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*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".
*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, IEEE Computational Intelligence Magazine, 2017, in press".
Since v1.6, we have uploaded our PlatEMO on github. You can find more details on Github home page.
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.
1. Update the reference point sampling strategies of WFG1 and WFG2.
2. Add more popular MOEAs: Currently there are 90 MOEAs in PlatEMO.
3. Fix some minor bugs in MOEAs and the GUI.
4. Since v1.6, we have uploaded our PlatEMO on github.
Update the introduction to CEC'2018 competition on many-objective optimization.
Add more popular MOEAs: Currently there are 77 MOEAs in PlatEMO.
Update the module for CEC'2018 competition on many-objective optimization.
Add more popular MOEAs: Currently there are 73 MOEAs in PlatEMO.
Add two recently accepted MOEAs, i.e., AR-MOEA and RSEA, which are very effective for multi- and many-objective optimization and superior over the state-of-the-art.
More popular MOEAs and MOPs: Currently there are 69 MOEAs and 129 MOPs in PlatEMO.
Now the function NDSort.m supports to sort the population with constraints, and many MOEAs including NSGA-II support to solve problems with constraints.
Add icons for all the menu items in the GUI.
The results can be plotted in experimental mode by right-clicking the statistical table.
Fix some minor bugs in MOEAs, MOPs and the GUI.
More popular MOEAs and MOPs: Currently there are 61 MOEAs and 126	MOPs in PlatEMO.
Simpler way to execute experiment in experimental module: The	configuration of each experiment will be automatically saved when	starting to execute the experiment. The configuration files can	be reloaded so that users need not configure the same experiment	once more.
Performance metrics ensemble: The performance metrics ensemble	method is adopted in the experimental module, which can provide a	ranking order of all the compared MOEAs on one MOP with respect	to all the performance indicators.
Fix some minor bugs in the experimental module, and enhance the	stability of the GUI.
The sampling method of the PF of MaF9 has been corrected.
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PlatEMO provides the only interface main() to users. If this function is invoked with input parameters, PlatEMO will be run without GUI. While if the function is invoked without any input parameter, PlatEMO will be run with a GUI. When PlatEMO is run without GUI, users should set the MOEA to be executed, the MOP to be solved, the operator to be used, and all the other parameter settings by passing input parameters to main(). Please refer to the user manual for the details of all the acceptable parameters of main(). After the MOEA has been terminated, the final population can be displayed in a figure, or saved in a .mat file. In the figure, users can select the data to be displayed on the menu, including the Pareto front of the result, the Pareto set of the result, the true Pareto front, and the evolutionary trajectories of any performance indicator values.
If main() is invoked without any input parameter, the GUI of PlatEMO will be shown, which currently contains two modules, i.e. test module and experimental module. The test module is used to analyze the performance of each MOEA, where one MOEA on an MOP can be executed each time. The test module provides similar functions to the PlatEMO without GUI, but users do not need to write any additional command or code.
The experimental module is designed for statistical experiments, where multiple MOEAs on a batch of MOPs can be executed at the same time. Different from the text module showing a figure, the experimental module shows a table containing all the statistical experimental results, including the mean and the standard deviation of the performance indicator value, and the Wilcoxon rank sum test result. Afterwards, the table can be saved as Excel table (.xlsx) or LaTeX table (.tex).
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".
If you have any question, comment or suggestion to PlatEMO or the algorithms in PlatEMO, please contact Ye Tian (field910921@gmail.com).
If you want to add your MOEA, MOP, operator or performance indicator to PlatEMO, please send the MATLAB code (able to be used in PlatEMO) and the relevant literature to field910921@gmail.com.