1.0 KiB
1.0 KiB
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
