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CS.py
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CS.py
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# -*- coding: utf-8 -*-
"""
Created on Tue May 24 13:13:28 2016
within the EvoloPy optimization library
@author: Hossam Faris
-> Modified by Anezka Kazikova to fit the uniform template in 2018
"""
import math
import numpy
import random
import time
import testing
import benchmark
func_num = 0
def get_cuckoos(nest, best, lb, ub, n, dim):
# perform Levy flights
tempnest = numpy.zeros((n, dim))
tempnest = numpy.array(nest)
beta = 3 / 2;
sigma = (math.gamma(1 + beta) * math.sin(math.pi * beta / 2) / (
math.gamma((1 + beta) / 2) * beta * 2 ** ((beta - 1) / 2))) ** (1 / beta);
s = numpy.zeros(dim)
for j in range(0, n):
s = nest[j, :]
u = numpy.random.randn(len(s)) * sigma
v = numpy.random.randn(len(s))
step = u / abs(v) ** (1 / beta)
alpha = 0.01 # I believe, this number should be the Alpha from the paper "Cuckoo Search via Levy Flight, Yang & Deb 2010"
stepsize = alpha * (step * (s - best))
s = s + stepsize * numpy.random.randn(len(s))
tempnest[j, :] = numpy.clip(s, lb, ub)
return tempnest
def get_best_nest(nest, newnest, fitness, n, dim, objf, evaluations):
# Evaluating all new solutions
tempnest = numpy.zeros((n, dim))
tempnest = numpy.copy(nest)
for j in range(0, n):
# for j=1:size(nest,1),
fnew = objf(newnest[j, :], dim, func_num)
evaluations += 1
if fnew <= fitness[j]:
fitness[j] = fnew
tempnest[j, :] = newnest[j, :]
# Find the current best
fmin = min(fitness)
K = numpy.argmin(fitness)
bestlocal = tempnest[K, :]
return fmin, bestlocal, tempnest, fitness, evaluations
# Replace some nests by constructing new solutions/nests
def empty_nests(nest, pa, n, dim):
# Discovered or not
tempnest = numpy.zeros((n, dim))
K = numpy.random.uniform(0, 1, (n, dim)) > pa
stepsize = random.random() * (nest[numpy.random.permutation(n), :] - nest[numpy.random.permutation(n), :])
tempnest = nest + stepsize * K
return tempnest
##########################################################################
def CS(number_of_runs, problem_definition, test_flags):
global func_num;
# lb=-1
# ub=1
n=50
# N_IterTotal=1000
# dim=30
test_convergence = test_flags['convergence']
test_statistics = test_flags['statistics']
test_error_values = test_flags['error_values']
dimension = problem_definition['dimension']
low_bound = problem_definition['low_bound']
up_bound = problem_definition['up_bound']
objf = problem_definition['function']
func_num = problem_definition['func_num']
filename = problem_definition['filename']
if test_flags['complexity_computation']:
max_evaluation = 200000
else:
max_evaluation = benchmark.get_max_fes(dimension, objf)
max_iteration = round(max_evaluation/n/2)
average_convergence_curve = numpy.zeros((number_of_runs, max_iteration))
all_errors = numpy.zeros((number_of_runs, len(benchmark.when_to_record_results(dimension, objf))))
evaluations_curve = numpy.zeros(max_iteration)
statistics = numpy.zeros(number_of_runs)
best_cuckoo = [0] * dimension
best_cuckoo_score = float("inf")
for runs in range(number_of_runs):
save_errors_at = benchmark.when_to_record_results(dimension, objf)
evaluations = 0
# Discovery rate of alien eggs/solutions
pa = 0.25
nd = dimension
# Lb=[lb]*nd
# Ub=[ub]*nd
convergence = []
# RInitialize nests randomly
nest = numpy.random.rand(n, dimension) * (up_bound - low_bound) + low_bound
new_nest = numpy.zeros((n, dimension))
new_nest = numpy.copy(nest)
bestnest = [0] * dimension
fitness = numpy.zeros(n)
fitness.fill(float("inf"))
fmin, bestnest, nest, fitness, evaluations = get_best_nest(nest, new_nest, fitness, n, dimension, objf, evaluations)
convergence = []
convergence_errors = []
# Main loop counter
for iter in range(0, max_iteration):
# Generate new solutions (but keep the current best)
new_nest = get_cuckoos(nest, bestnest, low_bound, up_bound, n, dimension)
# Evaluate new solutions and find best
fnew, best, nest, fitness, evaluations = get_best_nest(nest, new_nest, fitness, n, dimension, objf, evaluations)
new_nest = numpy.clip(empty_nests(new_nest, pa, n, dimension), low_bound, up_bound)
# Evaluate new solutions and find best
fnew, best, nest, fitness, evaluations = get_best_nest(nest, new_nest, fitness, n, dimension, objf, evaluations)
if fnew < fmin:
fmin = fnew
bestnest = best
if test_convergence:
convergence.append(fmin)
evaluations_curve[iter] = evaluations
if test_error_values and evaluations >= save_errors_at[0]:
convergence_errors.append(fmin - benchmark.known_optimum_value(func_num))
save_errors_at.pop(0)
if test_convergence:
average_convergence_curve[runs] = convergence
if test_statistics:
statistics[runs] = numpy.min(fitness)
if test_error_values:
all_errors[runs] = numpy.array(convergence_errors)
print(['CS ' + str(runs) + ': [' + str(fmin) + '] Evaluations: ' + str(
evaluations) + ' Iterations: ' + str(max_iteration)])
if best_cuckoo_score > fmin:
best_cuckoo_score = fmin
best_cuckoo = bestnest
if test_convergence:
testing.evaluate_average_convergence(average_convergence_curve, evaluations_curve, "CS", "-.")
if test_statistics:
statistics = testing.evaluate_all_statistics(statistics)
if test_error_values:
filename = filename + '/cs_' + str(func_num) + '_' + str(dimension) + '.csv'
testing.save_errors_to_file(all_errors, filename)
return statistics, best_cuckoo