A python implementation of Particle Swarm Optimization.

PSOPy (pronounced "Soapy") is a SciPy compatible super fast Python implementation for Particle Swarm Optimization. The codes are tested for standard optimization test functions (both constrained and unconstrained).

The library provides two implementations, one that mimics the interface to scipy.optimize.minimize and one that directly runs PSO. The SciPy compatible function is a wrapper over the direct implementation, and therefore may be slower in execution time, as the constraint and fitness functions are wrapped.

To install this library from GitHub,

$ git clone https://github.com/jerrytheo/psopy.git
$ cd psopy
$ python setup.py install

In order to run the tests,

$ python setup.py test

This library is available on the PyPI as psopy. If you have pip installed run,

$ pip install psopy

Consider the problem of minimizing the Rosenbrock function, implemented as scipy.optimize.rosen using a swarm of 1000 particles.

>>> import numpy as np >>> from psopy import minimize_pso >>> from scipy.optimize import rosen >>> x0 = np.random.uniform(0, 2, (1000, 5)) >>> res = minimize_pso(rosen, x0, options={'stable_iter': 50}) >>> res.x array([1.00000003, 1.00000017, 1.00000034, 1.0000006 , 1.00000135])

Next, we consider a minimization problem with several constraints. The intial positions for constrained optimization must adhere to the constraints imposed by the problem. This can be ensured using the provided function psopy.init_feasible. Note, there are several caveats regarding the use of this function. Consult its documentation for more information.

>>> # The objective function. >>> fun = lambda x: (x[0] - 1)2 + (x[1] - 2.5)2 >>> # The constraints. >>> cons = ({'type': 'ineq', 'fun': lambda x: x[0] - 2 * x[1] + 2}, ... {'type': 'ineq', 'fun': lambda x: -x[0] - 2 * x[1] + 6}, ... {'type': 'ineq', 'fun': lambda x: -x[0] + 2 * x[1] + 2}, ... {'type': 'ineq', 'fun': lambda x: x[0]}, ... {'type': 'ineq', 'fun': lambda x: x[1]}) >>> from psopy import init_feasible >>> x0 = init_feasible(cons, low=0., high=2., shape=(1000, 2)) >>> res = minimize_pso(fun, x0, constrainsts=cons, options={ ... 'g_rate': 1., 'l_rate': 1., 'max_velocity': 4., 'stable_iter': 50}) >>> res.x array([ 1.39985398, 1.69992748])

• Abhijit Theophilus (abhijit.theo@gmail.com)
• Dr. Snehanshu Saha (snehanshusaha@pes.edu)
• Suryoday Basak (suryodaybasak@gmail.com)

Chaotic Quantum Particle Swarm Optimization has been added to this repository by Arun John & Anish Murthy. It work similar to the pso, with added levy_rate and decay_rate which are used to turn on the levy walk and decay respectively. Example:

Unconstrained Optimization

>>> import numpy as np >>> from psopy import minimize_pso >>> from scipy.optimize import rosen >>> x0 = np.random.uniform(0, 2, (1000, 5)) >>> res = minimize_qpso(rosen, x0, options={'stable_iter': 50}) >>> res.x array([1.00000003, 1.00000017, 1.00000034, 1.0000006 , 1.00000135])