Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (pypi.python.org).
Help us improve Python packaging - Donate today!

Fuzzy Self-Tuning PSO global optimization library

Project Description

Fuzzy Self-Tuning PSO (FST-PSO) is a swarm intelligence global optimization method [1] based on Particle Swarm Optimization [2].

FST-PSO is designed for the optimization of real-valued multi-dimensional multi-modal minimization problems.

FST-PSO is settings-free version of PSO which exploits fuzzy logic to dynamically assign the functioning parameters to each particle in the swarm. Specifically, during each generation, FST-PSO is determines the optimal choice for the cognitive factor, the social factor, the inertia value, the minimum velocity, and the maximum velocity. FST-PSO also uses an heuristics to choose the swarm size, so that the user must not select any functioning setting.

In order to use FST-PSO, the programmer must implement a custom fitness function. Moreover, the programmer must specify the number of dimensions of the problem and the boundaries of the search space for each dimension. The programmer can optionally specify the maximum number of iterations. When the stopping criterion is met, FST-PSO returns the best fitting solution found, along with its fitness value.

Example

FST-PSO can be used as follows:

from fstpso import FuzzyPSO

def example_fitness( particle ):

return sum(map(lambda x: x**2, particle))

if __name__ == ‘__main__’:

dims = 10

FP = FuzzyPSO( D=dims )

FP.set_fitness(example_fitness)

FP.set_search_space( [[-10, 10]]*dims )

result = FP.solve_with_fstpso(max_iter=100)

print “Best solution:”, result[0]

print “Whose fitness is:”, result[1]

Further information

FST-PSO has been created by M.S. Nobile, D. Besozzi, G. Pasi, G. Mauri, R. Colombo (University of Milan-Bicocca, Italy), and P. Cazzaniga (University of Bergamo, Italy). The source code was written by M.S. Nobile.

FST-PSO requires two packages: pyfuzzy and numpy.

[1] Nobile, Cazzaniga, Besozzi, Colombo, Mauri, Pasi, “Fuzzy Self-Tuning PSO: A Settings-Free Algorithm for Global Optimization”, Swarm & Evolutionary Computation, 2017 (doi:10.1016/j.swevo.2017.09.001)

[2] Kennedy, Eberhart, Particle swarm optimization, in: Proceedings IEEE International Conference on Neural Networks, Vol. 4, 1995, pp. 1942–1948

<http://www.sciencedirect.com/science/article/pii/S2210650216303534>

Release History

Release History

This version
History Node

1.1.15

History Node

1.1.14

History Node

1.1.12

History Node

1.1.11

History Node

1.1.10

History Node

1.1.9

History Node

1.1.8

History Node

1.1.7

History Node

1.1.6

History Node

1.1.5

History Node

1.1.4

History Node

1.1.3

History Node

1.1.2

History Node

1.1.1

History Node

1.1.0

History Node

1.0.0

Download Files

Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
fst-pso-1.1.15.zip (17.0 kB) Copy SHA256 Checksum SHA256 Source Sep 13, 2017

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting