1008 B
1008 B
EMOA
Benchmarks of Evolutionary multi-objective optimization algorithms (EMOA) on Real-world Multi-objective Optimization Problem Suite.
Quick Start
Creating the environment：
conda create -n pymoo python=3.8
conda activate pymoo
pip install -U pymoo
Change the test problem in main.py
python main.py
Benchmark
population size: 100 number of generations: 200
CRE-2-3-1
Time (s): CTAEA 6.297407865524292 NSGA2 9.709688425064087 NSGA3 13.19536280632019
CRE-2-4-2
Time (s): CTAEA 5.5211687088012695 NSGA2 8.863621950149536 NSGA3 12.693290948867798
CRE-2-4-3
Time (s): CTAEA 5.683619022369385 NSGA2 9.352391481399536 NSGA3 13.239986419677734
CRE-2-7-4
Time (s): CTAEA 6.6659016609191895 NSGA2 10.643601417541504 NSGA3 14.66565227508545
CRE-2-4-5
Time (s): CTAEA 5.434146165847778 NSGA2 10.283865928649902 NSGA3 15.255037069320679
