class Algorithmically::Stochastic::AdaptiveRandomSearch
Public Class Methods
new(max_iter, bounds, init_factor, s_factor, l_factor, iter_mult, max_no_impr)
click to toggle source
# File lib/Algorithmically/Stochastic/adaptive_random_search.rb, line 6 def initialize(max_iter, bounds, init_factor, s_factor, l_factor, iter_mult, max_no_impr) problem_size = bounds @bounds1 = Array.new(problem_size) { |i| [-5, +5] } @max_iter = max_iter @init_factor = init_factor @s_factor = s_factor @l_factor = l_factor @iter_mult = iter_mult @max_no_impr = max_no_impr @best = search(@max_iter, @bounds1, @init_factor, @s_factor, @l_factor, @iter_mult, @max_no_impr) puts "Done. Best Solution: c=#{@best[:cost]}, v=#{@best[:vector].inspect}" end
Public Instance Methods
large_step_size(iter, step_size, s_factor, l_factor, iter_mult)
click to toggle source
# File lib/Algorithmically/Stochastic/adaptive_random_search.rb, line 43 def large_step_size(iter, step_size, s_factor, l_factor, iter_mult) step_size * l_factor if iter > 0 and iter.modulo(iter_mult) == 0 step_size * s_factor end
objective_function(vector)
click to toggle source
# File lib/Algorithmically/Stochastic/adaptive_random_search.rb, line 19 def objective_function(vector) vector.inject(0) { |sum, x| sum + (x ** 2.0) } end
rand_in_bounds(min, max)
click to toggle source
# File lib/Algorithmically/Stochastic/adaptive_random_search.rb, line 23 def rand_in_bounds(min, max) min + ((max-min) * rand()) end
random_vector(minmax)
click to toggle source
# File lib/Algorithmically/Stochastic/adaptive_random_search.rb, line 27 def random_vector(minmax) Array.new(minmax.size) do |i| rand_in_bounds(minmax[i][0], minmax[i][1]) end end
search(max_iter, bounds, init_factor, s_factor, l_factor, iter_mult, max_no_impr)
click to toggle source
# File lib/Algorithmically/Stochastic/adaptive_random_search.rb, line 57 def search(max_iter, bounds, init_factor, s_factor, l_factor, iter_mult, max_no_impr) step_size = (bounds[0][1]-bounds[0][0]) * init_factor current, count = {}, 0 current[:vector] = random_vector(bounds) current[:cost] = objective_function(current[:vector]) max_iter.times do |iter| big_stepsize = large_step_size(iter, step_size, s_factor, l_factor, iter_mult) step, big_step = take_steps(bounds, current, step_size, big_stepsize) if step[:cost] <= current[:cost] or big_step[:cost] <= current[:cost] if big_step[:cost] <= step[:cost] step_size, current = big_stepsize, big_step else current = step end count = 0 else count += 1 count, step_size = 0, (step_size/s_factor) if count >= max_no_impr end puts " > iteration #{(iter+1)}, best=#{current[:cost]}" end current end
take_step(minmax, current, step_size)
click to toggle source
# File lib/Algorithmically/Stochastic/adaptive_random_search.rb, line 33 def take_step(minmax, current, step_size) position = Array.new(current.size) position.size.times do |i| min = [minmax[i][0], current[i]-step_size].max max = [minmax[i][1], current[i]+step_size].min position[i] = rand_in_bounds(min, max) end position end
take_steps(bounds, current, step_size, big_stepsize)
click to toggle source
# File lib/Algorithmically/Stochastic/adaptive_random_search.rb, line 48 def take_steps(bounds, current, step_size, big_stepsize) step, big_step = {}, {} step[:vector] = take_step(bounds, current[:vector], step_size) step[:cost] = objective_function(step[:vector]) big_step[:vector] = take_step(bounds, current[:vector], big_stepsize) big_step[:cost] = objective_function(big_step[:vector]) return step, big_step end