class Algorithmically::Swarm::ParticleSwarm
Public Class Methods
new(problem_size, max_gens, search_space, vel_space, pop_size, max_vel, c1, c2)
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# File lib/Algorithmically/Swarm/particle_swarm.rb, line 6 def initialize(problem_size, max_gens, search_space, vel_space, pop_size, max_vel, c1, c2) @search_space, @vel_space = search_space, vel_space @search_space = Array.new(problem_size) {|i| [-5, 5]} @vel_space = Array.new(problem_size) {|i| [-1, 1]} best = search(max_gens, @search_space, @vel_space, pop_size, max_vel, c1, c2) puts "done! Solution: f=#{best[:cost]}, s=#{best[:position].inspect}" end
Public Instance Methods
create_particle(search_space, vel_space)
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# File lib/Algorithmically/Swarm/particle_swarm.rb, line 24 def create_particle(search_space, vel_space) particle = {} particle[:position] = random_vector(search_space) particle[:cost] = objective_function(particle[:position]) particle[:b_position] = Array.new(particle[:position]) particle[:b_cost] = particle[:cost] particle[:velocity] = random_vector(vel_space) particle end
get_global_best(population, current_best=nil)
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# File lib/Algorithmically/Swarm/particle_swarm.rb, line 34 def get_global_best(population, current_best=nil) population.sort! { |x, y| x[:cost] <=> y[:cost] } best = population.first if current_best.nil? or best[:cost] <= current_best[:cost] current_best = {} current_best[:position] = Array.new(best[:position]) current_best[:cost] = best[:cost] end current_best end
objective_function(vector)
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# File lib/Algorithmically/Swarm/particle_swarm.rb, line 14 def objective_function(vector) vector.inject(0.0) { |sum, x| sum + (x ** 2.0) } end
random_vector(minmax)
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# File lib/Algorithmically/Swarm/particle_swarm.rb, line 18 def random_vector(minmax) Array.new(minmax.size) do |i| minmax[i][0] + ((minmax[i][1] - minmax[i][0]) * rand()) end end
search(max_gens, search_space, vel_space, pop_size, max_vel, c1, c2)
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# File lib/Algorithmically/Swarm/particle_swarm.rb, line 74 def search(max_gens, search_space, vel_space, pop_size, max_vel, c1, c2) pop = Array.new(pop_size) { create_particle(search_space, vel_space) } gbest = get_global_best(pop) max_gens.times do |gen| pop.each do |particle| update_velocity(particle, gbest, max_vel, c1, c2) update_position(particle, search_space) particle[:cost] = objective_function(particle[:position]) update_best_position(particle) end gbest = get_global_best(pop, gbest) puts " > gen #{gen+1}, fitness=#{gbest[:cost]}" end gbest end
update_best_position(particle)
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# File lib/Algorithmically/Swarm/particle_swarm.rb, line 68 def update_best_position(particle) return if particle[:cost] > particle[:b_cost] particle[:b_cost] = particle[:cost] particle[:b_position] = Array.new(particle[:position]) end
update_position(part, bounds)
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# File lib/Algorithmically/Swarm/particle_swarm.rb, line 55 def update_position(part, bounds) part[:position].each_with_index do |v, i| part[:position][i] = v + part[:velocity][i] if part[:position][i] > bounds[i][1] part[:position][i]=bounds[i][1]-(part[:position][i]-bounds[i][1]).abs part[:velocity][i] *= -1.0 elsif part[:position][i] < bounds[i][0] part[:position][i]=bounds[i][0]+(part[:position][i]-bounds[i][0]).abs part[:velocity][i] *= -1.0 end end end
update_velocity(particle, gbest, max_v, c1, c2)
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# File lib/Algorithmically/Swarm/particle_swarm.rb, line 45 def update_velocity(particle, gbest, max_v, c1, c2) particle[:velocity].each_with_index do |v, i| v1 = c1 * rand() * (particle[:b_position][i] - particle[:position][i]) v2 = c2 * rand() * (gbest[:position][i] - particle[:position][i]) particle[:velocity][i] = v + v1 + v2 particle[:velocity][i] = max_v if particle[:velocity][i] > max_v particle[:velocity][i] = -max_v if particle[:velocity][i] < -max_v end end