01.from numpy import array

02.from random import random

03.from math import sin, sqrt

04.

05.iter_max = 10000

06.pop_size = 100

07.dimensions = 28.c1 = 29.c2 = 2

10.err_crit = 0.00001

11.

12.class Particle:

13.    pass

14.

15.def f6(param):

16.    '''Schaffer's F6 function'''

17.    para = param*10

18.    para = param[0:2]

19.    num = (sin(sqrt((para[0] * para[0]) + (para[1] * para[1])))) * \

20.        (sin(sqrt((para[0] * para[0]) + (para[1] * para[1])))) - 0.5

21.    denom = (1.0 + 0.001 * ((para[0] * para[0]) + (para[1] * para[1]))) * \

22.            (1.0 + 0.001 * ((para[0] * para[0]) + (para[1] * para[1])))

23.    f6 =  0.5 - (num/denom)

24.    errorf6 = 1 - f6

25.    return f6, errorf6;

26.

27.#initialize the particles

28.particles = []

29.for i in range(pop_size):

30.    p = Particle()

31.    p.params = array([random() for i in range(dimensions)])

32.    p.fitness = 0.0

33.    p.v = 0.0

34.    particles.append(p)

35.

36.# let the first particle be the global best

37.gbest = particles[0]

38.err = 999999999

39.while i < iter_max :

40.    for p in particles:

41.        fitness,err = f6(p.params)

42.        if fitness > p.fitness:

43.            p.fitness = fitness

44.            p.best = p.params

45.

46.        if fitness > gbest.fitness:

47.            gbest = p

48.        v = p.v + c1 * random() * (p.best - p.params) \

49.                + c2 * random() * (gbest.params - p.params)

50.        p.params = p.params + v

51.

52.    i  += 1

53.    if err < err_crit:

54.        break

55.    #progress bar. '.' = 10%

56.    if i % (iter_max/10) == 0:

57.        print '.'

58.

59.print '\nParticle Swarm Optimisation\n'

60.print 'PARAMETERS\n','-'*9

61.print 'Population size : ', pop_size

62.print 'Dimensions      : ', dimensions

63.print 'Error Criterion : ', err_crit

64.print 'c1              : ', c1

65.print 'c2              : ', c2

66.print 'function        :  f6'

67.

68.print 'RESULTS\n', '-'*7

69.print 'gbest fitness   : ', gbest.fitness

70.print 'gbest params    : ', gbest.params

71.print 'iterations      : ', i+1

72.## Uncomment to print particles

73.#for p in particles:

74.#    print 'params: %s, fitness: %s, best: %s' % (p.params, p.fitness, p.best)