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
If you like our framework then we would really appreciate a Star ⭐!
- Fourteen nature-inspired metaheuristic optimizers were implemented.
- The implementation uses the fast array manipulation using
NumPy. - Matrix support using
SciPy's package. - More optimizers is coming soon.
- Python 3.6 or higher is required.
Run
pip install -r requirements.txt
(possibly with sudo)
This command will install sklearn, NumPy, SciPy, and other dependencies for you.
-
For Windows: Please install Anaconda from here, which is the leading open data science platform powered by Python.
-
For Ubuntu or Debian (Python 3):
sudo apt-get install python3-numpy python3-scipy liblapack-dev libatlas-base-dev libgsl0-dev fftw-dev libglpk-dev libdsdp-dev
Clone the Git repository from GitHub
git clone https://github.com/7ossam81/EvoloPy.git
EvoloPy toolbox contains twenty three benchmarks (F1-F24). 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 optimizers, the benchmarks, number of runs, number of iterations, and population size.
The following is a sample example to use the EvoloPy toolbox.
Select optimizers from the list of available ones: "SSA","PSO","GA","BAT","FFA","GWO","WOA","MVO","MFO","CS","HHO","SCA","JAYA","DE". For example:
optimizer=["SSA","PSO","GA"]
After that, Select benchmark function from the list of available ones: "F1","F2","F3","F4","F5","F6","F7","F8","F9","F10","F11","F12","F13","F14","F15","F16","F17","F18","F19". For example:
objectivefunc=["F3","F4"]
Select the number of repetitions for each experiment. To obtain meaningful statistical results, usually 30 independent runs are executed for each algorithm. For example:
NumOfRuns=10
Select general parameters for all optimizers (population size, number of iterations). For example:
params = {'PopulationSize' : 30, 'Iterations' : 50}
Choose whether to Export the results in different formats. For example:
export_flags = {'Export_avg':True, 'Export_details':True, 'Export_convergence':True, 'Export_boxplot':True}
Now your experiment is ready to run. Enjoy!
Run the example file:
python examples/example.py
- Issue Tracker: https://github.com/7ossam81/EvoloPy/issues
- Source Code: https://github.com/7ossam81/EvoloPy
- Video Demo:https://www.youtube.com/watch?v=8t10SyrhDjQ
- Paper source: https://github.com/7ossam81/EvoloPy
- Paper: https://www.scitepress.org/Papers/2016/60482/60482.pdf
- Poster source: https://github.com/7ossam81/EvoloPy-poster
- Live Demo: http://evo-ml.com/evolopy-live-demo/
- 7ossam81
- RaneemQaddoura
- aljarrahcs
- jbae11
- dietmarwo
- bionboy
- deepak-158
- JJ
For more information about EvoloPy, please refer to our paper:
Faris, Hossam, Ibrahim Aljarah, Seyedali Mirjalili, Pedro A. Castillo, and Juan Julián Merelo Guervós. "EvoloPy: An Open-source Nature-inspired Optimization Framework in Python." In IJCCI (ECTA), pp. 171-177. 2016. https://www.scitepress.org/Papers/2016/60482/60482.pdf
Please include the following related citations:
- Qaddoura, Raneem, Hossam Faris, Ibrahim Aljarah, and Pedro A. Castillo. "EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework in Python." In International Conference on the Applications of Evolutionary Computation (Part of EvoStar), pp. 20-36. Springer, Cham, 2020.
- Ruba Abu Khurma, Ibrahim Aljarah, Ahmad Sharieh, and Seyedali Mirjalili. Evolopy-fs: An open-source nature-inspired optimization framework in python for feature selection. In Evolutionary Machine Learning Techniques, pages 131–173. Springer, 2020
Use the issue tracker to report bugs or request features.
