def optimal_solution(self, k, l, pos_params):
"""Create one Pareto-optimal solution with given position parameters."""
# the result vector
result = []
result.extend(pos_params)
# set the distance parameters
for i in range(k, k + l):
result.append(0.35)
# scale to the correct domains
for i in range(k + l):
result[i] *= 2.0 * (i + 1)
ind = Individual()
ind.phenome = result
return ind
def optimal_solution(self, k, l, pos_params):
"""Create one Pareto-optimal solution with given position parameters."""
result = []
result.extend(pos_params)
# calculate the distance parameters
for i in range(k, k + l):
w = [1.0] * len(result)
u = r_sum(result, w)
tmp1 = abs(math.floor(0.5 - u) + 0.98 / 49.98)
tmp2 = 0.02 + 49.98 * (0.98 / 49.98 - (1.0 - 2.0 * u) * tmp1)
result.append(pow(0.35, pow(tmp2, -1.0)))
# scale to the correct domains
for i in range(k + l):
result[i] *= 2.0 * (i + 1)
ind = Individual()
ind.phenome = result
return ind
def optimal_solution(self, k, l, pos_params):
"""Create one Pareto-optimal solution with given position parameters."""
result = []
result.extend(pos_params)
result.extend([0.0] * l)
# calculate the distance parameters
result[k + l - 1] = 0.35
for i in range(k + l - 2, k - 1, -1):
result_sub = []
for j in range(i + 1, k + l):
result_sub.append(result[j])
w = [1.0] * len(result_sub)
tmp1 = r_sum(result_sub, w)
result[i] = pow(0.35, pow(0.02 + 1.96 * tmp1, -1.0))
# scale to the correct domains
for i in range(k + l):
result[i] *= 2.0 * (i + 1)
ind = Individual()
ind.phenome = result
return ind
