def __init__(self, dimension, bound, d_function, n_nest):
self.pa = 0.25 #parameter
self.dimension = dimension
self.bound = bound
self.d_function = d_function
self.nests = [(d_function(*p), p) for p in lhs.latin_hypercube(dimension, bound, n_nest)]
self.best_value, self.best = max(self.nests)
def __init__(self, dimension, bound, d_function, n_nest):
self.pa = 0.25 #parameter
self.dimension = dimension
self.bound = bound
self.d_function = d_function
self.nests = [(d_function(*p), p)
for p in lhs.latin_hypercube(dimension, bound, n_nest)]
self.best_value, self.best = max(self.nests)
def optimize(dimension, boundary, function_d, n_parts, n_turns):
"""Optimize *function_d* of given *dimension* in space bounded by
symmetrical *boundary*. Use *n_parts* particles for *n_turn* turns."""
pool = multiprocessing.Pool(None)
v_max = boundary
particles = [
particle(dimension, boundary, v_max, function_d, pos)
for pos in lhs.latin_hypercube(dimension, bound, n_parts)
]
gl_best = max(particles)
for _ in xrange(n_turns):
move_p = partial(move,
omega=0.98,
phi_p=2.75,
phi_g=3.,
v_max=v_max,
global_best=gl_best[1],
func_d=function_d)
particles = pool.map(move_p, particles)
gl_best = max(max(particles), gl_best)
pool.terminate()
pool.join()
return gl_best[1]