def printPos(self):
self.formattedList = []
for item in self.output:
for i in item:
# print(i)
self.formattedList.append("%.2f" % i)
print(
"\tLocation:",
self.currentPos,
"\tFitness:{0:.3f}".format(fit.rastrigin(self.currentPos)),
)
def printVals(self):
self.formattedList = []
for item in self.output:
for i in item:
# print(i)
self.formattedList.append("%.2f" % i)
# print(self.name)
# print('\tGlobal Best:',self.globalBest)
print("\tLocal Best:", self.localBest)
print("\tSelf Best:", self.selfBest)
print("\tCurrent Location:", self.currentPos)
print("\tCurrent Velocity:", self.currentVel)
print("Fitness:{0:.3f}".format(fit.rastrigin(self.currentPos)))
def resetPos(swarm):
fits = []
for particle in swarm:
fits.append(
[fit.rastrigin(particle.currentPos), particle.name, particle.currentPos]
)
fits.sort()
fits = fits[-2:]
# print('\n',fits,'\n')
for particle in swarm:
if particle.name == fits[0][1]:
particle.currentPos = [np.random.random() * 5.8, np.random.random() * 5.8]
elif particle.name == fits[1][1]:
particle.currentPos = [np.random.random() * 5.8, np.random.random() * 5.8]
def swapPos(swarm):
fits = []
for particle in swarm:
fits.append(
[fit.rastrigin(particle.currentPos), particle.name, particle.currentPos]
)
fits.sort()
fits = fits[-2:]
# print('\n',fits,'\n')
for particle in swarm:
if particle.name == fits[0][1]:
particle.currentPos = fits[1][2]
elif particle.name == fits[1][1]:
particle.currentPos = fits[0][2]
def updateLocal(swarm: Swarm):
finalDist = []
for particle1 in swarm:
dist = []
for particle2 in swarm:
if particle1.name == particle2.name:
continue
dist.append(
[
distance(particle1.currentPos, particle2.currentPos),
particle2.name,
fit.rastrigin(particle2.currentPos),
particle2.currentPos,
]
)
dist.sort()
finalDist.append(dist[:n])
counter = 0
for d in finalDist:
d.sort(key=lambda x: x[2])
swarm[counter].localBest = d[0][3]
counter += 1
def main():
swarm = []
# particle initialization
for name in swarmNames:
swarm.append(Particle.New(name))
# finds the best position from the init particle locations
bestG = 9999
for particle in swarm:
if fit.rastrigin(particle.currentPos) < bestG:
loc = particle.currentPos
bestG = fit.rastrigin(particle.currentPos)
for particle in swarm:
particle.updateGlobal(loc)
updateGlobal(loc, swarm)
# printParts(swarm) # [particle.printVals() for particle in swarm] # prints particles
updateLocal(swarm)
printParts(swarm)
swapNums = 0
start = timeit.default_timer()
iterations = 10000
for i in range(iterations):
for particle in swarm:
# if i == 0 or i == 100 or i == 1000 or i == 5000:
# particle.printPos()
# Part 1: If current position is less than the personal best,
if fit.rastrigin(particle.currentPos) < fit.rastrigin(particle.selfBest):
# Update personal best
particle.selfBest = particle.currentPos
# Part 2: If current pos is less than global best,
if fit.rastrigin(particle.currentPos) < fit.rastrigin(particle.globalBest):
updateGlobal(swarm, particle.currentPos)
# Update global best
# Part 3: If personal best is less than local best,
# if fit.rastrigin(particle.currentPos) < fit.rastrigin(particle.localBest):
# Update global best
# updateLocal(swarm)
# Part 4: Update velocity and position matrices
update_velocity(particle)
update_position(particle)
# if i % 50== 0:
# print('Iterations:',i)
# particle.printVals()
"""
if randVal() < p:
resetPos(swarm)
swapNums += 1
"""
stop = timeit.default_timer()
print("##################################")
bestVal = 9999
for particle in swarm:
# particle.printVals()
particle.printPos()
if fit.rastrigin(particle.currentPos) <= bestVal:
bestVal = fit.rastrigin(particle.currentPos)
best = particle
print("W:", w, " C:", C, " S1:", S, " S2:", S2, " N:", n, " p:", p)
print("Number of swaps:", swapNums)
# best.printPos()
print("Time to run: {0:.3f}".format(stop - start))