# PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization

arXiv: https://arxiv.org/abs/1701.00879 · HTML: https://mlsanity.com/paper/1701.00879/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2017.

## Built on by (most cited first, 30 of 41)

- [Multi-agent Dynamic Algorithm Configuration](https://mlsanity.com/paper/2210.06835/) (2022)
- [Large Language Model for Multi-objective Evolutionary Optimization](https://mlsanity.com/paper/2310.12541/) (2023)
- [Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems](https://mlsanity.com/paper/2105.02741/) (2021)
- [Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment](https://mlsanity.com/paper/2208.04321/) (2022)
- [OptunaHub: A Platform for Black-Box Optimization](https://mlsanity.com/paper/2510.02798/) (2025)
- [A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving](https://mlsanity.com/paper/2509.08269/) (2025)
- [LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch](https://mlsanity.com/paper/2409.02969/) (2024)
- [A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization](https://mlsanity.com/paper/2106.12086/) (2021)
- [A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems](https://mlsanity.com/paper/2410.03205/) (2024)
- [Multi-objective Feature Selection with Missing Data in Classification](https://mlsanity.com/paper/2104.08747/) (2021)
- [Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis](https://mlsanity.com/paper/2403.14413/) (2024)
- [Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization](https://mlsanity.com/paper/2101.07512/) (2021)
- [An Analysis of Phenotypic Diversity in Multi-Solution Optimization](https://mlsanity.com/paper/2105.04252/) (2021)
- [Assessing Ranking and Effectiveness of Evolutionary Algorithm Hyperparameters Using Global Sensitivity Analysis Methodologies](https://mlsanity.com/paper/2207.04820/) (2022)
- [MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow](https://mlsanity.com/paper/2505.14126/) (2025)
- [Expected Coordinate Improvement for High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2404.11917/) (2024)
- [Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons](https://mlsanity.com/paper/2603.21544/) (2026)
- [PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack](https://mlsanity.com/paper/2101.07538/) (2021)
- [Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization](https://mlsanity.com/paper/2201.06700/) (2022)
- [GPSAF: A Generalized Probabilistic Surrogate-Assisted Framework for Constrained Single- and Multi-objective Optimization](https://mlsanity.com/paper/2204.04054/) (2022)
- [Molecule optimization via multi-objective evolutionary in implicit chemical space](https://mlsanity.com/paper/2212.08826/) (2022)
- [Improving Performance Insensitivity of Large-scale Multiobjective Optimization via Monte Carlo Tree Search](https://mlsanity.com/paper/2304.04071/) (2023)
- [Experience-Based Evolutionary Algorithms for Expensive Optimization](https://mlsanity.com/paper/2304.04166/) (2023)
- [Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems](https://mlsanity.com/paper/2304.09444/) (2023)
- [Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization](https://mlsanity.com/paper/2309.11994/) (2023)
- [EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning](https://mlsanity.com/paper/2501.15129/) (2025)
- [PlatMetaX: An Integrated MATLAB platform for Meta-Black-Box Optimization](https://mlsanity.com/paper/2503.22722/) (2025)
- [Variable Division and Optimization for Constrained Multiobjective Portfolio Problems](https://mlsanity.com/paper/2101.08552/) (2021)
- [PAMELI: A Meta-Algorithm for Computationally Expensive Multi-Objective Optimization Problems](https://mlsanity.com/paper/2103.10736/) (2021)
- [Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model](https://mlsanity.com/paper/2108.04197/) (2021)

## Abstract (arXiv)

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: http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.