During a “vanity search” of this blog “btbytes.github.io”, I found out that some of my code have been quoted in published books:
(A Particle Swarm Optimization) implementation in python in “Intelligent IoT Projects in 7 Days”. I remember writing this code for Dr Eberhart’s “Computation Intelligence” class at IUPUI.
The code in question:
#!/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)That’s in Python2.x. Should probably rewrite it in Python3 one of these days.
A link to my PyOFC2 Python charting library in “Building a Cloud Computing Service: An Introduction to the Cloud”.
