A general framework of dynamic constrained multiobjective evolutionary algorithms for constrained optimization
- This is the DCMOEA in python 2.7 for Windows.
- This program is coded by the evolutionary computation group in China University of Geosciences.
- All the problems are in the directory PROBLEM, and the results will be put in the directory RESULT by the program.
- The algorithm starts by if name == 'main' in the file main.py:
- (1) import the problem you want to solve (i.e., import g02, g03)
- (2) put the problem in list of module that you want to run (i.e, module = [g02])
- (3) You can change the total number of independent runs (i.e., t = 25)
- If you want to modify the parameter setting, please open the conf.py, and change
- (1) the maximum number of generaions (i.e., K=2400)
- (2) population size (i.e., popsize=100)
Please kindly cite this paper in your publications if it helps your research:
@article{zeng2017general,
title={A general framework of dynamic constrained multiobjective evolutionary algorithms for constrained optimization},
author={Zeng, Sanyou and Jiao, Ruwang and Li, Changhe and Li, Xi and Alkasassbeh, Jawdat S},
journal={IEEE transactions on Cybernetics},
volume={47},
number={9},
pages={2678--2688},
year={2017},
publisher={IEEE}
}