Fuzzy Self-Tuning PSO global optimization library
Project description
=====================
Fuzzy Self-Tuning PSO
=====================
*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 real-valued
multi-dimensional minimization problems.
FST-PSO can be used as follows:
from fstpso import FuzzyPSO
# example of fitness function (min x^2)
def fitnessfunction(x):
return x**2
dims = 10 # number of dimensions of the problem
FP = FuzzyPSO( D=dims )
FP.set_fitness( fitnessfunction )
FP.set_search_space( [[-30, 30]]*dims ) # definition of the search space
result = FP.solve_with_fstpso()
print "Best solution:", result[0]
print "Whose fitness is:", result[1]
Basics
======
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.
The programmer must specify:
* a custom fitness function;
* the number of dimensions of the problem;
* the boundaries of the search space for each dimension.
The programmer can also specify the maximum number of fitness evaluations.
FST-PSO returns the best fitting solution along with its fitness value.
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.
Further information:
[1] Nobile, Cazzaniga, Besozzi, Colombo, Mauri, Pasi, "Fuzzy Self-Tuning PSO:
A Settings-Free Algorithm for Global Optimization", Swarm & Evolutionary
Computation, 2017 (in press)
[2] Kennedy, Eberhart, Particle swarm optimization, in: Proceedings IEEE
International Conference on Neural Networks, Vol. 4, 1995, pp. 1942–1948
<http://www.disco.unimib.it/go/45712>`_
Fuzzy Self-Tuning PSO
=====================
*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 real-valued
multi-dimensional minimization problems.
FST-PSO can be used as follows:
from fstpso import FuzzyPSO
# example of fitness function (min x^2)
def fitnessfunction(x):
return x**2
dims = 10 # number of dimensions of the problem
FP = FuzzyPSO( D=dims )
FP.set_fitness( fitnessfunction )
FP.set_search_space( [[-30, 30]]*dims ) # definition of the search space
result = FP.solve_with_fstpso()
print "Best solution:", result[0]
print "Whose fitness is:", result[1]
Basics
======
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.
The programmer must specify:
* a custom fitness function;
* the number of dimensions of the problem;
* the boundaries of the search space for each dimension.
The programmer can also specify the maximum number of fitness evaluations.
FST-PSO returns the best fitting solution along with its fitness value.
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.
Further information:
[1] Nobile, Cazzaniga, Besozzi, Colombo, Mauri, Pasi, "Fuzzy Self-Tuning PSO:
A Settings-Free Algorithm for Global Optimization", Swarm & Evolutionary
Computation, 2017 (in press)
[2] Kennedy, Eberhart, Particle swarm optimization, in: Proceedings IEEE
International Conference on Neural Networks, Vol. 4, 1995, pp. 1942–1948
<http://www.disco.unimib.it/go/45712>`_
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