EvoOpt - Evolutionary Optimization in Python

EvoOpt: Evolutionary Optimization in Python

* Open sourced * Automatically Vectorized * Fast Computation * One Library Do-it-all

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Python implementation of state-of-art meta-heuristic and evolutionary optimisation algorithms.

This library is implemented in Numpy (which was written in C) for fast processing speed

Table of Contents

• About the Project
  • Current support for algorithms
• Getting Started
  • Dependencies
  • Installation
• Usage
• Contributing
• License
• Contact
• Acknowledgements

About The Project

Current support for algorithms

[x] Genetic Algorithm

[x] Duelist Algorithm

[X] Particle Swarm Optimization

[X] Gravitational Search Algorithm

[X] Firefly Algorithm

[X] Simulated Annealing

[X] Multi-Verse Optimization

[X] Grey-Wolf Optimization

More algorithms to come...

Getting Started

There are four simple steps to run an optimization problem using EvoOpt

(Example 2 from example folder)

Prerequisites

from EvoOpt.solver.DuelistAlgorithm import DuelistAlgorithm

**1. Define your function. Say you want to minimize the equation f=(x1,x2) = (x1)^2+(x2)^2 **

def f(x1,x2):
	return x1*x1+x2*x2

**2. Define the variables that can be manipulated for optimization. Define their names as string and put them in an array. **

x=["x1","x2"]

3. Define the boundaries for the manipulated variables:

Say:

x1 is bounded from -2 to 10 (-2 is min value of x1 and 10 is max value of x1)

x2 is bounded from 10 to 15 (10 is min value of x2 and 15 is max value of x2)

We can arrange these boundaries according to the definition array in step 2.

Variables	x1	x2
Min	-2	5
Max	10	15

The corresponding code is:

 xmin=[-2,5]
 xmax=[10,15]

4. Setup the solver and start the solve procedure.

DA=DuelistAlgorithm(f,x,xmin,xmax,max_gen=1000)
DA.solve(plot=True)

Example Result

Dependencies

Numpy and Matplotlib

Windows:

$python -m pip install numpy matplotlib

Linux:

$pip install numpy matplotlib

Installation

You can use two methods for installation:

1. Install from github (recommended as this will download the newest version)

First download the git repository. You can do this by clicking the download button or using the git command:

$ git pull https://github.com/tsyet12/EvoOpt

Move to the directory:

$ cd (directory of EvoOpt)

Run setup. The following command installs all files in directory:

$ pip install -e .

**1. Install from pip **

You can install this package from pip.

Linux:

$ pip install EvoOpt

Windows:

$python -m pip install EvoOpt

Usage

To be updated.

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b testbranch/solvers)
3. Commit your Changes (git commit -m 'Improve testbranch/solvers')
4. Push to the Branch (git push origin testbranch/solvers)
5. Open a Pull Request

License

Distributed under the BSD-2-Clause License. See LICENSE(https://github.com/tsyet12/EvoOpt/blob/master/LICENSE) for more information.

Contact

Sin Yong Teng: tsyet12@gmail.com

Project Link: https://github.com/tsyet12/EvoOpt

Acknowledgements