wuhanstudio
/
EMOA
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity
Benchmarks of Evolutionary multi-objective optimization algorithms (EMOA) on Real-world Multi-objective Optimization Problem Suite.
9 Commits 1 Branch 0 Tags 364 KiB
Python 100%
Go to file
wuhanstudio 69c1bbeba8 Add spaces 2022-07-15 23:59:45 +01:00
images Add benchmark results 2022-07-15 23:58:15 +01:00
.gitignore Merge branch 'master' of https://github.com/dongyawang/ssci 2022-06-30 22:42:12 +01:00
README.md Add spaces 2022-07-15 23:59:45 +01:00
main.py Add benchmark results 2022-07-15 23:58:15 +01:00
reproblem.py initial code 2022-06-30 21:34:55 +01:00

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