Artificial Bee Colony#

class bee_colony#

Artificial Bee Colony Algorithm.

../../../_images/BeeColony.gif

Artificial Bee Colony is an optimization algorithm based on the intelligent foraging behaviour of honey bee swarm, proposed by Karaboga in 2005.

The implementation provided for PaGMO is based on the pseudo-code provided in Mernik et al. (2015) - Algorithm 2. pagmo::bee_colony is suitable for box-constrained single-objective continuous optimization.

See: https://abc.erciyes.edu.tr/ for the official ABC web site

See: https://link.springer.com/article/10.1007/s10898-007-9149-x for the paper that introduces Artificial Bee Colony

See: https://www.sciencedirect.com/science/article/pii/S0020025514008378 for the pseudo-code

Public Types

typedef std::tuple<unsigned, unsigned long long, double, double> log_line_type#

Single entry of the log (gen, fevals, best, cur_best)

typedef std::vector<log_line_type> log_type#

The log.

Public Functions

bee_colony(unsigned gen = 1u, unsigned limit = 20u, unsigned seed = pagmo::random_device::next())#

Constructor.

Constructs a bee_colony algorithm

Parameters

  • gen – number of generations. Note that the total number of fitness evaluations will be 2*gen

  • limit – maximum number of trials for abandoning a source

  • seed – seed used by the internal random number generator (default is random)

Throws

std::invalid_argument – if limit equals 0

population evolve(population) const#

Algorithm evolve method.

Evolves the population for a maximum number of generations

Parameters

pop – population to be evolved

Throws

  • std::invalid_argument – if the problem is multi-objective or constrained or stochastic

  • std::invalid_argument – if the population size is smaller than 2

Returns

evolved population

void set_seed(unsigned)#

Sets the seed.

Parameters

seed – the seed controlling the algorithm stochastic behaviour

inline unsigned get_seed() const#

Gets the seed.

Returns

the seed controlling the algorithm stochastic behaviour

inline void set_verbosity(unsigned level)#

Sets the algorithm verbosity.

Sets the verbosity level of the screen output and of the log returned by get_log(). level can be:

  • 0: no verbosity

  • >0: will print and log one line each level generations.

Example (verbosity 100): 

Gen:        Fevals:          Best: Current Best:
   1             40         261363         261363
 101           4040        112.237        267.969
 201           8040        20.8885        265.122
 301          12040        20.6076        20.6076
 401          16040         18.252        140.079
Gen is the generation number, Fevals the number of function evaluation used, , Best is the best fitness found, Current best is the best fitness currently in the population.

Parameters

level – verbosity level

inline unsigned get_verbosity() const#

Gets the verbosity level.

Returns

the verbosity level

inline unsigned get_gen() const#

Gets the number of generations.

Returns

the number of generations to evolve for

inline std::string get_name() const#

Algorithm name.

Returns

a string containing the algorithm name

std::string get_extra_info() const#

Extra info.

Returns

a string containing extra info on the algorithm

inline const log_type &get_log() const#

Get log.

A log containing relevant quantities monitoring the last call to evolve. Each element of the returned std::vector is a bee_colony::log_line_type containing: Gen, Fevals, Current best, Best as described in bee_colony::set_verbosity().

Returns

an std::vector of bee_colony::log_line_type containing the logged values Gen, Fevals, Current best, Best