non-dominance matrix (2021)
  3. Chen, Huangke; Cheng, Ran; Wen, Jinming; Li, Haifeng; Weng, Jian: Solving large-scale many-objective optimization problems by covariance matrix adaptation evolution strategy with scalable small subpopulations (2020)
  4. Dong, Zhiming; Wang, Xianpeng; Tang, Lixin: MOEA/D with a self-adaptive weight vector adjustment strategy based on chain segmentation (2020)
  5. Hou, Zhanglu; He, Cheng; Cheng, Ran: Reformulating preferences into constraints for evolutionary multi- and many-objective optimization (2020)
  6. Julian Blank, Kalyanmoy Deb: pymoo: Multi-objective Optimization in Python (2020) arXiv
  7. Liu, Zhi-Zhong; Wang, Yong; Huang, Pei-Qiu: AnD: a many-objective evolutionary algorithm with angle-based selection and shift-based density estimation (2020)
  8. Li, Wenhua; Wang, Rui; Zhang, Tao; Ming, Mengjun; Li, Kaiwen: Reinvestigation of evolutionary many-objective optimization: focus on the Pareto knee front (2020)
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  11. Rojas Gonzalez, Sebastian; Jalali, Hamed; van Nieuwenhuyse, Inneke: A multiobjective stochastic simulation optimization algorithm (2020)
  12. Wang, Xilu; Jin, Yaochu; Schmitt, Sebastian; Olhofer, Markus: An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization (2020)
  13. Zhang, XuWei; Liu, Hao; Tu, LiangPing; Zhao, Jian: An efficient multi-objective optimization algorithm based on level swarm optimizer (2020)
  14. Zhang, Zhechen; Liu, Sanyang; Gao, Weifeng; Xu, Jingwei; Zhu, Shengqi: An enhanced multi-objective evolutionary optimization algorithm with inverse model (2020)
  15. Zheng, Wei; Wu, Jianyu; Zhang, Chenghu; Sun, Jianyong: A clustering-based multiobjective evolutionary algorithm for balancing exploration and exploitation (2020)
  16. Benitez-Hidalgo, A.; Nebro, AJ; Garcia-Nieto, J.; Oregi, I.; Del Ser, J.: jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics (2019) arXiv
  17. Chen, Min-Rong; Zeng, Guo-Qiang; Lu, Kang-Di: A many-objective population extremal optimization algorithm with an adaptive hybrid mutation operation (2019)
  18. Han, Dong; Du, Wenli; Du, Wei; Jin, Yaochu; Wu, Chunping: An adaptive decomposition-based evolutionary algorithm for many-objective optimization (2019)
  19. Lin, Wu; Lin, Qiuzhen; Zhu, Zexuan; Li, Jianqiang; Chen, Jianyong; Ming, Zhong: Evolutionary search with multiple utopian reference points in decomposition-based multiobjective optimization (2019)
  20. Liu, Haitao; Du, Wei; Guo, Zhaoxia: A multi-population evolutionary algorithm with single-objective guide for many-objective optimization (2019)

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