Package: beecolpy
BeeColPyBeeColPy 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_algorithmInstallationDependenciesBeeColPy requires:Python (>= 3.0)NumPy (>= 1.1.0)BeeColPy do not support Python 2.7.User installationpip install beecolpyUsage InstructionsFor 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 : NameA 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 TuplesA list of tuples containing the lower and upper boundaries ofeach dimension of function domain.Obs.: The number of boundaries determines the dimension offunction.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. Halfof this amount determines the number of points analyzed (foodsources).According articles, half of this number determines the amountof Employed bees and other half is Onlooker bees.[scouts] : Float --optional-- (default: 0.5)Determines the limit of tries for scout bee discard a foodsource and replace for a new one.- If scouts = 0 :Scout_limit = colony_size * dimension- If scouts = (0 to 1) :Scout_limit = colony_size * dimension * scoutsObs.: scouts = 0.5 is used in [3] as benchmark.- If scouts >= 1 :Scout_limit = scoutsObs.: 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 costduring initialization or during scout events. This optionusually helps the algorithm stability because, in rare cases,NaN values can lock the algorithm in a infinite loop.Obs.: NaN protection can drastically increases calculationtime if analysed function has too many values of domainreturning NaN.[log_agents] : Boolean --optional-- (default: False)If true, beecolpy will register, before each iteration, theposition of each food source. Useful to debug but, if there ahigh amount of food sources and/or iterations, this optiondrastically 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/maximumcoordinate.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 activatedget_agents()Returns a list with the position of each food source duringeach iteration if "log_agents = True".Parameters----------[reset_agents] : bool --optional-- (default: False)If true, the food source position log will be cleaned innext 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 : NameA 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 knowexactly how binary solvers work, just inform the number of bits(bits_count) and the default boundaries will be used. Theseboundaries usually are enough to solve most problems.bits_count : IntThe number of bits that compose the output vector.boundaries : List of TuplesA list of tuples containing the lower and upper boundariesthat will be applied over sigmoid or angle modulation functionto 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. Halfof this amount determines the number of points analyzed(food sources).According articles, half of this number determines the amountof Employed bees and other half is Onlooker bees.[scouts] : Float --optional-- (default: 0.5)Determines the limit of tries for scout bee discard a foodsource and replace for a new one.- If scouts = 0 :Scout_limit = colony_size * dimension- If scouts = (0 to 1) :Scout_limit = colony_size * dimension * scoutsObs.: scouts = 0.5 is used in [3] as benchmark.- If scouts >= 1 :Scout_limit = scoutsObs.1: Scout_limit is rounded down in all cases.Obs.2: In Binary form, the scouts tends to be more relevantthan 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 oftimes the function are recalculated when it returns a NaN.(default (int): 3)If true or greater than 0, re-generate food sources that getNaN value as cost during initialization or during scoutevents. This option usually helps the algorithm stabilitybecause, in rare cases, NaN values can lock the algorithm ina infinite loop.Obs.: NaN protection can drastically increases calculationtime if analysed function has too many values of domainreturning NaN.[transfer_function] : String --optional-- (default: 'sigmoid')Only used with "method='bin'". Defines the transfer functionused 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, theresult vector are represented by a probability vector withthe probability of each bit becomes "True". This propertydetermines how output bit vector will be estimated.- If result_format = 'average' :Returns the most frequent bit vector after"best_model_iterations" simulations of the probabilityvector. This approach is ideal to solve problems withhighly random elements.Obs.: To use this method efficiently, use highvalues in "best_model_iterations". Usually valuesgreater 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 isuseful to solve highly noisy problems.[best_model_iterations] : int --optional--(default: iterations count)Only used with "method='bin'". Due stochastic aspect ofBinary form of particle based metaheuristic, after executionof ABC, the cost function will be calculated"best_model_iterations" times and the "best" or the "mostfrequent" 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, theposition of each food source. Useful to debug but, if there ahigh amount of food sources and/or iterations, this optiondrastically 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/maximumcoordinate.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 withprobability of each bit becomes "True". Useful to useprobability as component of stopping criteria or toevaluate solution stability.- If probability_vector = True :"get_solution" returns a vector with theprobability 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 activatedget_agents()Returns a list with the position of each food source duringeach iteration if "log_agents = True".Obs.: In binary form, this method returns the position ofeach food source after transformation "binary -> continuous".I.e. returns the values applied on angle modulation functionin 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 innext fit()."""Example"""To find the minimum  of sphere function on interval (-10 to 10) with2 dimensions in domain using default parameters:"""frombeecolpyimportabcdefsphere(x):total=0foriinrange(len(x)):total+=x[i]**2returntotalabc_obj=abc(sphere,[(-10,10),(-10,10)])#Load dataabc_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()AuthorSamuel Carlos Pessoa Oliveira-samuelcpoliveira@gmail.comLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile 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] PamparÃ¡, 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