EMOA/README.md

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