Release Highlights of PlatEMO 2.0

• Lighter framework. The architecture of PlatEMO is simplified, which leads to lower learning cost and higher efficiency. The result file size is also reduced.
• Higher efficiency. The runtime of Pareto dominance based algorithms is reduced by using a more efficient non-dominated sorting algorithm. The runtime of decomposition based algorithms is reduced due to the new architecture of PlatEMO. The runtime of hypervolume calculation is reduced by new logic and GPU acceleration. In experimental module, the algorithms can be executed in parallel.
• More conveniences. The populations obtained during the evolutionary process can be saved in result files. The references of each algorithm, problem, operator, and metric are given in the comments of the function. The codes of GUI are now open source.

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},
}