PlatEMO v3.4 (2022/1/15)

• Remake the application module, a more powerful and friendly interface enables users to define problems more easily. The defined problems can also be saved into files and solved in other modules.

• Add two multi-objective evolutionary algorithms MOEA/D-DYTS and MOEA/D-UR, two surrogate-assisted multi-objective evolutionary algorithms PB-NSGA-III and PB-RVEA, a constrained multi-objective evolutionary algorithm DSPCMDE, three large-scale multi-objective evolutionary algorithms FDV, IM-MOEA/D, and LMOEA-DS, two sparse multi-objective evolutionary algorithm SLMEA and SparseEA2, and four single-objective mathematical programming methods Adam, Nelder-Mead, RMSProp, and SD. There are currently 176 algorithms in the platform.

PlatEMO v3.3 (2021/8/14)

• Add four multi-objective evolutionary algorithms DEA-GNG, ICMA, PeEA, and RVEA-iGNG. There are currently 162 algorithms in the platform.

• Add five constrained multi-objective optimization problems FCP1-FCP5 and a sparse multi-objective optimization problem Sparse_KP. There are currently 345 problems in the platform.

• When solving user-defined problems, the objective and constraint functions can be either @(x,d) or @(x).

PlatEMO v3.2 (2021/5/17)

• Add four surrogate-assisted multi-objective evolutionary algorithms AB-SAEA, EDN-ARMOEA, HeE-MOEA, KTA2, and a constrained multi-objective evolutionary algorithm c-DPEA. There are currently 158 algorithms in the platform.

PlatEMO v3.1 (2021/3/22)

• Add two multi-objective optimization algorithms CCGDE3 and NSGA-II+ARSBX and one single-objective optimization algorithm OFA. There are currently 153 algorithms in the platform.

• Fix some minor bugs in algorithms and the GUI.

PlatEMO v3.0 (2021/2/12)

• 20+ algorithms and 100+ problems for single-objective optimization. There are currently 150 algorithms and 339 problems in the platform, including single-objective optimization, multi-objective optimization, many-objective optimization, combinatorial optimization, large-scale optimization, constrained optimization, multimodal optimization, expensive optimization, sparse optimization, and preference optimization.

• A totally new GUI with more powerful functions, which contains a test module, an application module, and an experiment module.

• A novel filter system based on hybrid labels, which facilitates the selection of suitable algorithms for solving different types of problems.

• More convenient interfaces for solving user-defined problems, where no file needs to be written by users.

• A better visualization of populations, where the true Pareto fronts and feasible regions can be shown in the plots.

PlatEMO v2.9.0 (2020/10/11)

• Add one algorithm for constrained optimization (i.e., CMOEA-MS), one algorithm for large-scale optimization (i.e., DGEA), one algorithm for expensive optimization (i.e., MESMO), and one algorithm for feature selection (i.e., DAEA). There are currently 122 algorithms in the platform.

PlatEMO v2.8.0 (2020/07/02)

• Add three algorithms for constrained optimization (i.e., CCMO, MOEA/D-DAE, and TiGE-2) and an algorithm for many-objective optimization (i.e., PREA). There are currently 118 algorithms in the platform.

• Fix some minor bugs in the Pareto front sampling methods in LIR-CMOP and MW problems.

PlatEMO v2.5.0 (2020/02/04)

• Add the time-varying ratio error estimation (TREE) test suite, which contains six constrained large-scale problems from real-world applications.
• Fix some minor bugs in algorithms and problems.

PlatEMO v2.3.0 (2019/10/25)

Release Highlights of PlatEMO 2.3

• Add four algorithms: C-TAEA, ToP, MOEA/D-URAW, and MultiObjectiveEGO. There are currently 108 algorithms on the platform.
• Add the constrained benchmark problems DOC1-9 and MW1-14. There are currently 201 problems on the platform.
• Update the Pareto front sampling methods of DAS-CMOP1-9 and LIR-CMOP1-14: Dynamically sample points on Pareto fronts instead of loading points from files.
• Update the table in the experiment module: Ignore NaN values when calculating the mean and standard deviation in each cell of the table.

Copyright

The Copyright of the PlatEMO belongs to the BIMK group. You are free to use the PlatEMO for research purposes. All publications which use this platform or any code in the platform should acknowledge the use of "PlatEMO" and reference "Ye Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum], IEEE Computational Intelligence Magazine, 2017, 12(4): 73-87".

@article{PlatEMO,
  title={{PlatEMO}: A {MATLAB} platform for evolutionary multi-objective optimization},
  author={Tian, Ye and Cheng, Ran and Zhang, Xingyi and Jin, Yaochu},
  journal={IEEE Computational Intelligence Magazine},
  volume={12},
  number={4},
  pages={73--87},
  year={2017},
}

PlatEMO v2.2.0 (2019/07/23)

fix a high-level bug.