# This file is part of NEORL.
# Copyright (c) 2021 Exelon Corporation and MIT Nuclear Science and Engineering
# NEORL is free software: you can redistribute it and/or modify
# it under the terms of the MIT LICENSE
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# -*- coding: utf-8 -*-
#"""
#Created on Thu Dec 3 14:42:29 2020
#
#@author: Majdi
#"""
import random
import numpy as np
import math
import time
import joblib
from neorl.evolu.discrete import mutate_discrete, encode_grid_to_discrete
from neorl.evolu.discrete import decode_discrete_to_grid, encode_grid_indv_to_discrete
from neorl.utils.seeding import set_neorl_seed
from neorl.utils.tools import get_population, check_mixed_individual
[docs]class WOA(object):
"""
Whale Optimization Algorithm
:param mode: (str) problem type, either ``min`` for minimization problem or ``max`` for maximization
:param bounds: (dict) input parameter type and lower/upper bounds in dictionary form. Example: ``bounds={'x1': ['int', 1, 4], 'x2': ['float', 0.1, 0.8], 'x3': ['float', 2.2, 6.2]}``
:param fit: (function) the fitness function
:param nwhales: (int): number of whales in the population
:param a0: (float): initial value for coefficient ``a``, which is annealed from ``a0`` to 0 (see **Notes** below for more info).
:param b: (float): constant for defining the shape of the logarithmic spiral
:param int_transform: (str): method of handling int/discrete variables, choose from: ``nearest_int``, ``sigmoid``, ``minmax``.
:param ncores: (int) number of parallel processors (must be ``<= nwhales``)
:param seed: (int) random seed for sampling
"""
def __init__(self, mode, bounds, fit, nwhales=5, a0=2, b=1, int_transform='nearest_int', ncores=1, seed=None):
set_neorl_seed(seed)
assert ncores <= nwhales, '--error: ncores ({}) must be less than or equal than nwhales ({})'.format(ncores, nwhales)
#--mir
self.mode=mode
if mode == 'min':
self.fit=fit
elif mode == 'max':
def fitness_wrapper(*args, **kwargs):
return -fit(*args, **kwargs)
self.fit=fitness_wrapper
else:
raise ValueError('--error: The mode entered by user is invalid, use either `min` or `max`')
self.int_transform=int_transform
if int_transform not in ["nearest_int", "sigmoid", "minmax"]:
raise ValueError('--error: int_transform entered by user is invalid, must be `nearest_int`, `sigmoid`, or `minmax`')
self.bounds=bounds
self.ncores = ncores
self.nwhales=nwhales
assert a0 > 0, '--error: a0 must be positive'
self.a0=a0
self.b=b
#infer variable types
self.var_type = np.array([bounds[item][0] for item in bounds])
#mir-grid
if "grid" in self.var_type:
self.grid_flag=True
self.orig_bounds=bounds #keep original bounds for decoding
#print('--debug: grid parameter type is found in the space')
self.bounds, self.bounds_map=encode_grid_to_discrete(self.bounds) #encoding grid to int
#define var_types again by converting grid to int
self.var_type = np.array([self.bounds[item][0] for item in self.bounds])
else:
self.grid_flag=False
self.bounds = bounds
self.orig_bounds=bounds
self.dim = len(bounds)
self.lb=np.array([self.bounds[item][1] for item in self.bounds])
self.ub=np.array([self.bounds[item][2] for item in self.bounds])
def init_sample(self, bounds):
indv=[]
for key in bounds:
if bounds[key][0] == 'int':
indv.append(random.randint(bounds[key][1], bounds[key][2]))
elif bounds[key][0] == 'float':
indv.append(random.uniform(bounds[key][1], bounds[key][2]))
#elif bounds[key][0] == 'grid':
# indv.append(random.sample(bounds[key][1],1)[0])
else:
raise Exception ('unknown data type is given, either int, float, or grid are allowed for parameter bounds')
return indv
def eval_whales(self):
#---------------------
# Fitness calcs
#---------------------
core_lst=[]
for case in range (0, self.Positions.shape[0]):
core_lst.append(self.Positions[case, :])
if self.ncores > 1:
with joblib.Parallel(n_jobs=self.ncores) as parallel:
fitness_lst=parallel(joblib.delayed(self.fit_worker)(item) for item in core_lst)
else:
fitness_lst=[]
for item in core_lst:
fitness_lst.append(self.fit_worker(item))
return fitness_lst
def select(self, pos, fit):
#this function selects the best fitness and position in a population
best_fit=np.min(fit)
min_idx=np.argmin(fit)
best_pos=pos[min_idx,:].copy()
return best_pos, best_fit
def ensure_bounds(self, vec): # bounds check
vec_new = []
for i, (key, val) in enumerate(self.bounds.items()):
# less than minimum
if vec[i] < self.bounds[key][1]:
vec_new.append(self.bounds[key][1])
# more than maximum
if vec[i] > self.bounds[key][2]:
vec_new.append(self.bounds[key][2])
# fine
if self.bounds[key][1] <= vec[i] <= self.bounds[key][2]:
vec_new.append(vec[i])
return vec_new
def fit_worker(self, x):
#This worker is for parallel calculations
# Clip the whale with position outside the lower/upper bounds and return same position
x=self.ensure_bounds(x)
if self.grid_flag:
#decode the individual back to the int/float/grid mixed space
x=decode_discrete_to_grid(x,self.orig_bounds,self.bounds_map)
# Calculate objective function for each search agent
fitness = self.fit(x)
return fitness
def ensure_discrete(self, vec):
#"""
#to mutate a vector if discrete variables exist
#handy function to be used three times within BAT phases
#Params:
#vec - bat position in vector/list form
#Return:
#vec - updated bat position vector with discrete values
#"""
for dim in range(self.dim):
if self.var_type[dim] == 'int':
vec[dim] = mutate_discrete(x_ij=vec[dim],
x_min=min(vec),
x_max=max(vec),
lb=self.lb[dim],
ub=self.ub[dim],
alpha=self.alpha,
method=self.int_transform,
)
return vec
def UpdateWhales(self):
# Update the Position of the whales agents
for i in range(0, self.nwhales):
r1 = random.random()
r2 = random.random()
self.A = 2 * self.a * r1 - self.a
C = 2 * r2
l = (self.fac - 1) * random.random() + 1
p = random.random()
for j in range(0, self.dim):
if p < 0.5:
if abs(self.A) >= 1:
r_index = math.floor(self.nwhales * random.random())
X_rand = self.Positions[r_index, :]
self.Positions[i, j] = X_rand[j] - self.A * abs(C * X_rand[j] - self.Positions[i, j])
elif abs(self.A) < 1:
self.Positions[i, j] = self.best_position[j] - self.A * abs(C * self.best_position[j] - self.Positions[i, j])
elif p >= 0.5:
distance2Leader = abs(self.best_position[j] - self.Positions[i, j])
self.Positions[i, j] = (distance2Leader * math.exp(self.b * l)
* math.cos(l * 2 * math.pi) + self.best_position[j])
self.Positions[i,:]=self.ensure_bounds(self.Positions[i,:])
self.Positions[i, :] = self.ensure_discrete(self.Positions[i,:])
[docs] def evolute(self, ngen, x0=None, verbose=False, **kwargs):
"""
This function evolutes the WOA algorithm for number of generations.
:param ngen: (int) number of generations to evolute
:param x0: (list of lists) initial position of the whales (must be of same size as ``nwhales``)
:param verbose: (bool) print statistics to screen
:return: (tuple) (best individual, best fitness, and dictionary containing major search results)
"""
self.history = {'local_fitness':[], 'global_fitness':[], 'a': [], 'A': []}
self.best_fitness=float("inf")
self.verbose=verbose
self.Positions = np.zeros((self.nwhales, self.dim))
if x0:
assert len(x0) == self.nwhales, '--error: the length of x0 ({}) MUST equal the number of whales in the group ({})'.format(len(x0), self.nwhales)
for i in range(self.nwhales):
check_mixed_individual(x=x0[i], bounds=self.orig_bounds) #assert the type provided is consistent
if self.grid_flag:
self.Positions[i,:] = encode_grid_indv_to_discrete(x0[i], bounds=self.orig_bounds, bounds_map=self.bounds_map)
else:
self.Positions[i,:] = x0[i]
else:
# Initialize the positions of whales
for i in range(self.nwhales):
self.Positions[i,:]=self.init_sample(self.bounds)
fitness0=self.eval_whales()
self.best_position, self.best_fitness = self.select(self.Positions, fitness0)
for k in range(0, ngen):
self.alpha= 1 - k * ((1) / ngen) #mir: alpha decreases linearly between 1 to 0, for discrete mutation
if 'a' in kwargs:
assert len(kwargs["fac"]) == ngen, '--error: the length of `a` in kwargs must equal to ngen'
self.a=kwargs["a"][k]
else:
# a is annealed from 2 to 0
self.a = self.a0 - k * ((self.a0) / (ngen))
if 'fac' in kwargs:
#take it from external vector
assert len(kwargs["fac"]) == ngen, '--error: the length of `fac` in kwargs must equal to ngen'
self.fac=kwargs["fac"][k]
else:
# fac is annealed from -1 to -2 to estimate l
self.fac = -1 + k * ((-1) / ngen)
#-----------------------------
# Update Whale Positions
#-----------------------------
self.UpdateWhales()
#----------------------
# Evaluate New Whales
#----------------------
fitness=self.eval_whales()
for i, fits in enumerate(fitness):
#save the best of the best!!!
if fits < self.best_fitness:
self.best_fitness=fits
self.best_position=self.Positions[i, :].copy()
#--mir
if self.mode=='max':
self.fitness_best_correct=-self.best_fitness
self.local_fitness=-np.min(fitness)
else:
self.fitness_best_correct=self.best_fitness
self.local_fitness=np.min(fitness)
self.history['local_fitness'].append(self.local_fitness)
self.history['global_fitness'].append(self.fitness_best_correct)
self.history['a'].append(self.a)
self.history['A'].append(self.A)
# Print statistics
if self.verbose and i % self.nwhales:
print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')
print('WOA step {}/{}, nwhales={}, Ncores={}'.format((k+1)*self.nwhales, ngen*self.nwhales, self.nwhales, self.ncores))
print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')
print('Best Whale Fitness:', np.round(self.fitness_best_correct,6))
if self.grid_flag:
self.whale_decoded = decode_discrete_to_grid(self.best_position, self.orig_bounds, self.bounds_map)
print('Best Whale Position:', self.whale_decoded)
else:
print('Best Whale Position:', self.best_position)
print('a:', np.round(self.a,3))
print('A:', np.round(self.A,3))
print('fac:', np.round(self.fac,3))
print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')
#mir-grid
if self.grid_flag:
self.whale_correct = decode_discrete_to_grid(self.best_position, self.orig_bounds, self.bounds_map)
else:
self.whale_correct = self.best_position
if self.verbose:
print('------------------------ WOA Summary --------------------------')
print('Best fitness (y) found:', self.fitness_best_correct)
print('Best individual (x) found:', self.whale_correct)
print('--------------------------------------------------------------')
if self.mode=='min':
fitness=np.array(fitness)
else:
fitness=-np.array(fitness)
#--mir return the last population for restart calculations
if self.grid_flag:
self.history['last_pop'] = get_population(self.Positions, fits=fitness, grid_flag=True,
bounds=self.orig_bounds, bounds_map=self.bounds_map)
else:
self.history['last_pop'] = get_population(self.Positions, fits=fitness, grid_flag=False)
return self.whale_correct, self.fitness_best_correct, self.history