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 versatile black-box 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 parameter uniformity between algorithms.

Benefits of using Optiseek include:

• support for float, integer, categorical, and boolean inputs for objective functions
• compatibility with black-box objective functions (requires no information on gradients or form of function)
• the algorithms are simple compared to alternatives (e.g. Bayesian optimization) with faster runtime on basic objective functions
• competitive convergence for computionally expensive objective functions in terms of number of function evaluations
• seamless integration into ML pipelines for hyper-parameter tuning
• access to a variety of stopping criteria, suitable for both computationally expensive and cheap objective functions
• carefully chosen default parameters for algorithms, with ability for user-defined fine tuning

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

# define a list of variables and their domains for the objective function
var_list = [
    var_float('x1', [-10, 10]),
    var_float('x2', [-10, 10])
]   

# create an instance of the algorithm to optimize the booth test function and set its parameters
alg = particle_swarm_optimizer(booth, var_list)

# define stopping criteria and optimize
alg.optimize(find_minimum=True, max_iter=10)

# show the results!
print(f'best_value = {alg.best_value:.5f}')
print(f'best_position = {alg.best_position}')
print(f'n_iter = {alg.completed_iter}')

best_value = 0.00217
best_position = {'x1': 0.9921537320723116, 'x2': 3.0265668104168326}
n_iter = 10

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.

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.