Metadata-Version: 1.1
Name: fst-pso
Version: 1.0.0
Summary: Fuzzy Self-Tuning PSO global optimization library
Home-page: http://pypi.python.org/pypi/fst-pso/
Author: Marco S. Nobile
Author-email: nobile@disco.unimib.it
License: LICENSE.txt
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>`_
Keywords: fuzzy logic,particle swarm optimization,optimization,pso
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.7
