EvoloPy-master
所属分类:Windows编程
开发工具:Python
文件大小:22KB
下载次数:6
上传日期:2018-07-19 21:30:16
上 传 者:
haifeng2415
说明: 灰狼算法来快速确定适应度值,然后确定猎物的位置,进行下一次的移动
(The Grey Wolf algorithm determines the fitness value quickly, and then determines the location of the prey and makes the next move.)
文件列表:
BAT.py (2653, 2017-10-16)
CS.py (3511, 2017-10-16)
FFA.py (3798, 2017-10-16)
GWO.py (3837, 2018-06-10)
MFO.py (4903, 2017-10-16)
MVO.py (4295, 2017-10-16)
PSO.py (2499, 2017-10-16)
WOA.py (3783, 2017-10-16)
benchmarks.py (6183, 2017-10-16)
experiment2018-06-14-11-04-08.csv (14900, 2018-06-14)
optimizer.py (3627, 2018-06-10)
requirements.txt (21, 2017-10-16)
solution.py (443, 2017-10-16)
灰狼算法.zip (1319, 2018-07-19)
###EvoloPy: An open source nature-inspired optimization toolbox for global optimization in Python
The EvoloPy toolbox provides classical and recent nature-inspired metaheuristic for the global optimization. The list of optimizers that have been implemented includes Particle Swarm Optimization (PSO), Multi-Verse Optimizer (MVO), Grey Wolf Optimizer (GWO), and Moth Flame Optimization (MFO). The full list of implemented optimizers is available here https://github.com/7ossam81/EvoloPy/wiki/List-of-optimizers
##Features
- Six nature-inspired metaheuristic optimizers were implemented.
- The implimentation uses the fast array manipulation using `NumPy`.
- Matrix support using `SciPy`'s package.
- More optimizers is comming soon.
##Installation
- Python 3.xx is required.
Run
pip3 install -r requirements.txt
(possibly with `sudo`)
That command above will install `sklearn`, `NumPy` and `SciPy` for
you.
- If you are installing EvoloPy Toolbox onto Windows, please Install Anaconda from here https://www.continuum.io/downloads, which is the leading open data science platform powered by Python.
- If you are installing onto Ubuntu or Debian and using Python 3 then
this will pull in all the dependencies from the repositories:
sudo apt-get install python3-numpy python3-scipy liblapack-dev libatlas-base-dev libgsl0-dev fftw-dev libglpk-dev libdsdp-dev
##Get the source
Clone the Git repository from GitHub
git clone https://github.com/7ossam81/EvoloPy.git
##Quick User Guide
EvoloPy toolbox contains twenty three benchamrks (F1-F23). The main file is the optimizer.py, which considered the interface of the toolbox. In the optimizer.py you can setup your experiment by selecting the optmizers, the benchmarks, number of runs, number of iterations, and population size.
The following is a sample example to use the EvoloPy toolbox.
To choose PSO optimizer for your experiment, change the PSO flag to true and others to false.
```
Select optimizers:
PSO= True
MVO= False
GWO = False
MFO= False
CS= False
...
```
After that, Select benchmark function:
```
F1=True
F2=False
F3=False
F4=False
F5=False
F6=False
....
```
Change NumOfRuns, PopulationSize, and Iterations variables as you want:
```
NumOfRuns=10
PopulationSize = 50
Iterations= 1000
```
Now your experiment is ready to run. Enjoy!
##Contribute
- Issue Tracker: https://github.com/7ossam81/EvoloPy/issues
- Source Code: https://github.com/7ossam81/EvoloPy
##Support
Use the [issue tracker](https://github.com/7ossam81/EvoloPy/issues).
近期下载者:
相关文件:
收藏者: