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:

References in zbMATH (referenced in 31 articles )

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  1. Wang, Wenyu; Akhtar, Taimoor; Shoemaker, Christine A.: Integrating (\varepsilon)-dominance and RBF surrogate optimization for solving computationally expensive many-objective optimization problems (2022)
  2. 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)
  3. Ma, Haiping; Wei, Haoyu; Tian, Ye; Cheng, Ran; Zhang, Xingyi: A multi-stage evolutionary algorithm for multi-objective optimization with complex constraints (2021)
  4. Palakonda, Vikas; Mallipeddi, Rammohan; Suganthan, Ponnuthurai Nagaratnam: An ensemble approach with external archive for multi- and many-objective optimization with adaptive mating mechanism and two-level environmental selection (2021)
  5. Sun, Yifei; Bian, Kun; Liu, Zhuo; Sun, Xin; Yao, Ruoxia: Adaptive strategies based on differential evolutionary algorithm for many-objective optimization (2021)
  6. Wang, Hao; Sun, Chaoli; Zhang, Guochen; Fieldsend, Jonathan E.; Jin, Yaochu: Non-dominated sorting on performance indicators for evolutionary many-objective optimization (2021)
  7. Xie, Yingbo; Qiao, Junfei; Wang, Ding; Yin, Baocai: A novel decomposition-based multiobjective evolutionary algorithm using improved multiple adaptive dynamic selection strategies (2021)
  8. Yan, Zeyuan; Tan, Yanyan; Zheng, Wei; Meng, Lili; Zhang, Huaxiang: Leader recommend operators selection strategy for a multiobjective evolutionary algorithm based on decomposition (2021)
  9. 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)
  10. 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)
  11. Dong, Zhiming; Wang, Xianpeng; Tang, Lixin: MOEA/D with a self-adaptive weight vector adjustment strategy based on chain segmentation (2020)
  12. Hou, Zhanglu; He, Cheng; Cheng, Ran: Reformulating preferences into constraints for evolutionary multi- and many-objective optimization (2020)
  13. Julian Blank, Kalyanmoy Deb: pymoo: Multi-objective Optimization in Python (2020) arXiv
  14. Liu, Zhi-Zhong; Wang, Yong; Huang, Pei-Qiu: AnD: a many-objective evolutionary algorithm with angle-based selection and shift-based density estimation (2020)
  15. Li, Wenhua; Wang, Rui; Zhang, Tao; Ming, Mengjun; Li, Kaiwen: Reinvestigation of evolutionary many-objective optimization: focus on the Pareto knee front (2020)
  16. Luo, Jianping; Huang, Xiongwen; Yang, Yun; Li, Xia; Wang, Zhenkun; Feng, Jiqiang: A many-objective particle swarm optimizer based on indicator and direction vectors for many-objective optimization (2020)
  17. Patil, Mukundraj V.; Kulkarni, Anand J.: Pareto dominance based multiobjective cohort intelligence algorithm (2020)
  18. Rojas Gonzalez, Sebastian; Jalali, Hamed; van Nieuwenhuyse, Inneke: A multiobjective stochastic simulation optimization algorithm (2020)
  19. Wang, Xilu; Jin, Yaochu; Schmitt, Sebastian; Olhofer, Markus: An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization (2020)
  20. Zhang, XuWei; Liu, Hao; Tu, LiangPing; Zhao, Jian: An efficient multi-objective optimization algorithm based on level swarm optimizer (2020)

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