pyrimidine.misc subpackage

pyrimidine.misc.aco module

Ant Colony Optimization

Ref Blum, Christian. “Ant colony optimization: Introduction and recent trends.” Physics of Life reviews 2.4 (2005): 353-373.

class pyrimidine.misc.aco.BaseAnt

Bases: FitnessMixin

get_length(distances=None)

init()

move(colony=None, n_steps=1)

To move an ant

Parameters:

• colony (None, optional) – the population of ants

• n_steps (int, optional) – the number of steps in each move

Returns:

bool

params = {'greedy_degree': 1, 'initial_position': None, 'move_flag': True, 'n_steps': 1, 'path': None}

release_pheromone(colony)

reset()

class pyrimidine.misc.aco.BaseAntColony(*args, **kwargs)

Bases: PopulationMixin

alias = {'ants': 'elements', 'get_worst_ants': 'get_worst_elements', 'worst_ant': 'worst_element'}

apply(f, *args, **kwargs)

element_class

alias of BaseAnt

property elements

classmethod from_distances(n_ants=10, distances=None)

classmethod from_positions(n_ants=10, positions=None)

init()

isa(cls)

move(*args, **kwargs)

property move_proba

property n_elements

params = {'alpha': 1, 'beta': 5, 'local_max_iter': 3, 'max_iter': 100, 'move_rate': 0.5, 'n_steps': 1, 'reset_rate': 0.3, 'sedimentation': 100, 'volatilization': 0.75}

regester(name, key, force=True)

regester_map(name, key=None, force=True)

transition(*args, **kwargs)

The core method of the object.

This method transitions one state of the object to another state based on certain rules, such as crossing and mutating for individuals in GA, or the moving method in Simulated Annealing.

update_pheromone()

pyrimidine.misc.aco.random() → x in the interval [0, 1).

pyrimidine.misc.ba module

The Bat Algorithm is a nature-inspired optimization algorithm developed by Xin-She Yang in 2010. It is based on the echolocation behavior of bats. Bats emit ultrasonic pulses and listen to the echoes to determine the distance to obstacles and the location of prey. This behavior forms the basis of the algorithm where solutions are represented as virtual bats, and their positions in the search space are adjusted iteratively to find the optimal solution.

References Gagnon, Iannick et al. “A critical analysis of the bat algorithm.” Engineering Reports 2 (2020): n. pag. Yang, Xin-She. “A New Metaheuristic Bat-Inspired Algorithm.” Nature Inspired Cooperative Strategies for Optimization (2010). Yang, Xin-She and Amir Hossein Gandomi. “Bat algorithm: a novel approach for global engineering optimization.” Engineering Computations 29 (2012): 464-483.

class pyrimidine.misc.ba.Bat(*args, **kwargs)

Bases: BaseParticle

a bat is regarded as a particle

see BaseParticle

alias = {'chromosomes': 'elements', 'n_chromosomes': 'n_elements'}

apply(f, *args, **kwargs)

property elements

isa(cls)

move()

property n_elements

params = {'frequency': 0.5, 'loudness': 0, 'max_iter': 100, 'pulse_rate': 0, 'scale': 0.1}

property position

regester(name, key, force=True)

regester_map(name, key=None, force=True)

property velocity

class pyrimidine.misc.ba.Bats(*args, **kwargs)

Bases: BasePopulation

alias = {'bats': 'elements', 'best_individual': 'best_element', 'best_individuals': 'best_elements', 'get_best_individual': 'get_best_element', 'get_best_individuals': 'get_best_elements', 'individuals': 'elements', 'n_bats': 'n_elements', 'n_individuals': 'n_elements', 'worst_individual': 'worst_element'}

apply(f, *args, **kwargs)

property elements

init()

isa(cls)

property n_elements

params = {'alpha': 0.95, 'gamma': 0.5, 'mate_prob': 0.75, 'max_iter': 100, 'mutate_prob': 0.2, 'pulse_rate': 0.9, 'scaling': 0.2, 'tourn_size': 5}

regester(name, key, force=True)

regester_map(name, key=None, force=True)

transition(k)

Transition of the states of population. The core method of the class

It is considered to be the standard flow of the Genetic Algorithm

pyrimidine.misc.ba.random() → x in the interval [0, 1).

pyrimidine.misc.fa module

Firefly Algorithm

References Yang, X. S. (2010). “Firefly Algorithm: A New Approach for Optimization”. In Stochastic Algorithms: Foundations and Applications (pp. 169-178). Springer Berlin Heidelberg. Yang, X. S. (2013). “Nature-Inspired Metaheuristic Algorithms”. Luniver Press.

class pyrimidine.misc.fa.BaseFirefly(*args, **kwargs)

Bases: BaseParticle

alias = {'chromosomes': 'elements', 'n_chromosomes': 'n_elements'}

apply(f, *args, **kwargs)

property elements

isa(cls)

property n_elements

params = {'alpha': 1, 'gamma': 1, 'max_iter': 100}

random_move()

regester(name, key, force=True)

regester_map(name, key=None, force=True)

update_vilocity(fame=None, *args, **kwargs)

class pyrimidine.misc.fa.StandardFireflies

Bases: PopulationMixin

Starndard Firefly Algorithm

element_class

alias of BaseFirefly

params = {'alpha': 0.2, 'beta': 1, 'gamma': 1}

transition(*args, **kwargs)

The core method of the object.

This method transitions one state of the object to another state based on certain rules, such as crossing and mutating for individuals in GA, or the moving method in Simulated Annealing.

pyrimidine.misc.fa.attractiveness(distance, gamma=1.0)

pyrimidine.misc.gsa module

The Gravity Searching Algorithm (GSA) is a metaheuristic optimization method that simulates the law of gravity in physics. It was introduced as a nature-inspired algorithm for solving optimization problems, particularly in continuous domains.

References 1. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). “GSA: A Gravitational Search Algorithm”. Information Sciences, 179(13), 2232-2248. 2. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2011). “A New Method for Solving Optimization Problems Using Gravitational Search Algorithm”. International Journal of Computer Applications, 22(8), 1-6. 3. Niazi, M., Mirjalili, S., Mirjalili, S. M., & Yang, X. S. (2016). “Enhanced Gravity Search Algorithm”. Swarm and Evolutionary Computation, 6(1), 10-21.

class pyrimidine.misc.gsa.GravitySearch

Bases: PopulationMixin

Standard GSA

Extends:

PopulationMixin

alias = {'n_particles': 'n_elements', 'particles': 'elements'}

compute_accelerate()

compute_mass()

default_size = 20

element_class

alias of Particle

move()

params = {'attenuation_coefficient': 10, 'gravity_coefficient': 100}

transition(k)

Transitation of the states of particles

class pyrimidine.misc.gsa.Particle(*args, **kwargs)

Bases: BaseParticle

A particle in GSA

Extends:

PolyIndividual

Variables:

default_size {number} – one individual represented by 2 chromosomes: position and velocity phantom {Particle} – the current state of the particle moving in the solution space.

alias = {'chromosomes': 'elements', 'n_chromosomes': 'n_elements'}

apply(f, *args, **kwargs)

default_size = 2

element_class

alias of FloatChromosome

property elements

isa(cls)

move(*args, **kwargs)

property n_elements

params = {'accelerate': 0, 'max_iter': 100}

property position

regester(name, key, force=True)

regester_map(name, key=None, force=True)

property velocity

pyrimidine.misc.gsa.random() → x in the interval [0, 1).

pyrimidine.misc.sma module

Slime Mould Algorithm

class pyrimidine.misc.sma.SlimeMould(*args, **kwargs)

Bases: HOFPopulation

Slime Mould Algorithm

alias = {'best_individuals': 'best_elements', 'get_best_individual': 'get_best_element', 'get_best_individuals': 'get_best_elements', 'hof': 'hall_of_fame', 'individuals': 'elements', 'n_individuals': 'n_elements', 'worst_individual': 'worst_element'}

apply(f, *args, **kwargs)

approach_food(t)

element_class

alias of SlimyMaterial

property elements

get_ranks()

isa(cls)

property n_elements

params = {'hof_size': 2, 'mate_prob': 0.75, 'max_iter': 100, 'mutate_prob': 0.2, 'tourn_size': 5}

regester(name, key, force=True)

regester_map(name, key=None, force=True)

transition(t)

Update the hall_of_fame after each step of evolution

class pyrimidine.misc.sma.SlimyMaterial(*args, **kwargs)

Bases: BaseIndividual

alias = {'chromosomes': 'elements', 'n_chromosomes': 'n_elements'}

apply(f, *args, **kwargs)

approach_food(fame, direction, p, vb, vc)

backup(check=True)

Backup the fitness and other information

Parameters:

check (bool, optional) – check whether the fitness increases.

copy(*args, **kwargs)

copy the object

Parameters:

• type – the type of new object

• element_class (None, optional) – the new element_class

Returns:

a new object copy the data but with new type

property elements

property fitness

init()

isa(cls)

property memory

property n_elements

params = {'max_iter': 100}

random_move()

regester(name, key, force=True)

regester_map(name, key=None, force=True)

set_memory(**d)

property solution

get the attribute from memory, where the best solution is stored

pyrimidine.misc.sma.random() → x in the interval [0, 1).

pyrimidine.misc.ssa module

Sparrow Search Algorithm

The framework of the SSA

Input: G: the maximum iterations PD: the number of producers SD: the number of sparrows who perceive the danger R2 : the alarm value n: the number of sparrows Initialize a population of n sparrows and define its relevant parameters. Output: Xbest, fg. while (t < G)

Rank the fitness values and find the current best individual and the current worst individual. R2 = rand(1) for i = 1 : PD

update the sparrow’s location;

for i = (PD + 1)n

update the sparrow’s location;

for l = 1SD

update the sparrow’s location;

Get the current new location; If the new location is better than before, update it; t = t + 1

return Xbest, fg.

References Jiankai Xuea, and Bo Shena, A novel swarm intelligence optimization approach: sparrow search algorithm.

class pyrimidine.misc.ssa.BaseSparrow(*args, **kwargs)

Bases: FloatChromosome

alias = {'chromosomes': 'elements', 'n_chromosomes': 'n_elements'}

backup(check=True)

Backup the fitness and other information

Parameters:

check (bool, optional) – check whether the fitness increases.

copy(*args, **kwargs)

copy the object

Parameters:

• type – the type of new object

• element_class (None, optional) – the new element_class

Returns:

a new object copy the data but with new type

property fitness

init()

property memory

move(ST, i)

params = {'max_iter': 100}

set_memory(**d)

property solution

get the attribute from memory, where the best solution is stored

class pyrimidine.misc.ssa.Producer(*args, **kwargs)

Bases: BaseSparrow

alias = {'chromosomes': 'elements', 'n_chromosomes': 'n_elements'}

params = {'max_iter': 100}

class pyrimidine.misc.ssa.Scrounger(*args, **kwargs)

Bases: BaseSparrow

alias = {'chromosomes': 'elements', 'n_chromosomes': 'n_elements'}

move(worst, producer, i)

params = {'max_iter': 100}

class pyrimidine.misc.ssa.StandardSparrowSearch

Bases: PopulationMixin

Starndard Sparrow Search Algorithm

element_class

alias of BaseSparrow

init()

params = {'PD': 0.2, 'SD': 0.2, 'ST': 0.7, 'rho': 0.001}

transition(*args, **kwargs)

The core method of the object.

This method transitions one state of the object to another state based on certain rules, such as crossing and mutating for individuals in GA, or the moving method in Simulated Annealing.

pyrimidine.misc.ssa.random() → x in the interval [0, 1).