# 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 -*-
#"""
#@author: Xubo
#@email: guxubo@alumni.sjtu.edu
#"""
import random
import numpy as np
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 JAYA:
"""
JAYA 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 npop: (int) number of individuals in the population
:param int_transform: (str): method of handling int/discrete variables, choose from: ``nearest_int``, ``sigmoid``, ``minmax``.
:param ncores: (int) number of parallel processors
:param seed: (int) random seed for sampling
"""
def __init__(self, mode, bounds, fit, npop=50, int_transform ='nearest_int', ncores=1, seed=None):
self.seed=seed
set_neorl_seed(self.seed)
assert npop > 3, '--eror: size of npop must be more than 3'
self.npop= npop
self.bounds=bounds
self.ncores=ncores
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.mode=mode
if mode == 'max':
self.fit=fit
elif mode == 'min':
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`')
#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=[self.bounds[item][1] for item in self.bounds]
self.ub=[self.bounds[item][2] for item in self.bounds]
def gen_indv(self, bounds): # individual
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])
return indv
def init_population(self, x0=None, verbose=False): # population
pop = []
if x0: # have premary solution
if verbose:
print('The first individual provided by the user:', x0[0])
print('The last individual provided by the user:', x0[-1])
for i in range(self.npop):
check_mixed_individual(x=x0[i], bounds=self.orig_bounds) #assert the type provided is consistent
if self.grid_flag:
pop.append(encode_grid_indv_to_discrete(x0[i], bounds=self.orig_bounds, bounds_map=self.bounds_map))
else:
pop.append(x0[i])
else: # random init
for i in range(self.npop):
indv=self.gen_indv(self.bounds)
pop.append(indv)
# array
pop = np.array(pop)
return pop
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 eval_pop(self, pos_array):
#"""
#Evaluate fitness of the population with parallel processing.
#Return:
#list - pop fitnesses
#"""
if self.ncores > 1:
with joblib.Parallel(n_jobs=self.ncores) as parallel:
fitness_lst = parallel(joblib.delayed(self.fit_worker)(pos_array[i, :]) for i in range(self.npop))
else:
fitness_lst = []
for i in range(self.npop):
fitness_lst.append(self.fit_worker(pos_array[i, :]))
return fitness_lst
def fit_worker(self, x):
# Clip the wolf 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)
fitness = self.fit(x)
return fitness
def ensure_discrete(self, vec):
#"""
#to mutate a vector if discrete variables exist
#Params:
#vec - position in vector/list form
#Return:
#vec - updated 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.b,
method=self.int_transform,
)
return vec
[docs] def evolute(self, ngen, x0=None, verbose=False):
"""
This function evolutes the MFO algorithm for number of generations.
:param ngen: (int) number of generations to evolute
:param x0: (list of lists) the initial individuals of the population
:param verbose: (bool) print statistics to screen
:return: (tuple) (best individual, best fitness, and a list of fitness history)
"""
self.history = {'local_fitness':[], 'global_fitness':[]}
N = self.npop # population size
dim = len(self.bounds) # individual length
set_neorl_seed(self.seed)
fitness_mat = np.zeros(N)
Best_pos = np.zeros(dim)
Best_score = float('-inf') # find a maximum, so the larger the better
Worst_pos = np.zeros(dim)
Worst_score = float('inf')
## INITIALIZE
# population
if x0:
assert len(x0) == N, '--error: the length of x0 ({}) (initial population) must equal to number of individuals npop ({})'.format(len(x0), self.npop)
pos = self.init_population(x0=x0, verbose=verbose)
else:
pos = self.init_population(verbose=verbose)
pos = pos*1.0 #this is to account for mixed intger-cont. problems, data needs to be float
# calulate fitness
fitness_mat=self.eval_pop(pos)
for i in range(N):
if fitness_mat[i] > Best_score:
Best_score = fitness_mat[i]
Best_pos = pos[i, :]
if fitness_mat[i] < Worst_score:
Worst_score = fitness_mat[i]
Worst_pos = pos[i, :]
## main loop
best_scores = []
for gen in range(1, ngen+1):
self.b= 1 - gen * ((1) / ngen) #mir: b decreases linearly between 1 to 0, for discrete mutation
new_pos = np.zeros((N,dim))
# update pos
for i in range(N):
r1=np.random.random(dim)
r2=np.random.random(dim)
# Update pos
new_pos[i,:] = (
pos[i,:]
+ r1*(Best_pos - abs(pos[i,:]))
- r2*(Worst_pos - abs(pos[i,:])) # !! minus
)
# check bounds
new_pos[i,:] = self.ensure_bounds(new_pos[i,:])
new_pos[i,:] = self.ensure_discrete(new_pos[i,:])
fitness_new=self.eval_pop(new_pos)
for i in range(N):
if fitness_new[i] > fitness_mat[i]:
pos[i,:] = new_pos[i,:]
fitness_mat[i] = fitness_new[i]
# update best_score and worst_score
for i in range(N):
if fitness_mat[i] > Best_score:
Best_score = fitness_mat[i]
Best_pos = pos[i, :]
if fitness_mat[i] < Worst_score:
Worst_score = fitness_mat[i]
Worst_pos = pos[i, :]
#-----------------------------
#Fitness saving
#-----------------------------
self.last_pop=new_pos.copy()
self.last_fit=np.array(fitness_new).copy()
gen_avg = sum(fitness_mat) / N # current generation avg. fitness
y_best = Best_score # fitness of best individual
x_best = Best_pos.copy()
best_scores.append(y_best)
#--mir show the value wrt min/max
if self.mode=='min':
y_best_correct=-y_best
gen_avg=-gen_avg
self.history['local_fitness'].append(-np.max(fitness_new))
else:
y_best_correct=y_best
self.history['local_fitness'].append(np.max(fitness_new))
if verbose:
print('************************************************************')
print('JAYA step {}/{}, Ncores={}'.format(gen*self.npop, ngen*self.npop, self.ncores))
print('************************************************************')
print('Best fitness:', np.round(y_best_correct,6))
if self.grid_flag:
x_decoded = decode_discrete_to_grid(x_best, self.orig_bounds, self.bounds_map)
print('Best individual:', x_decoded)
else:
print('Best individual:', x_best)
print('Average fitness:', np.round(gen_avg,6))
print('************************************************************')
#mir-grid
if self.grid_flag:
x_best_correct = decode_discrete_to_grid(x_best, self.orig_bounds, self.bounds_map)
else:
x_best_correct = x_best
if verbose:
print('------------------------ JAYA Summary --------------------------')
print('Best fitness (y) found:', y_best_correct)
print('Best individual (x) found:', x_best_correct)
print('--------------------------------------------------------------')
if self.mode=='min':
best_scores=[-item for item in best_scores]
self.last_fit=-self.last_fit
self.history['global_fitness'] = best_scores
#--mir return the last population for restart calculations
if self.grid_flag:
self.history['last_pop'] = get_population(self.last_pop, fits=self.last_fit, grid_flag=True,
bounds=self.orig_bounds, bounds_map=self.bounds_map)
else:
self.history['last_pop'] = get_population(self.last_pop, fits=self.last_fit, grid_flag=False)
return x_best_correct, y_best_correct, self.history