PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]
By Ye Tian, Ran Cheng, Xing-yi Zhang, Yaochu Jin. IEEE Computational Intelligence Magazine, 2017
2,245 citations recorded by the source corpus.
Over the last three decades, a large number of evolutionary algorithms have been developed for solving multi-objective optimization problems. However, there lacks an upto-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 multiobjective 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: http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/02ec0b2af6dd42ffd42ae8e64ba731016211a346
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Citations in the corpus
- An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems (2020, methodology): “All the experiments are conducted on the evolutionary MOP platform PlatEMO [70].”
- A Coevolutionary Framework for Constrained Multiobjective Optimization Problems (2021, methodology): “The experiments are implemented on the evolutionary multiobjective optimization platform [45].”
- COCO: a platform for comparing continuous optimizers in a black-box setting (2016, background): “Newer platforms that attempt to benchmark multiobjective solvers are the MOEA Framework (in Java), its Python counterpart Platypus, jMetal (originally in Java, now also in other languages) [25], PLATEMO (in Matlab) [67], and ecr (in R) [14].”
- An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility (2018, methodology): “All the compared MOEAs in this paper are implemented in the MATLAB-based MOEA platform PlatEMO [59].”
- Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, methodology): “All the experiments are performed on PlatEMO [55].”
- A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization (2019, methodology): “In this section, we examine the performance of CSEA by empirically comparing it with a few state-of-the-art algorithms, namely NSGA-III [11], ParEGO [37], CPS-MOEA [46], MOEA/D-EGO [21], and K-RVEA [39] (all the compared algorithms are implemented in PlatEMO [69]).”
- Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization (2021, methodology): “All the experiments are conducted on PlatEMO [42].”
- A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, methodology): “All the experiments are implemented on the PlatEMO1 [40].”
- Evolutionary Large-Scale Multi-Objective Optimization: A Survey (2021, background): “[161] built by us, with which users can easily conduct comparative studies via one-click operation.”
- Deep Reinforcement Learning for Multiobjective Optimization (2019, methodology): “Meanwhile, all experiments of the compared MOEAs are conducted on the standard software platform PlatEMO 2 [36] which is written in MATLAB.”
- A competitive mechanism based multi-objective particle swarm optimizer with fast convergence (2018, methodology): “The source codes of NSGA-II, SPEA2 and MOEA/D are provided in PlatEMO [40], and the source codes of MPSOD, SMPSO and MMOPSO are obtained from the authors of the original papers.”