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Created March 16, 2009 13:32
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A simple Particle Swarm Optimisation implementation in Python
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 *~ *.pyc

# A Particle Swarm Implementation in Python

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 #!/usr/bin/env python ''' pso.py A simple implementation of the Particle Swarm Optimisation Algorithm. Uses Numpy for matrix operations. Pradeep Gowda 2009-03-16 ''' from numpy import array from random import random from math import sin, sqrt iter_max = 10000 pop_size = 100 dimensions = 2 c1 = 2 c2 = 2 err_crit = 0.00001 class Particle: pass def f6(param): '''Schaffer's F6 function''' para = param*10 para = param[0:2] num = (sin(sqrt((para[0] * para[0]) + (para[1] * para[1])))) * \ (sin(sqrt((para[0] * para[0]) + (para[1] * para[1])))) - 0.5 denom = (1.0 + 0.001 * ((para[0] * para[0]) + (para[1] * para[1]))) * \ (1.0 + 0.001 * ((para[0] * para[0]) + (para[1] * para[1]))) f6 = 0.5 - (num/denom) errorf6 = 1 - f6 return f6, errorf6; #initialize the particles particles = [] for i in range(pop_size): p = Particle() p.params = array([random() for i in range(dimensions)]) p.fitness = 0.0 p.v = 0.0 particles.append(p) # let the first particle be the global best gbest = particles[0] err = 999999999 while i < iter_max : for p in particles: fitness,err = f6(p.params) if fitness > p.fitness: p.fitness = fitness p.best = p.params if fitness > gbest.fitness: gbest = p v = p.v + c1 * random() * (p.best - p.params) \ + c2 * random() * (gbest.params - p.params) p.params = p.params + v i += 1 if err < err_crit: break #progress bar. '.' = 10% if i % (iter_max/10) == 0: print '.' print '\nParticle Swarm Optimisation\n' print 'PARAMETERS\n','-'*9 print 'Population size : ', pop_size print 'Dimensions : ', dimensions print 'Error Criterion : ', err_crit print 'c1 : ', c1 print 'c2 : ', c2 print 'function : f6' print 'RESULTS\n', '-'*7 print 'gbest fitness : ', gbest.fitness print 'gbest params : ', gbest.params print 'iterations : ', i+1 ## Uncomment to print particles #for p in particles: # print 'params: %s, fitness: %s, best: %s' % (p.params, p.fitness, p.best)

### DMandy commented Apr 18, 2017

Thanks for putting this here. I studied your code for my first class project.

### nam77156 commented Jun 1, 2018

thanks you very much, love you

### konark51 commented Jan 29, 2019

I think we also need to update the particle velocity after calculating the updated particle position

``````v = p.v + c1 * random() * (p.best- p.param) \
+ c2 * random() * (gbest.param- p.param)
p.param = p.param + v
p.v= v
``````