optiseek 0.2.0

A collection of single objective optimization algorithms for multi-dimensional functions.

optiseek

An open source collection of single-objective optimization algorithms for multi-dimensional functions.

The purpose of this library is to give users access to a variety of optimization algorithms with extreme ease of use and interoperability. The parameters of each of the algorithms can be tuned by the users and there is a high level of input uniformity between algorithms of similar type.

Installation

$ pip install optiseek

Usage

optiseek provides access to numerous optimization algorithms that require minimal effort from the user. An example using the well-known particle swarm optimization algorithm can be as simple as this:

from optiseek.metaheuristics import particle_swarm_optimizer
from optiseek.testfunctions import booth

# create an instance of the algorithm, set its parameters, and solve
my_algorithm = particle_swarm_optimizer(booth)  # create instance to optimize the booth function
my_algorithm.b_lower = [-10, -10] # define lower bounds
my_algorithm.b_upper = [10, 10] # define upper bounds

# execute the algorithm
my_algorithm.solve()

# show the results!
print(my_algorithm.best_value)
print(my_algorithm.best_position)
print(my_algorithm.completed_iter)

This is a fairly basic example implementation without much thought put into parameter selection. Of course, the user is free to tune the parameters of the algorithm any way they would like.

Documentation

For full documentation, visit the github pages site.

License

optiseek was created by Alex Dundore. It is licensed under the terms of the MIT license.

Credits and Dependencies

optiseek is powered by numpy.