NiaPy.algorithms

NiaPy.algorithms.basic

Implementation of basic nature-inspired algorithms.

class NiaPy.algorithms.basic.BatAlgorithm(D, NP, nFES, A, r, Qmin, Qmax, benchmark)

Bases: object

Implementation of Bat algorithm.

Algorithm: Bat algorithm

Date: 2015

Authors: Iztok Fister Jr. and Marko Burjek

License: MIT

Reference paper:

Yang, Xin-She. “A new metaheuristic bat-inspired algorithm.” Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, 2010. 65-74.

__init__(self, D, NP, nFES, A, r, Qmin, Qmax, benchmark).

Arguments:

D {integer} – dimension of problem

NP {integer} – population size

nFES {integer} – number of function evaluations

A {decimal} – loudness

r {decimal} – pulse rate

Qmin {decimal} – minimum frequency

Qmax {decimal } – maximum frequency

benchmark {object} – benchmark implementation object

Raises:

TypeError – Raised when given benchmark function which does not exists.

best_bat()

Find the best bat.

eval_true()

Check evaluations.

init_bat()

Initialize population.

move_bat()

Move bats in search space.

run()

Run algorithm with initialized parameters.

Return {decimal} - best

classmethod simplebounds(val, lower, upper)

Keep it within bounds.

class NiaPy.algorithms.basic.FireflyAlgorithm(D, NP, nFES, alpha, betamin, gamma, benchmark)

Bases: object

Implementation of Firefly algorithm.

Algorithm: Firefly algorithm

Date: 2016

Authors: Iztok Fister Jr. and Iztok Fister

License: MIT

Reference paper:

Fister, I., Fister Jr, I., Yang, X. S., & Brest, J. (2013). A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation, 13, 34-46.

__init__(self, D, NP, nFES, alpha, betamin, gamma, benchmark).

Arguments:

D {integer} – dimension of problem

NP {integer} – population size

nFES {integer} – number of function evaluations

alpha {decimal} – alpha parameter

betamin {decimal} – betamin parameter

gamma {decimal} – gamma parameter

benchmark {object} – benchmark implementation object

Raises:

TypeError – Raised when given benchmark function which does not exists.

FindLimits(k)

Find limits.

alpha_new(a)

Optionally recalculate the new alpha value.

eval_true()

Check evaluations.

init_ffa()

Initialize firefly population.

move_ffa()

Move fireflies.

replace_ffa()

Replace the old population according to the new Index values.

run()

Run.

sort_ffa()

Implement bubble sort.

class NiaPy.algorithms.basic.DifferentialEvolutionAlgorithm(D, NP, nFES, F, CR, benchmark)

Bases: object

Implementation of Differential evolution algorithm.

Algorithm: Differential evolution algorithm

Date: 2018

Author: Uros Mlakar

License: MIT

Reference paper:

Storn, Rainer, and Kenneth Price. “Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces.” Journal of global optimization 11.4 (1997): 341-359.

__init__(self, D, NP, nFES, F, CR, benchmark).

Arguments:

D {integer} – dimension of problem

NP {integer} – population size

nFES {integer} – number of function evaluations

F {decimal} – scaling factor

CR {decimal} – crossover rate

benchmark {object} – benchmark implementation object

Raises:

TypeError – Raised when given benchmark function which does not exists.

evalPopulation()

Evaluate population.

generationStep(Population)

Implement main generation step.

initPopulation()

Initialize population.

run()

Run.

class NiaPy.algorithms.basic.FlowerPollinationAlgorithm(D, NP, nFES, p, benchmark)

Bases: object

Implementation of Flower Pollination algorithm.

Algorithm: Flower Pollination algorithm

Date: 2018

Authors: Dusan Fister & Iztok Fister Jr.

License: MIT

Reference paper:

Yang, Xin-She. “Flower pollination algorithm for global optimization.” International conference on unconventional computing and natural computation. Springer, Berlin, Heidelberg, 2012.

Implementation is based on the following MATLAB code: https://www.mathworks.com/matlabcentral/fileexchange/45112-flower-pollination-algorithm?requestedDomain=true

__init__(self, D, NP, nFES, p, benchmark).

Arguments:

D {integer} – dimension of problem

NP {integer} – population size

nFES {integer} – number of function evaluations

p {decimal} – probability switch

benchmark {object} – benchmark implementation object

Raises:

TypeError – Raised when given benchmark function which does not exists.

Levy()

Levy flight.

best_flower()

Check best solution.

eval_true()

Check evaluations.

init_flower()

Initialize flowers.

move_flower()

Move in search space.

run()

Run.

classmethod simplebounds(val, lower, upper)

Keep it within bounds.

class NiaPy.algorithms.basic.GreyWolfOptimizer(D, NP, nFES, benchmark)

Bases: object

Implementation of Grey wolf optimizer.

Algorithm: Grey wolf optimizer

Date: 2018

Author: Iztok Fister Jr.

License: MIT

Reference paper:

Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. “Grey wolf optimizer.” Advances in engineering software 69 (2014): 46-61. & Grey Wold Optimizer (GWO) source code version 1.0 (MATLAB) from MathWorks

__init__(self, D, NP, nFES, benchmark).

Arguments:

D {integer} – dimension of problem

NP {integer} – population size

nFES {integer} – number of function evaluations

benchmark {object} – benchmark implementation object

Raises:

TypeError – Raised when given benchmark function which does not exists.

bounds(position)

Keep it within bounds.

eval_true()

Check evaluations.

initialization()

Initialize positions.

move()

Move wolves in search space.

run()

Run.

class NiaPy.algorithms.basic.GeneticAlgorithm(D, NP, nFES, Ts, Mr, gamma, benchmark)

Bases: object

Implementation of Genetic algorithm.

Algorithm: Genetic algorithm

Date: 2018

Author: Uros Mlakar

License: MIT

__init__(self, D, NP, nFES, Ts, Mr, gamma, benchmark).

Arguments:

D {integer} – dimension of problem

NP {integer} – population size

nFES {integer} – number of function evaluations

Ts {integer} – tournament selection

Mr {decimal} – mutation rate

gamma {decimal} – minimum frequency

benchmark {object} – benchmark implementation object

Raises:

TypeError – Raised when given benchmark function which does not exists.

CrossOver(parent1, parent2)

Crossover.

Mutate(child)

Mutation.

TournamentSelection()

Tournament selection.

checkForBest(pChromosome)

Check best solution.

init()

Initialize population.

run()

Run.

tryEval(c)

Check evaluations.

class NiaPy.algorithms.basic.ArtificialBeeColonyAlgorithm(D, NP, nFES, benchmark)

Bases: object

Implementation of Artificial Bee Colony algorithm.

Algorithm: Artificial Bee Colony algorithm

Date: 2018

Author: Uros Mlakar

License: MIT

Reference paper:

Karaboga, D., and Bahriye B. “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm.” Journal of global optimization 39.3 (2007): 459-471.

__init__(self, D, NP, nFES, benchmark).

Arguments:

D {integer} – dimension of problem

NP {integer} – population size

nFES {integer} – number of function evaluations

benchmark {object} – benchmark implementation object

Raises:

TypeError – Raised when given benchmark function which does not exists.

CalculateProbs()

Calculate probs.

checkForBest(Solution)

Check best solution.

init()

Initialize positions.

run()

Run.

tryEval(b)

Check evaluations.

class NiaPy.algorithms.basic.ParticleSwarmAlgorithm(D, NP, nFES, C1, C2, w, vMin, vMax, benchmark)

Bases: object

Implementation of Particle Swarm Optimization algorithm.

Algorithm: Particle Swarm Optimization algorithm

Date: 2018

Authors: Lucija Brezočnik, Grega Vrbančič, and Iztok Fister Jr.

License: MIT

Reference paper:

Kennedy, J. and Eberhart, R. “Particle Swarm Optimization”. Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948, 1995.

__init__(self, NP, D, nFES, C1, C2, w, vMin, vMax, benchmark).

Arguments:

NP {integer} – population size

D {integer} – dimension of problem

nFES {integer} – number of function evaluations

C1 {decimal} – cognitive component

C2 {decimal} – social component

w {decimal} – inertia weight

vMin {decimal} – minimal velocity

vMax {decimal} – maximal velocity

benchmark {object} – benchmark implementation object

bounds(position)

Keep it within bounds.

eval_true()

Check evaluations.

init()

Initialize positions.

move_particles()

Move particles in search space.

run()

Run.

NiaPy.algorithms.modified

Implementation of modified nature-inspired algorithms.

class NiaPy.algorithms.modified.HybridBatAlgorithm(D, NP, nFES, A, r, F, CR, Qmin, Qmax, benchmark)

Bases: object

Implementation of Hybrid bat algorithm.

Algorithm: Hybrid bat algorithm

Date: 2018

Author: Grega Vrbancic

License: MIT

Reference paper:

Fister Jr., Iztok and Fister, Dusan and Yang, Xin-She. “A Hybrid Bat Algorithm”. Elektrotehniski vestnik, 2013. 1-7.

__init__(self, D, NP, nFES, A, r, Qmin, Qmax, benchmark).

Arguments:

D {integer} – dimension of problem

NP {integer} – population size

nFES {integer} – number of function evaluations

A {decimal} – loudness

r {decimal} – pulse rate

Qmin {decimal} – minimum frequency

Qmax {decimal } – maximum frequency

benchmark {object} – benchmark implementation object

Raises:

TypeError – Raised when given benchmark function which does not exists.

best_bat()

Find the best bat.

eval_true()

Check evauations.

init_bat()

Initialize population.

move_bat()

Move bats in search space.

run()

Run.

classmethod simplebounds(val, lower, upper)

Keep it within bounds.

class NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolutionAlgorithm(D, NP, nFES, F, CR, Tao, benchmark)

Bases: object

Implementation of Self-adaptive differential evolution algorithm.

Algorithm: Self-adaptive differential evolution algorithm

Date: 2018

Author: Uros Mlakar

License: MIT

Reference paper:

Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE transactions on evolutionary computation, 10(6), 646-657, 2006.

__init__(self, D, NP, nFES, F, CR, Tao, benchmark).

Arguments:

D {integer} – dimension of problem

NP {integer} – population size

nFES {integer} – number of function evaluations

F {decimal} – scaling factor

CR {decimal} – crossover rate

Tao {decimal}

benchmark {object} – benchmark implementation object

Raises:

TypeError – Raised when given benchmark function which does not exists.

evalPopulation()

Evaluate population.

generationStep(Population)

Implement main DE/jDE step.

initPopulation()

Initialize population.

run()

Run.

tryEval(v)

Check evaluations.