def constriction_factor_particle_swarm_test(Tx, Ty):
particles = []
pbestvector = []
lenTx = len(Tx)
lenTy = len(Ty)
# global objfitness
n = random.randint(0, 10)
for i in range(20):
Sx = gd.get_xdata(0, 500, lenTx - 2)
Sy = gd.get_ydata(0, 500, lenTx - 2)
Xs = reduce((lambda x, y: x + y), Sx) // len(Sx)
Ys = reduce((lambda x, y: x + y), Sy) // len(Sy)
particle = Particle(Sx, Sy, 0, Xs, Ys)
particles.append(particle)
pbestvector.append(math.inf)
for i in range(20):
# objfitness = []
for j in range(len(particles)):
mst = get_fitness(Tx, Ty, particles[j])
if mst < pbestvector[j]:
pbestvector[j] = mst
Rx = Tx[0:len(Tx) - lenTx]
Ry = Ty[0:len(Tx) - lenTy]
Tx = Tx[0:len(Tx) - len(Rx)]
Ty = Ty[0:len(Ty) - len(Ry)]
best_obj_fitness = min(pbestvector)
for j in range(len(particles)):
constriction_factor_particle_swarm_optimization(best_obj_fitness, particles[j], pbestvector[j])
particle_best = particles[pbestvector.index(min(pbestvector))]
return particle_best
def create_particles(Tx, Ty, len_S, particles):
global particle_no # Global Variables declared at the begening of the program
# Creating and initiating the Particles
for _ in range(particle_no):
Sx = gd.get_xdata(0, 500, len_S)
Sy = gd.get_ydata(0, 500, len_S)
particle = Particle(Sx, Sy)
particle.fitness = get_fitness(np.copy(Tx), np.copy(Ty), particle)
particles.append(particle)
def iwo_test(Tx, Ty):
lenTx = len(Tx)
lenTy = len(Ty)
weedlist = []
for i in range(10):
Wx = gd.get_xdata(0, 500, 8)
Wy = gd.get_ydata(0, 500, 8)
Xp = reduce((lambda x, y: x + y), Wx) // len(Wx)
Yp = reduce((lambda x, y: x + y), Wy) // len(Wy)
weed = Weed(Wx, Wy, Xp, Yp, 0)
weedlist.append(weed)
# iwo_algorithm(Tx,Ty,weedlist,itermax,pmax,Smax,Smin,sigmafinal,sigmainit,n)
wlist = iwo_algorithm(Tx, Ty, weedlist, 5, 150, 10, 1, 0.01, 1, 3)
fmax = get_max_fitness(wlist)
for weed in wlist:
if fmax == weed.fitness:
bestweed = weed
break
Rx = Tx[0:len(Tx) - lenTx]
Ry = Ty[0:len(Ty) - lenTy]
Tx = Tx[0:len(Tx) - len(Rx)]
Ty = Ty[0:len(Ty) - len(Ry)]
print("X-Coordinates of best weed",
list(map(lambda x: math.ceil(x), bestweed.Wx)))
print("Y-Coordinates of best weed",
list(map(lambda y: math.ceil(y), bestweed.Wy)))
count = 0
bestweed.Wx = list(map(lambda x: math.ceil(x), bestweed.Wx))
bestweed.Wy = list(map(lambda y: math.ceil(y), bestweed.Wy))
for i in range(len(bestweed.Wx)):
if bestweed.Wx[i] < min(Tx) or bestweed.Wx[i] > max(Tx):
continue
if bestweed.Wy[i] < min(Ty) or bestweed.Wy[i] > max(Ty):
continue
Tx.append(bestweed.Wx[i])
Ty.append(bestweed.Wy[i])
count = count + 1
print("Updated X Coordinates", Tx)
print("Updated Y Coordinates", Ty)
distancevector = gd.get_distancevector(Tx, Ty)
mst = pa.mst_prim(distancevector)
mst_size = pa.get_tree(distancevector)
print("Size of Steiner Tree for IWO", mst_size)
pa.draw_gridgraph(Tx, Ty, mst, lenTx, lenTy)
Tx = Tx[0:len(Tx) - count]
Ty = Ty[0:len(Ty) - count]
print("Restored X Coordinates=", Tx)
print("Restored Y Coordinates=", Ty)
print("End of IWO")
def pigeon_test(Tx, Ty):
pigeons = []
global objfitness
#n = random.randint(0, 10)
lenTx = len(Tx)
lenTy = len(Ty)
for i in range(150): # No. of Pigeons
Sx = gd.get_xdata(0, 500, lenTx - 2)
Sy = gd.get_ydata(0, 500, lenTy - 2)
Xs = reduce((lambda x, y: x + y), Sx) // len(Sx)
Ys = reduce((lambda x, y: x + y), Sy) // len(Sy)
pigeon = Pigeon(Sx, Sy, 0, Xs, Ys, 0)
pigeons.append(pigeon)
for i in range(10):
for j in range(len(pigeons)):
#Txnew = Tx[:]
#Tynew = Ty[:]
mst = get_fitness(Tx, Ty, pigeons[j])
pigeons[j].fitness = 1 / mst
Rx = Tx[0:len(Tx) - lenTx]
Ry = Ty[0:len(Tx) - lenTy]
Tx = Tx[0:len(Tx) - len(Rx)]
Ty = Ty[0:len(Ty) - len(Ry)]
#get_original_list(Tx,Ty,lenTx,lenTy)
# Tx = Tx[0:len(Tx) - len(pigeons[j].Sx)]
# print(Tx)
# Ty = Ty[0:len(Ty) - len(pigeons[j].Sy)]
# print(Ty)
best_fitness = max(pigeons, key=lambda pigeon: pigeon.fitness).fitness
for j in range(len(pigeons)):
pigeon_optimization_map_and_compass(best_fitness, pigeons[j], i)
pigeon_optimization_landmark(Tx, Ty, lenTx, lenTy, pigeons)
pigeon_best = max(pigeons, key=lambda pigeon: pigeon.fitness)
print(pigeon_best.Sx)
print(pigeon_best.Sy)
return pigeon_best
def pigeon_test(Tx, Ty):
pigeons = []
#global objfitness
#n = random.randint(0, 10)
lenTx = len(Tx)
lenTy = len(Ty)
for i in range(150):
Sx = gd.get_xdata(0, 500, lenTx - 2)
Sy = gd.get_ydata(0, 500, lenTy - 2)
len_S = Sx.size
#Xs = reduce((lambda x, y: x + y), Sx) // len(Sx)
#Ys = reduce((lambda x, y: x + y), Sy) // len(Sy)
Xs = np.sum(Sx) // len_S
Ys = np.sum(Sy) // len_S
pigeon = Pigeon(Sx, Sy, 0, Xs, Ys,0)
pigeons.append(pigeon)
for i in range(15):
for j in range(len(pigeons)):
mst = get_fitness(Tx, Ty, pigeons[j])
pigeons[j].fitness = 1/mst
temp_len = Tx.size
Rx = Tx[0:temp_len - lenTx]
len_Rx = Rx.size
Tx = Tx[0:temp_len - len_Rx]
Ty = Ty[0:temp_len - len_Rx]
# Tx = Tx[0:len(Tx) - len(pigeons[j].Sx)]
# print(Tx)
# Ty = Ty[0:len(Ty) - len(pigeons[j].Sy)]
# print(Ty)
best_fitness = max(pigeons, key = lambda pigeon: pigeon.fitness).fitness
for j in range(len(pigeons)):
pigeon_optimization_map_and_compass(best_fitness, pigeons[j], i)
pigeon_optimization_landmark(Tx,Ty,lenTx,lenTy,pigeons)
pigeon_best = max(pigeons, key = lambda pigeon: pigeon.fitness)
#print(pigeon_best.Sx)
#print(pigeon_best.Sy)
return pigeon_best
def particle_swarm_test(Tx, Ty):
particles = []
pbestvector = []
lenTx = Tx.size
lenTy = lenTx
for i in range(150):
len_S = lenTx - 2
Sx = gd.get_xdata(0, 500, len_S)
Sy = gd.get_ydata(0, 500, len_S)
#Xs = reduce((lambda x, y: x+y), Sx) // len(Sx)
#Ys = reduce((lambda x, y: x+y), Sy) // len(Sy)
Xs = np.sum(Sx) // len_S
Ys = np.sum(Sy) // len_S
particle = Particle(Sx, Sy, 0, Xs, Ys)
particles.append(particle)
pbestvector.append(math.inf)
for i in range(15):
#objfitness = []
for j in range(len(particles)):
mst = get_fitness(Tx, Ty, particles[j])
if mst < pbestvector[j]:
pbestvector[j] = mst
temp_len = Tx.size
Rx = Tx[0:temp_len - lenTx]
Ry = Ty[0:temp_len - lenTy]
len_Rx = Rx.size
Tx = Tx[0:temp_len - len_Rx]
Ty = Ty[0:temp_len - len_Rx]
best_obj_fitness = min(pbestvector)
for j in range(len(particles)):
particle_swarm_optimization(best_obj_fitness, particles[j],
pbestvector[j])
#print("X Coordinates of particle",particles[j].Sx)
#print("Y Coordinates of particle",particles[j].Sy)
particle_best = particles[pbestvector.index(min(pbestvector))]
return particle_best
import prim_algorithm as pa
import gridgraphdemo as gd
import pigeon_optimization_algorithm as poa
import particle_swarm_optimization as pso
import constriction_factor_pso as cpso
Tx = gd.get_xdata(0, 500, 20) #Change values over here and test
Ty = gd.get_ydata(0, 500, 20) #Change values over here and test
lenTx = len(Tx)
lenTy = len(Ty)
print("X Coordinates", Tx)
print("Y Coordinates", Ty)
distancevector = gd.get_distancevector(Tx, Ty)
mst = pa.mst_prim(distancevector)
mst_size = pa.get_tree(distancevector)
print("Size of the MST = ", mst_size)
pa.draw_gridgraph(Tx, Ty, mst, lenTx, lenTy)
poa.call_methods(Tx, Ty, lenTx, lenTy)
pso.call_methods(Tx, Ty, lenTx, lenTy)
cpso.call_methods(Tx, Ty, lenTx, lenTy)
Ty.append(bestweed.Wy[i])
count = count + 1
print("Updated X Coordinates", Tx)
print("Updated Y Coordinates", Ty)
distancevector = gd.get_distancevector(Tx, Ty)
mst = pa.mst_prim(distancevector)
mst_size = pa.get_tree(distancevector)
print("Size of Steiner Tree for IWO", mst_size)
pa.draw_gridgraph(Tx, Ty, mst, lenTx, lenTy)
Tx = Tx[0:len(Tx) - count]
Ty = Ty[0:len(Ty) - count]
print("Restored X Coordinates=", Tx)
print("Restored Y Coordinates=", Ty)
print("End of IWO")
Tx = gd.get_xdata(0, 500, 10)
Ty = gd.get_ydata(0, 500, 10)
print(Tx)
print(Ty)
lenTx = len(Tx)
lenTy = len(Ty)
print("X Coordinates", Tx)
print("Y Coordinates", Ty)
distancevector = gd.get_distancevector(Tx, Ty)
mst = pa.mst_prim(distancevector)
mst_size = pa.get_tree(distancevector)
print("Size of the MST = ", mst_size)
pa.draw_gridgraph(Tx, Ty, mst, lenTx, lenTy)
iwo_test(Tx, Ty)
import gridgraphdemo as gd
import numpy as np
dim = int(input('Enter the dimension of the search space : '))
limit = int(input('Enter the limit of terminal points : '))
for i in range(10, limit + 10, 10):
Tx = gd.get_xdata(0, dim, i)
Ty = gd.get_ydata(0, dim, i)
filename = 'terminal_point_{0}_{1}'.format(str(dim), str(i))
np.save(filename, np.array([Tx, Ty]))
