def funX(self, x, y):
r"""Get x values.
Args:
x (numpy.ndarray): First krill/individual.
y (numpy.ndarray): Second krill/individual.
Returns:
numpy.ndarray: --
"""
return ((y - x) + self.epsilon) / (euclidean(y, x) + self.epsilon)
def sensRange(self, ki, KH):
r"""Calculate sense range for selected individual.
Args:
ki (int): Selected individual.
KH (numpy.ndarray): Krill heard population.
Returns:
float: Sense range for krill.
"""
return np.sum([euclidean(KH[ki], KH[i])
for i in range(self.NP)]) / (self.nn * self.NP)
def getNeighbours(self, i, ids, KH):
r"""Get neighbours.
Args:
i (int): Individual looking for neighbours.
ids (float): Maximal distance for being a neighbour.
KH (numpy.ndarray): Current population.
Returns:
numpy.ndarray: Neighbours of krill heard.
"""
N = list()
for j in range(self.NP):
if j != i and ids > euclidean(KH[i], KH[j]): N.append(j)
if not N: N.append(self.integers(self.NP))
return np.asarray(N)
def initPopulation(self, task):
r"""Initialize population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
1. Initialized population of glowwarms.
2. Initialized populations function/fitness values.
3. Additional arguments:
* L (numpy.ndarray): TODO.
* R (numpy.ndarray): TODO.
* rs (numpy.ndarray): TODO.
"""
GS, GS_f, d = Algorithm.initPopulation(self, task)
rs = euclidean(np.zeros(task.D), task.bRange)
L, R = np.full(self.NP, self.l0), np.full(self.NP, rs)
d.update({'L': L, 'R': R, 'rs': rs})
return GS, GS_f, d
def selection(self, pop, npop, xb, fxb, task, **kwargs):
r"""Operator for selection of individuals.
Args:
pop (numpy.ndarray): Current population.
npop (numpy.ndarray): New population.
xb (numpy.ndarray): Current global best solution.
fxb (float): Current global best solutions fitness/objective value.
task (Task): Optimization task.
kwargs (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, float]:
1. New population.
2. New global best solution.
3. New global best solutions fitness/objective value.
"""
P = []
for e in npop:
i = np.argmin([euclidean(e, f) for f in pop])
P.append(pop[i] if pop[i].f < e.f else e)
return np.asarray(P), xb, fxb