def generate(histogram, ubound, lbound, size, u, lb, m):
agent = creator.Agent(
[random.uniform(ubound, lbound),
random.uniform(u, lb)] for _ in range(size))
agent.best = agent
agent.fitness = toolbox.evaluate(agent=agent, data=histogram, m=m)
return agent
def main():
Swarm = toolbox.swarm(n = 5) #intializing the swarm with n agents.
GEN = 2000 #the numer of max iterations.
best = None #initializing the best as none.
#print(Swarm)
for agent in Swarm: #finding the best position for the initial swarm/population
if not best or best.fitness > agent.fitness:
best = creator.Agent(agent)
best.fitness = agent.fitness
#print(best)
for g in range(GEN): #runing the GAO Gen times
c = compute_c(g + 1, GEN) #computing c
for agent in Swarm: #updating each agent using the best solution found
toolbox.update(agent = agent, Population = Swarm, c = c, best = best )
for agent in Swarm: #updating the best solution after updating the whole swarm.
if not best or best.fitness > agent.fitness:
best = creator.Agent(agent)
best.fitness = agent.fitness
print(best,best.fitness)
def generate():
agent = creator.Agent([random.uniform(ubound, lbound), random.uniform(u, 0)] for _ in range(size))
agent.best = agent
agent.fitness = toolbox.evaluate(agent = agent)
return agent