# 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  
```

![](images/CRE-2-3-1.png)

#### 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  
```

![](images/CRE-2-4-3.png)

#### CRE-2-7-4

```
Time (s):

CTAEA 6.6659016609191895  
NSGA2 10.643601417541504  
NSGA3 14.66565227508545  
```

![](images/CRE-2-7-4.png)

#### CRE-2-4-5

```
Time (s):
CTAEA 5.434146165847778  
NSGA2 10.283865928649902  
NSGA3 15.255037069320679  
```

![](images/CRE-2-4-5.png)