def _business_three(self, agents, function):
"""Performs the third business phase.
Args:
agents (list): List of agents.
function (Function): A Function object that will be used as the objective function.
"""
# Sorts the agents
agents.sort(key=lambda x: x.fit)
# Calculates the probability of handling the business
pr = [i / len(agents) for i in range(1, len(agents) + 1)]
# Iterates through all agents
for i, agent in enumerate(agents):
# Creates another temporary agent
a = copy.deepcopy(agent)
# Iterates through all decision variables
for j in range(agent.n_variables):
# Generates a uniform random number
r1 = r.generate_uniform_random_number()
# If random number is smaller than probability of handling the business
if r1 < pr[i]:
# Randomly selects two individuals
A_1, A_2 = np.random.choice(agents, 2, replace=False)
# Generates an Erlang number
e = r.generate_gamma_random_number(1, 0.5, 1)
# Updates temporary agent's position (Eq. 17)
a.position[j] = A_1.position[j] + e * (A_2.position[j] -
a.position[j])
# Evaluates the agent
a.fit = function(a.position)
# If the new fitness is better than the current agent's fitness
if a.fit < agent.fit:
# Replaces the current agent's position
agent.position = copy.deepcopy(a.position)
# Also replaces its fitness
agent.fit = copy.deepcopy(a.fit)
def test_generate_gamma_random_number():
gamma_array = random.generate_gamma_random_number(1, 1, 5)
assert gamma_array.shape == (5, )
def _business_two(self, agents: List[Agent], function: Function) -> None:
"""Performs the second business phase.
Args:
agents: List of agents.
function: A Function object that will be used as the objective function.
"""
# Sorts agents
agents.sort(key=lambda x: x.fit)
# Copies temporary agents to represent `A_1`, `A_2` and `A_3`
A_1, A_2, A_3 = (
copy.deepcopy(agents[0]),
copy.deepcopy(agents[1]),
copy.deepcopy(agents[2]),
)
# Calculates the number of agents in each queue
q_1, q_2, _ = self._calculate_queue(len(agents), A_1.fit, A_2.fit,
A_3.fit)
# Calculates the probability of handling the business
pr = [i / len(agents) for i in range(1, len(agents) + 1)]
# Calculates the confusion degree
cv = A_1.fit / (A_2.fit + A_3.fit + c.EPSILON)
# Iterates through all agents
for i, agent in enumerate(agents):
# Creates another temporary agent
a = copy.deepcopy(agent)
# If index is smaller than the number of agents in first queue
if i < q_1:
# `A` will receive a copy from `A_1`
A = copy.deepcopy(A_1)
# If index is between first and second queues
elif q_1 <= i < q_1 + q_2:
# `A` will receive a copy from `A_2`
A = copy.deepcopy(A_2)
# If index is between second and third queues
else:
# `A` will receive a copy from `A_3`
A = copy.deepcopy(A_3)
# Generates a uniform random number
r1 = r.generate_uniform_random_number()
# If random number is smaller than probability of handling the business
if r1 < pr[i]:
# Randomly selects two individuals
A_1, A_2 = np.random.choice(agents, 2, replace=False)
# Generates another uniform random number
r2 = r.generate_uniform_random_number()
# Generates an Erlang number
e = r.generate_gamma_random_number(1, 0.5, 1)
# If random number is smaller than confusion degree
if r2 < cv:
# Calculates the fluctuation (eq. 14)
F_1 = e * (A_1.position - A_2.position)
# Update agent's position (eq. 12)
a.position += F_1
# If random number is bigger than confusion degree
else:
# Calculates the fluctuation (eq. 15)
F_2 = e * (A.position - A_1.position)
# Update agent's position (eq. 13)
a.position += F_2
# Evaluates the agent
a.fit = function(a.position)
# If the new fitness is better than the current agent's fitness
if a.fit < agent.fit:
# Replaces the current agent's position and fitness
agent.position = copy.deepcopy(a.position)
agent.fit = copy.deepcopy(a.fit)
# If index is between second and third queues
else:
# If index is the first agent in third queue
if i == q_1 + q_2:
# Defines the case as one
case = 1
# `A` will receive a copy from `A_3`
A = copy.deepcopy(A_3)
# Generates a uniform random number
alpha = r.generate_uniform_random_number(-1, 1)
# Generates an Erlang distribution
E = r.generate_gamma_random_number(
1, 0.5, (agent.n_variables, agent.n_dimensions))
# If case is defined as one
if case == 1:
# Generates an Erlang number
e = r.generate_gamma_random_number(1, 0.5, 1)
# Calculates the fluctuation (eq. 6)
F_1 = beta * alpha * (E * np.fabs(A.position - a.position)
) + e * (A.position - a.position)
# Updates the temporary agent's position (eq. 4)
a.position = A.position + F_1
# Evaluates the agent
a.fit = function(a.position)
import opytimizer.math.random as r # Generating a binary random number array b = r.generate_binary_random_number(size=10) print(b) # Generating an Erlang/gamma random number array e = r.generate_gamma_random_number(shape=1.0, scale=1.0, size=10) print(e) # Generating an integer random number array i = r.generate_integer_random_number(low=0, high=1, size=1) print(i) # Generating a random uniform number array u = r.generate_uniform_random_number(low=0.0, high=1.0, size=1) print(u) # Generating a random gaussian number array g = r.generate_gaussian_random_number(mean=0.5, variance=1.0, size=10) print(g)