ParticleSwarm: Swarm
In particle.swarm.optimisation: Optimisation with Particle Swarm Optimisation
Description
Active bindings
Methods
Examples
Description
Particle Swarm, used to launch the Particle Swarm Optimisation, The PSO is used to maximise the fitness.
Active bindings
pop_size(numeric) number of particles in the swarm
ranges_of_values(list) range for each value for the particle
values_names(list) list of names for each value (optionnal)
pop(list) list of particle in the swarm
fitness_function(function) fitness function used to find the fitness of the particle
list_fitness(list) list of fitness of the particles
max_it(numeric) maximum number of iteration
acceleration_coefficient_range(list) coefficient c1 and c2 for the particles
swarm_best_fitness(numeric) best fitness of the swarm
swarm_best_values(numeric) values of the particle with the best fitness
inertia(numeric) inertia of the particles
Methods
Public methods
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Method new()
Create a new ParticleSwarm object.
Usage
ParticleSwarm$new(
pop_size,
values_names,
fitness_function,
max_it,
acceleration_coefficient_range,
inertia,
ranges_of_values
)
Arguments
pop_sizenumber of individu in the swarm. (numeric)
values_nameslist of names for each value (character)
fitness_functionfunction used to test the Particle and find his fitness. (function)
max_itMaximum number of iteration for the PSO. (numeric)
acceleration_coefficient_rangea vector of four values (min and max for c1 and c2) (numeric)
inertiaThe inertia for the particle (the influence of the previous velocity on the next velocity). (numeric)
ranges_of_valuesrange for each value of the particle (min and max). (List)
Returns
A new ParticleSwarm object.
Examples
# Create a ParticleSwarm object
swarm <- ParticleSwarm$new(pop_size=20,
values_names=c('a','b'),
max_it=20,
fitness_function = function(values){return(values[1]+values[2])},
acceleration_coefficient=list(c(0.5,1),c(0.5,1)),
inertia=0.5,
ranges_of_values=list(c(-100,100),c(-100,100)))
Method run()
Make the Particle Swarm Optimisation
Usage
ParticleSwarm$run(
verbose = TRUE,
plot = TRUE,
save_file = FALSE,
dir_name = "PSO_pop"
)
Arguments
verboseprint the different step (iteration and individu)
plotplot the result of each iteration (only for 2D or 3D problem)
save_filesave the population of each Iteration in a file and save the plot if plot=TRUE
dir_namename of the directory, default value is PSO_pop
Returns
self
Examples
# Create a ParticleSwarm object
swarm <- ParticleSwarm$new(pop_size=20,
values_names=c('a','b'),
max_it=20,
fitness_function = function(values){return(values[1]+values[2])},
acceleration_coefficient=list(c(0.5,1),c(0.5,1)),
inertia=0.5,
ranges_of_values=list(c(-100,100),c(-100,100)))
# run the PSO
swarm$run(verbose = FALSE,
plot = FALSE,
save_file = FALSE)
# return the best result:
print(swarm$swarm_best_values)
Method generate_pop()
create the population of the swarm (this method is automatically called by the run method)
Usage
ParticleSwarm$generate_pop(verbose = TRUE)
Arguments
verboseprint the advancement or not
Returns
self
Method move_the_swarm()
The method used to change the location of each particle (this method is automatically called by the run method)
Usage
ParticleSwarm$move_the_swarm(verbose)
Arguments
verboseprint or not the advancement
Returns
self
Method save_pop()
The method used to save the values and fitness of the population in a CSV file (this method is automatically called by the run method if you have chosen to save the result)
Usage
ParticleSwarm$save_pop(nb_it, dir_name)
Arguments
nb_itnumber of the iteration, used to create the name of the csv file
dir_nameName of the directory
Returns
self
Method plot_the_swarm_2D()
method used to plot a 2D plot (this method is automatically called by the run method if you have chosen to plot the swarm)
Usage
ParticleSwarm$plot_the_swarm_2D(nb_it, save_file)
Arguments
nb_itnumber of the iteration used to save the plot as a png
save_filesave the plot as a file
Returns
self
Method plot_the_swarm_3D()
method used to plot a 3D plot
Usage
ParticleSwarm$plot_the_swarm_3D(nb_it, save_file)
Arguments
nb_itnumber of the iteration used to save the plot as a png (this method is automatically called by the run method if you have chosen to plot the swarm)
save_filesave the plot as a file
Returns
self
Method print()
Print the current result of the population
Usage
ParticleSwarm$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
ParticleSwarm$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
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# In this example we use the PSO to solve the following equation:
# a * 5 + b * 25 + 10 = 15
fitness_function <- function(values){
a <- values[1]
b <- values[2]
particule_result <- a*5 + b*25 + 10
difference <- 15 - particule_result
fitness <- 1 - abs(difference)
return(fitness)
}
values_ranges <- list(c(-10^3,10^3),c(-10^3,10^3))
swarm <- ParticleSwarm$new(pop_size = 200,
values_names = list("a","b"),
fitness_function = fitness_function,
max_it = 75,
acceleration_coefficient_range = list(c(0,1),c(0,1)),
inertia = 0.5,
ranges_of_values = values_ranges)
swarm$run(plot = FALSE,verbose = FALSE,save_file = FALSE)
# the solution is :
swarm$swarm_best_values
swarm$swarm_best_values[[1]]*5 + swarm$swarm_best_values[[2]] *25 + 10
## ------------------------------------------------
## Method `ParticleSwarm$new`
## ------------------------------------------------
# Create a ParticleSwarm object
swarm <- ParticleSwarm$new(pop_size=20,
values_names=c('a','b'),
max_it=20,
fitness_function = function(values){return(values[1]+values[2])},
acceleration_coefficient=list(c(0.5,1),c(0.5,1)),
inertia=0.5,
ranges_of_values=list(c(-100,100),c(-100,100)))
## ------------------------------------------------
## Method `ParticleSwarm$run`
## ------------------------------------------------
# Create a ParticleSwarm object
swarm <- ParticleSwarm$new(pop_size=20,
values_names=c('a','b'),
max_it=20,
fitness_function = function(values){return(values[1]+values[2])},
acceleration_coefficient=list(c(0.5,1),c(0.5,1)),
inertia=0.5,
ranges_of_values=list(c(-100,100),c(-100,100)))
# run the PSO
swarm$run(verbose = FALSE,
plot = FALSE,
save_file = FALSE)
# return the best result:
print(swarm$swarm_best_values)
particle.swarm.optimisation documentation built on May 21, 2021, 9:06 a.m.
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Description Active bindings Methods Examples
Description
Particle Swarm, used to launch the Particle Swarm Optimisation, The PSO is used to maximise the fitness.
Active bindings
pop_size(numeric) number of particles in the swarm
ranges_of_values(list) range for each value for the particle
values_names(list) list of names for each value (optionnal)
pop(list) list of particle in the swarm
fitness_function(function) fitness function used to find the fitness of the particle
list_fitness(list) list of fitness of the particles
max_it(numeric) maximum number of iteration
acceleration_coefficient_range(list) coefficient c1 and c2 for the particles
swarm_best_fitness(numeric) best fitness of the swarm
swarm_best_values(numeric) values of the particle with the best fitness
inertia(numeric) inertia of the particles
Methods
Public methods
Method new()
Create a new ParticleSwarm object.
Usage
ParticleSwarm$new( pop_size, values_names, fitness_function, max_it, acceleration_coefficient_range, inertia, ranges_of_values )
Arguments
pop_sizenumber of individu in the swarm. (numeric)
values_nameslist of names for each value (character)
fitness_functionfunction used to test the Particle and find his fitness. (function)
max_itMaximum number of iteration for the PSO. (numeric)
acceleration_coefficient_rangea vector of four values (min and max for c1 and c2) (numeric)
inertiaThe inertia for the particle (the influence of the previous velocity on the next velocity). (numeric)
ranges_of_valuesrange for each value of the particle (min and max). (List)
Returns
A new ParticleSwarm object.
Examples
# Create a ParticleSwarm object
swarm <- ParticleSwarm$new(pop_size=20,
values_names=c('a','b'),
max_it=20,
fitness_function = function(values){return(values[1]+values[2])},
acceleration_coefficient=list(c(0.5,1),c(0.5,1)),
inertia=0.5,
ranges_of_values=list(c(-100,100),c(-100,100)))
Method run()
Make the Particle Swarm Optimisation
Usage
ParticleSwarm$run( verbose = TRUE, plot = TRUE, save_file = FALSE, dir_name = "PSO_pop" )
Arguments
verboseprint the different step (iteration and individu)
plotplot the result of each iteration (only for 2D or 3D problem)
save_filesave the population of each Iteration in a file and save the plot if plot=TRUE
dir_namename of the directory, default value is PSO_pop
Returns
self
Examples
# Create a ParticleSwarm object
swarm <- ParticleSwarm$new(pop_size=20,
values_names=c('a','b'),
max_it=20,
fitness_function = function(values){return(values[1]+values[2])},
acceleration_coefficient=list(c(0.5,1),c(0.5,1)),
inertia=0.5,
ranges_of_values=list(c(-100,100),c(-100,100)))
# run the PSO
swarm$run(verbose = FALSE,
plot = FALSE,
save_file = FALSE)
# return the best result:
print(swarm$swarm_best_values)
Method generate_pop()
create the population of the swarm (this method is automatically called by the run method)
Usage
ParticleSwarm$generate_pop(verbose = TRUE)
Arguments
verboseprint the advancement or not
Returns
self
Method move_the_swarm()
The method used to change the location of each particle (this method is automatically called by the run method)
Usage
ParticleSwarm$move_the_swarm(verbose)
Arguments
verboseprint or not the advancement
Returns
self
Method save_pop()
The method used to save the values and fitness of the population in a CSV file (this method is automatically called by the run method if you have chosen to save the result)
Usage
ParticleSwarm$save_pop(nb_it, dir_name)
Arguments
nb_itnumber of the iteration, used to create the name of the csv file
dir_nameName of the directory
Returns
self
Method plot_the_swarm_2D()
method used to plot a 2D plot (this method is automatically called by the run method if you have chosen to plot the swarm)
Usage
ParticleSwarm$plot_the_swarm_2D(nb_it, save_file)
Arguments
nb_itnumber of the iteration used to save the plot as a png
save_filesave the plot as a file
Returns
self
Method plot_the_swarm_3D()
method used to plot a 3D plot
Usage
ParticleSwarm$plot_the_swarm_3D(nb_it, save_file)
Arguments
nb_itnumber of the iteration used to save the plot as a png (this method is automatically called by the run method if you have chosen to plot the swarm)
save_filesave the plot as a file
Returns
self
Method print()
Print the current result of the population
Usage
ParticleSwarm$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
ParticleSwarm$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | # In this example we use the PSO to solve the following equation:
# a * 5 + b * 25 + 10 = 15
fitness_function <- function(values){
a <- values[1]
b <- values[2]
particule_result <- a*5 + b*25 + 10
difference <- 15 - particule_result
fitness <- 1 - abs(difference)
return(fitness)
}
values_ranges <- list(c(-10^3,10^3),c(-10^3,10^3))
swarm <- ParticleSwarm$new(pop_size = 200,
values_names = list("a","b"),
fitness_function = fitness_function,
max_it = 75,
acceleration_coefficient_range = list(c(0,1),c(0,1)),
inertia = 0.5,
ranges_of_values = values_ranges)
swarm$run(plot = FALSE,verbose = FALSE,save_file = FALSE)
# the solution is :
swarm$swarm_best_values
swarm$swarm_best_values[[1]]*5 + swarm$swarm_best_values[[2]] *25 + 10
## ------------------------------------------------
## Method `ParticleSwarm$new`
## ------------------------------------------------
# Create a ParticleSwarm object
swarm <- ParticleSwarm$new(pop_size=20,
values_names=c('a','b'),
max_it=20,
fitness_function = function(values){return(values[1]+values[2])},
acceleration_coefficient=list(c(0.5,1),c(0.5,1)),
inertia=0.5,
ranges_of_values=list(c(-100,100),c(-100,100)))
## ------------------------------------------------
## Method `ParticleSwarm$run`
## ------------------------------------------------
# Create a ParticleSwarm object
swarm <- ParticleSwarm$new(pop_size=20,
values_names=c('a','b'),
max_it=20,
fitness_function = function(values){return(values[1]+values[2])},
acceleration_coefficient=list(c(0.5,1),c(0.5,1)),
inertia=0.5,
ranges_of_values=list(c(-100,100),c(-100,100)))
# run the PSO
swarm$run(verbose = FALSE,
plot = FALSE,
save_file = FALSE)
# return the best result:
print(swarm$swarm_best_values)
|
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