Artificial Bee Colony solver
Project description
BeeColPy
BeeColPy is a module for function optimization through artificial bee colony algorithm, a method developed by Karaboga [1], a variant of classical particle swarm optimization.
Websites:
Source code: https://github.com/renard162/BeeColPy/
Introduction: https://en.wikipedia.org/wiki/Artificial_bee_colony_algorithm
Installation
Dependencies
BeeColPy requires:
- Python (>= 3.0)
- NumPy (>= 1.1.0)
BeeColPy do not support Python 2.7.
User installation
pip install beecolpy
Usage Instructions
For cost functions with continuous domain:
#Step-by-step:
#Create object and set the solver parameters:
abc_obj = abc(function,
boundaries,
colony_size=40,
scouts=0.5,
iterations=50,
min_max='min',
nan_protection=True,
log_agents=True)
#Execute algorithm:
abc_obj.fit()
#Get solution obtained after fit() execution:
solution = abc_obj.get_solution()
"""
Obs.: Each time fit() was executed, the algorithm iterate
'iterations' times resuming from last fit() execution.
"""
"""
Parameters
----------
function : Name
A name of a function to minimize/maximize.
Example: if the function is:
def my_func(x): return x[0]**2 + x[1]**2 + 5*x[1]
Use "my_func" as parameter.
boundaries : List of Tuples
A list of tuples containing the lower and upper boundaries of
each dimension of function domain.
Obs.: The number of boundaries determines the dimension of
function.
Example: A function F(x1, x2) = y with:
(-5 <= x1 <= 5) and (-20 <= x2 <= 20) have the boundaries:
[(-5,5), (-20,20)]
[colony_size] : Int --optional-- (default: 40)
A value that determines the number of bees in algorithm. Half
of this amount determines the number of points analyzed (food
sources).
According articles, half of this number determines the amount
of Employed bees and other half is Onlooker bees.
[scouts] : Float --optional-- (default: 0.5)
Determines the limit of tries for scout bee discard a food
source and replace for a new one.
- If scouts = 0 :
Scout_limit = colony_size * dimension
- If scouts = (0 to 1) :
Scout_limit = colony_size * dimension * scouts
Obs.: scouts = 0.5 is used in [3] as benchmark.
- If scouts >= 1 :
Scout_limit = scouts
Obs.: Scout_limit is rounded down in all cases.
[iterations] : Int --optional-- (default: 50)
The number of iterations executed by algorithm.
[min_max] : String --optional-- (default: 'min')
Determines if algorithm will minimize or maximize the function.
- If min_max = 'min' : (default)
Locate the minimum of function.
- If min_max = 'max' :
Locate the maximum of function.
[nan_protection] : Boolean --optional-- (default: True)
If true, re-generate food sources that get NaN value as cost
during initialization or during scout events. This option
usually helps the algorithm stability because, in rare cases,
NaN values can lock the algorithm in a infinite loop.
Obs.: NaN protection can drastically increases calculation
time if analysed function has too many values of domain
returning NaN.
[log_agents] : Boolean --optional-- (default: False)
If true, beecolpy will register, before each iteration, the
position of each food source. Useful to debug but, if there a
high amount of food sources and/or iterations, this option
drastically increases memory usage.
[seed] : Int --optional-- (default: None)
If defined as an int, set the seed used in all random process.
Methods
----------
fit()
Execute the algorithm with defined parameters.
Obs.: Returns a list with values found as minimum/maximum
coordinate.
get_solution()
Returns the value obtained after fit() the method.
get_status()
Returns a tuple with:
- Number of complete iterations executed
- Number of scout events during iterations
- Number of times that NaN protection was activated
get_agents()
Returns a list with the position of each food source during
each iteration if "log_agents = True".
Parameters
----------
[reset_agents] : bool --optional-- (default: False)
If true, the food source position log will be cleaned in
next fit().
"""
For cost function with binary domain:
#Step-by-step:
#Create object and set the solver parameters:
bin_abc_obj = bin_abc(function,
bits_count,
method='am',
colony_size=40,
scouts=0.5,
iterations=50,
best_model_iterations=0,
min_max='min',
nan_protection=True,
transfer_function='sigmoid',
best_model_iterations=0,
log_agents=True)
#Execute algorithm:
bin_abc_obj.fit()
#Get solution after execute fit() without execute it again:
solution = bin_abc_obj.get_solution()
"""
Obs.: Each time fit() was executed, the algorithm iterate
'iterations' times resuming from last fit() execution.
"""
"""
Parameters
----------
function : Name
A name of a function to minimize/maximize.
Example: if the function is:
def my_func(x): return x[0] or (x[1] and x[2])
Use "my_func" as parameter.
-=x=--=x=--=x=--=x=--=x=-
Just one of these parameters are mandatory. If you don't know
exactly how binary solvers work, just inform the number of bits
(bits_count) and the default boundaries will be used. These
boundaries usually are enough to solve most problems.
bits_count : Int
The number of bits that compose the output vector.
boundaries : List of Tuples
A list of tuples containing the lower and upper boundaries
that will be applied over sigmoid or angle modulation function
to determine the probability to bit become 1.
Example: A function F(b1, b2) = y with:
(-5 <= b1 <= 5) and (-20 <= b2 <= 20) have the boundaries:
[(-5,5), (-20,20)]
Obs.:
- If boundaries are set:
boundaries take the priority over the bits_count.
- If boundaries are not set:
boundaries became (-2,2) to each bit in AMABC method or
(-10,10) to each bit in BABC method.
-=x=--=x=--=x=--=x=--=x=-
[method] : String --optional-- (default: 'am')
Select the applied solver:
- If method = 'am' : (default)
Applied Angle Modulated ABC (AMABC).
- If method = 'bin' :
Applied Binary ABC (BABC).
[colony_size] : Int --optional-- (default: 40)
A value that determines the number of bees in algorithm. Half
of this amount determines the number of points analyzed
(food sources).
According articles, half of this number determines the amount
of Employed bees and other half is Onlooker bees.
[scouts] : Float --optional-- (default: 0.5)
Determines the limit of tries for scout bee discard a food
source and replace for a new one.
- If scouts = 0 :
Scout_limit = colony_size * dimension
- If scouts = (0 to 1) :
Scout_limit = colony_size * dimension * scouts
Obs.: scouts = 0.5 is used in [3] as benchmark.
- If scouts >= 1 :
Scout_limit = scouts
Obs.1: Scout_limit is rounded down in all cases.
Obs.2: In Binary form, the scouts tends to be more relevant
than in continuous form. If your problem are badly solved,
try to reduce the scouts value.
[iterations] : Int --optional-- (default: 50)
The number of iterations executed by algorithm.
[min_max] : String --optional-- (default: 'min')
Determines if algorithm will minimize or maximize the function.
- If min_max = 'min' : (default)
Locate the minimum of function.
- If min_max = 'max' :
Locate the maximum of function.
[nan_protection] : Boolean or Int --optional--
(default (boolean): True)
With "method='am'", this variable are used as a boolean.
With "method='bin'", this variable determines the number of
times the function are recalculated when it returns a NaN.
(default (int): 3)
If true or greater than 0, re-generate food sources that get
NaN value as cost during initialization or during scout
events. This option usually helps the algorithm stability
because, in rare cases, NaN values can lock the algorithm in
a infinite loop.
Obs.: NaN protection can drastically increases calculation
time if analysed function has too many values of domain
returning NaN.
[transfer_function] : String --optional-- (default: 'sigmoid')
Only used with "method='bin'". Defines the transfer function
used to calculate the probability for each bit becomes '1'.
The possibilities are explained on article [6]:
- If transfer_function = 'sigmoid' : (default)
S(x) = 1/(1 + exp(-x))
- If transfer_function = 'sigmoid-2x' :
S(x) = 1/(1 + exp(-2*x))
- If transfer_function = 'sigmoid-x/2' :
S(x) = 1/(1 + exp(-x/2))
- If transfer_function = 'sigmoid-x/3' :
S(x) = 1/(1 + exp(-x/3))
[result_format] : String --optional-- (default: 'best')
Only used with "method='bin'". In a stochastic method, the
result vector are represented by a probability vector with
the probability of each bit becomes "True". This property
determines how output bit vector will be estimated.
- If result_format = 'average' :
Returns the most frequent bit vector after
"best_model_iterations" simulations of the probability
vector. This approach is ideal to solve problems with
highly random elements.
Obs.: To use this method efficiently, use high
values in "best_model_iterations". Usually values
greater than 100 have better results.
- If result_format = 'best' : (default)
Returns the best result after "best_model_iterations"
simulations of the probability vector. This approach is
useful to solve highly noisy problems.
[best_model_iterations] : int --optional--
(default: iterations count)
Only used with "method='bin'". Due stochastic aspect of
Binary form of particle based metaheuristic, after execution
of ABC, the cost function will be calculated
"best_model_iterations" times and the "best" or the "most
frequent" result will be returned.
- If best_model_iterations = 0 : (default)
Tries "iterations" times.
- If best_model_iterations = N :
Tries "N" times.
Obs.: If "best_model_iterations" (or "iterations") is even,
then "best_model_iterations" is increased by one.
[log_agents] : Boolean --optional-- (default: False)
If true, beecolpy will register, before each iteration, the
position of each food source. Useful to debug but, if there a
high amount of food sources and/or iterations, this option
drastically increases memory usage.
[seed] : Int --optional-- (default: None)
If defined as an int, set the seed used in all random process.
Methods
----------
fit()
Execute the algorithm with defined parameters.
Obs.: Returns a list with values found as minimum/maximum
coordinate.
get_solution()
Returns the value obtained after fit() the method.
Parameters
----------
[probability_vector] : bool --optional-- (default: False)
Only used with "method='bin'". Returns the vector with
probability of each bit becomes "True". Useful to use
probability as component of stopping criteria or to
evaluate solution stability.
- If probability_vector = True :
"get_solution" returns a vector with the
probability of each bit becomes "True".
- If probability_vector = False: (default)
"get_solution" returns the solution bit vector.
get_status()
Returns a tuple with:
- Number of complete iterations executed
- Number of scout events during iterations
- Number of times that NaN protection was activated
get_agents()
Returns a list with the position of each food source during
each iteration if "log_agents = True".
Obs.: In binary form, this method returns the position of
each food source after transformation "binary -> continuous".
I.e. returns the values applied on angle modulation function
in AMABC or the values applied on transfer function in BABC.
Parameters
----------
[reset_agents] : bool --optional-- (default: False)
If true, the food source position log will be cleaned in
next fit().
"""
Example
"""
To find the minimum of sphere function on interval (-10 to 10) with
2 dimensions in domain using default parameters:
"""
from beecolpy import abc
def sphere(x):
total = 0
for i in range(len(x)):
total += x[i]**2
return total
abc_obj = abc(sphere, [(-10,10), (-10,10)]) #Load data
abc_obj.fit() #Execute the algorithm
#If you want to get the obtained solution after execute the fit() method:
solution = abc_obj.get_solution()
#If you want to get the number of iterations executed, number of times that
#scout event occur and number of times that NaN protection actuated:
iterations = abc_obj.get_status()[0]
scout = abc_obj.get_status()[1]
nan_events = abc_obj.get_status()[2]
#If you want to get a list with position of all points (food sources) used in each iteration:
food_sources = abc_obj.get_agents()
Author
Samuel Carlos Pessoa Oliveira - samuelcpoliveira@gmail.com
License
This project is licensed under the MIT License - see the LICENSE.md file for details.
Bibliography
[1] Karaboga, D. and Basturk, B., 2007 A powerful and efficient algorithm for numerical function optimization: artificial bee colony ABC) algorithm. Journal of global optimization, 39(3), pp.459-471. Doi: https://doi.org/10.1007/s10898-007-9149-x
[2] Liu, T., Zhang, L. and Zhang, J., 2013 Study of binary artificial bee colony algorithm based on particle swarm optimization. Journal of Computational Information Systems, 9(16), pp.6459-6466. Link: https://api.semanticscholar.org/CorpusID:8789571
[3] Anuar, S., Selamat, A. and Sallehuddin, R., 2016 A modified scout bee for artificial bee colony algorithm and its performance on optimization problems. Journal of King Saud University-Computer and Information Sciences, 28(4), pp. 95-406. Doi: https://doi.org/10.1016/j.jksuci.2016.03.001
[4] Kennedy, J. and Eberhart, R.C., 1997, October. A discrete binary version of the particle swarm algorithm. In 1997 IEEE International conference on systems, man, and cybernetics. Computational cybernetics and simulation (Vol. 5, pp. 4104-4108). IEEE. Doi: https://doi.org/10.1109/ICSMC.1997.637339
[5] Pampará, G. and Engelbrecht, A.P., 2011, April. Binary artificial bee colony optimization. In 2011 IEEE Symposium on Swarm Intelligence (pp. 1-8). IEEE. Doi: https://doi.org/10.1109/SIS.2011.5952562
[6] Mirjalili, S., Hashim, S., Taherzadeh, G., Mirjalili, S.Z. and Salehi, S., 2011. A study of different transfer functions for binary version of particle swarm optimization. In International Conference on Genetic and Evolutionary Methods (Vol. 1, No. 1, pp. 2-7). Link: http://hdl.handle.net/10072/48831
[7] Huang, S.C., 2015. Polygonal approximation using an artificial bee colony algorithm. Mathematical Problems in Engineering, 2015. Doi: https://doi.org/10.1155/2015/375926
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