porcupy.porcupines

Classes

PorcupinePopulation	Class representing a population of porcupines for the Crested Porcupine Optimizer.
DefenseMechanisms	Class implementing the four defense mechanisms of the Crested Porcupine Optimizer.
PopulationManager	Class for managing the population dynamics in the Crested Porcupine Optimizer.

Module Contents

class porcupy.porcupines.PorcupinePopulation(pop_size: int, dimensions: int, bounds: Tuple[numpy.ndarray, numpy.ndarray], init_pos: numpy.ndarray | None = None)

Class representing a population of porcupines for the Crested Porcupine Optimizer.

This class handles the population structure, initialization, and provides methods for population management and dynamics.

Parameters:

• pop_size (int) – Number of porcupines in the population.

• dimensions (int) – Number of dimensions in the search space.

• bounds (tuple of numpy.ndarray) – A tuple of size 2 where the first entry is the minimum bound while the second entry is the maximum bound. Each array must be of shape (dimensions,).

• init_pos (numpy.ndarray, optional) – Initial positions for the porcupines. If None, random positions are generated.

pop_size

Current number of porcupines in the population.

Type:

int

dimensions

Number of dimensions in the search space.

Type:

int

bounds

Bounds for the search space.

Type:

tuple of numpy.ndarray

positions

Current positions of all porcupines, shape (pop_size, dimensions).

Type:

numpy.ndarray

fitness

Current fitness values of all porcupines, shape (pop_size,).

Type:

numpy.ndarray

personal_best_pos

Personal best positions found by each porcupine, shape (pop_size, dimensions).

Type:

numpy.ndarray

personal_best_fitness

Personal best fitness values for each porcupine, shape (pop_size,).

Type:

numpy.ndarray

best_pos

Global best position found so far, shape (dimensions,).

Type:

numpy.ndarray

best_fitness

Global best fitness value found so far.

Type:

float

pop_size

dimensions

bounds

fitness

personal_best_pos

personal_best_fitness

best_pos = None

best_fitness

evaluate(objective_func: Callable, **kwargs) → None

Evaluate the fitness of all porcupines

Parameters:

• objective_func (callable) – The objective function to minimize

• **kwargs – Additional arguments for the objective function

resize(new_size: int) → None

Resize the population to a new size

Parameters:

new_size (int) – New population size

apply_bounds() → None

Apply boundary constraints to all positions

class porcupy.porcupines.DefenseMechanisms(alpha: float = 0.2, tf: float = 0.8)

Class implementing the four defense mechanisms of the Crested Porcupine Optimizer.

This class provides methods for the four defense mechanisms: sight, sound, odor, and physical attack, which are used to update the positions of porcupines.

Parameters:

• alpha (float) – Convergence rate for the fourth defense mechanism.

• tf (float) – Tradeoff threshold between third and fourth mechanisms.

alpha

Convergence rate for the fourth defense mechanism.

Type:

float

tf

Tradeoff threshold between third and fourth mechanisms.

Type:

float

alpha = 0.2

tf = 0.8

sight_defense(position: numpy.ndarray, other_position: numpy.ndarray, best_position: numpy.ndarray) → numpy.ndarray

Apply the first defense mechanism (sight)

Parameters:

• position (numpy.ndarray) – Current position of the porcupine

• other_position (numpy.ndarray) – Position of another random porcupine

• best_position (numpy.ndarray) – Global best position

Returns:

Updated position

Return type:

numpy.ndarray

sound_defense(position: numpy.ndarray, other_position: numpy.ndarray, rand_diff: numpy.ndarray) → numpy.ndarray

Apply the second defense mechanism (sound)

Parameters:

• position (numpy.ndarray) – Current position of the porcupine

• other_position (numpy.ndarray) – Position of another random porcupine

• rand_diff (numpy.ndarray) – Random difference vector

Returns:

Updated position

Return type:

numpy.ndarray

odor_defense(position: numpy.ndarray, other_position: numpy.ndarray, rand_diff: numpy.ndarray, fitness: float, fitness_sum: float, t: int, max_iter: int) → numpy.ndarray

Apply the third defense mechanism (odor)

Parameters:

• position (numpy.ndarray) – Current position of the porcupine

• other_position (numpy.ndarray) – Position of another random porcupine

• rand_diff (numpy.ndarray) – Random difference vector

• fitness (float) – Current fitness of the porcupine

• fitness_sum (float) – Sum of all fitness values in the population

• t (int) – Current iteration

• max_iter (int) – Maximum number of iterations

Returns:

Updated position

Return type:

numpy.ndarray

physical_attack(position: numpy.ndarray, other_position: numpy.ndarray, best_position: numpy.ndarray, fitness: float, fitness_sum: float, t: int, max_iter: int) → numpy.ndarray

Apply the fourth defense mechanism (physical attack)

Parameters:

• position (numpy.ndarray) – Current position of the porcupine

• other_position (numpy.ndarray) – Position of another random porcupine

• best_position (numpy.ndarray) – Global best position

• fitness (float) – Current fitness of the porcupine

• fitness_sum (float) – Sum of all fitness values in the population

• t (int) – Current iteration

• max_iter (int) – Maximum number of iterations

Returns:

Updated position

Return type:

numpy.ndarray

class porcupy.porcupines.PopulationManager(initial_pop_size: int, min_pop_size: int, max_iter: int, cycles: int = 2)

Class for managing the population dynamics in the Crested Porcupine Optimizer.

This class handles the cyclic population reduction strategy and other population management tasks.

Parameters:

• initial_pop_size (int) – Initial population size.

• min_pop_size (int) – Minimum population size during reduction.

• max_iter (int) – Maximum number of iterations.

• cycles (int) – Number of cycles for population reduction.

initial_pop_size

Initial population size.

Type:

int

min_pop_size

Minimum population size during reduction.

Type:

int

max_iter

Maximum number of iterations.

Type:

int

cycles

Number of cycles for population reduction.

Type:

int

initial_pop_size

min_pop_size

max_iter

cycles = 2

calculate_pop_size(iteration: int) → int

Calculate the population size for the current iteration

Parameters:

iteration (int) – Current iteration number

Returns:

Population size for the current iteration

Return type:

int