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 installIn order to run the tests,
$ python setup.py testThis library is available on the PyPI as psopy. If you have pip installed run,
$ pip install psopyConsider 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
>>> from scipy.optimize import rosen
>>> x0 = np.random.uniform(0, 2, (1000, 5))
>>> res = minimize(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(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 ([email protected])
- Dr. Snehanshu Saha ([email protected])
- Suryoday Basak ([email protected])
| [1] | Theophilus, A., Saha, S., Basak, S. and Murthy, J., 2018. A Novel Exoplanetary Habitability Score via Particle Swarm Optimization of CES Production Functions. arXiv preprint arXiv:1805.08858. |
| [2] | Ray, T. and Liew, K.M., 2001. A swarm with an effective information sharing mechanism for unconstrained and constrained single objective optimisation problems. In Evolutionary Computation, 2001. Proceedings of the 2001 Congress on (Vol. 1, pp. 75-80). IEEE. |
| [3] | Eberhart, R. and Kennedy, J., 1995, October. A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE. |
| [4] | Shi, Y. and Eberhart, R., 1998, May. A modified particle swarm optimizer. In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on (pp. 69-73). IEEE. |