Extended Ant Colony Optimization (gaco)
Extended Ant Colony Optimization (gaco)#

class gaco#
Extended Ant Colony Optimization.
Ant colony optimization is a class of optimization algorithms modeled on the actions of an ant colony. Artificial ‘ants’ (e.g. simulation agents) locate optimal solutions by moving through a parameter space representing all possible solutions. Real ants lay down pheromones directing each other to resources while exploring their environment. The simulated ‘ants’ similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate better solutions.
In pagmo we propose a version of this algorithm called extended ACO and originally described by Schlueter et al. Extended ACO generates future generations of ants by using the a multikernel gaussian distribution based on three parameters (i.e., pheromone values) which are computed depending on the quality of each previous solution. The solutions are ranked through an oracle penalty method.
This algorithm can be applied to boxbounded singleobjective, constrained and unconstrained optimization, with both continuous and integer variables.
See also
M. Schlueter, et al. (2009). Extended ant colony optimization for nonconvex mixed integer nonlinear programming. Computers & Operations Research.
Note
The ACO version implemented in PaGMO is an extension of Schlueter’s originally proposed extended ACO algorithm. The main difference between the implemented version and the original one lies in how two of the three pheromone values are computed (in particular, the weights and the standard deviations).
Image credit: https://commons.wikimedia.org/wiki/File:Knapsack_ants.svg
Public Types

typedef std::tuple<unsigned, vector_double::size_type, double, unsigned, double, double, double> log_line_type#
Single entry of the log (gen, m_fevals, best_fit, m_ker, m_oracle, dx, dp)

typedef std::vector<log_line_type> log_type#
The log.
Public Functions

gaco(unsigned gen = 1u, unsigned ker = 63u, double q = 1.0, double oracle = 0., double acc = 0.01, unsigned threshold = 1u, unsigned n_gen_mark = 7u, unsigned impstop = 100000u, unsigned evalstop = 100000u, double focus = 0., bool memory = false, unsigned seed = pagmo::random_device::next())#
Constructs the ACO user defined algorithm for singleobjective optimization.
 Parameters
gen – [in] Generations: number of generations to evolve.
ker – [in] Kernel: number of solutions stored in the solution archive.
q – [in] Convergence speed parameter: this parameter is useful for managing the convergence speed towards the found minima (the smaller the faster).
oracle – [in] Oracle parameter: this is the oracle parameter used in the penalty method.
acc – [in] Accuracy parameter: for maintaining a minimum penalty function’s values distances.
threshold – [in] Threshold parameter: when the generations reach the threshold then q is set to 0.01 automatically.
n_gen_mark – [in] Standard deviations convergence speed parameter: this parameters determines the convergence speed of the standard deviations values.
impstop – [in] Improvement stopping criterion: if a positive integer is assigned here, the algorithm will count the runs without improvements, if this number will exceed impstop value, the algorithm will be stopped.
evalstop – [in] Evaluation stopping criterion: same as previous one, but with function evaluations.
focus – [in] Focus parameter: this parameter makes the search for the optimum greedier and more focused on local improvements (the higher the greedier). If the value is very high, the search is more focused around the current best solutions.
memory – [in] Memory parameter: if true, memory is activated in the algorithm for multiple calls
seed – seed used by the internal random number generator (default is random).
 Throws
std::invalid_argument – if
acc
is not \( >=0 \),impstop
is not a positive integer,evalstop
is not a positive integer,focus
is not \( >=0 \),ker
is not \( >=2 \),oracle
is not positive,threshold
is not \( \in [1,gen] \) when \(memory=false\) and \(gen!=0\),threshold
is not \( >=1 \) when \(memory=true\) and \(gen!=0\),q
is not \( >=0 \)

population evolve(population) const#
Algorithm evolve method.
Evolves the population for the requested number of generations.
 Parameters
pop – population to be evolved
 Throws
std::invalid_argument – if pop.get_problem() is multiobjective or stochastic
std::invalid_argument – if the population size is not at least 2
std::invalid_argument – if kernel parameter is bigger than the population size
 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 1):
Gen, is the generation number, Fevals the number of function evaluation used, Best is the best fitness function value found until that generation, Kernel is the kernel size, Oracle is the oracle parameter value, dx is the flatness in the individuals, dp is the flatness in the penalty function values.*Gen: Fevals: Best: Kernel: Oracle: dx: dp: 1 0 179.464 13 100 4.33793 47876 2 15 14.205 13 100 5.20084 5928.12 3 30 14.205 13 14.205 1.24173 1037.44 4 45 14.205 13 14.205 3.05807 395.89 5 60 7.91087 13 14.205 0.711446 286.599 6 75 2.81437 13 7.91087 5.80451 71.8174 7 90 2.81437 13 2.81437 1.90561 48.3829 8 105 2.81437 13 2.81437 1.3072 26.9496 9 120 1.4161 13 2.81437 1.61732 10.6527 10 150 1.4161 13 1.4161 2.54262 3.67034
 Parameters
level – verbosity level

inline unsigned get_verbosity() const#
Gets the verbosity level.
 Returns
the verbosity level

inline unsigned get_gen() const#
Gets the generations.
 Returns
the number of generations to evolve for

void set_bfe(const bfe &b)#
Sets the batch function evaluation scheme.
 Parameters
b – batch function evaluation object

inline std::string get_name() const#
Algorithm name.
Returns the name of the algorithm.
 Returns
std::string
containing the algorithm name

std::string get_extra_info() const#
Extra info.
Returns extra information on the algorithm.
 Returns
an
std::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 gaco::log_line_type containing: gen, m_fevals, best_fit, m_ker, m_oracle, dx, dp as described in gaco::set_verbosity Returns
an
std::vector
of gaco::log_line_type containing the logged values gen, m_fevals, best_fit, m_ker, m_oracle, dx, dp

typedef std::tuple<unsigned, vector_double::size_type, double, unsigned, double, double, double> log_line_type#