Metadata-Version: 1.1
Name: fst-pso
Version: 1.1.2
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 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. 
        
        FST-PSO is designed for real-valued multi-dimensional minimization problems.
        
        In order to use FST-PSO, the programmer must implement:
        
        * 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.
        
        
        
        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.
        
        [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>
Keywords: fuzzy logic,particle swarm optimization,optimization,pso
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.7
