Class: MetaheuristicAlgorithms::SimulatedAnnealing

Inherits:
Object
  • Object
show all
Includes:
Helper
Defined in:
lib/metaheuristic_algorithms/simulated_annealing.rb

Instance Method Summary collapse

Methods included from Helper

#bigdecimal_rand

Constructor Details

#initialize(function_wrapper, number_of_variables: 1, objective: :maximization) ⇒ SimulatedAnnealing 



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# File 'lib/metaheuristic_algorithms/simulated_annealing.rb', line 9

def initialize(function_wrapper, number_of_variables: 1, objective: :maximization)
  @function_wrapper = function_wrapper
  @number_of_variables = number_of_variables
  @objective_comparison_operator = case objective
                           when :maximization
                             :>
                           when :minimization
                             :<
                         end        
end

Instance Method Details

#search(temperature: BigDecimal('100.0'), minimal_temperature: BigDecimal('1'), maximum_number_of_rejections: 2500, maximum_number_of_runs: 500, maximum_number_of_acceptances: 15, bolzmann_constant: BigDecimal('1'), cooling_factor: BigDecimal('0.95'), energy_norm: BigDecimal('10'), standard_diviation_for_estimation: BigDecimal('6'), ratio_of_energy_delta_over_evaluation_delta: BigDecimal('10')) ⇒ Object 



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# File 'lib/metaheuristic_algorithms/simulated_annealing.rb', line 20

def search(temperature: BigDecimal('100.0'), minimal_temperature: BigDecimal('1'), maximum_number_of_rejections: 2500, 
           maximum_number_of_runs: 500, maximum_number_of_acceptances: 15, 
           bolzmann_constant: BigDecimal('1'), cooling_factor: BigDecimal('0.95'), energy_norm: BigDecimal('10'), 
           standard_diviation_for_estimation: BigDecimal('6'), ratio_of_energy_delta_over_evaluation_delta: BigDecimal('10'))

  temperature = BigDecimal(temperature.to_s) unless temperature.kind_of?(BigDecimal)
  minimal_temperature = BigDecimal(minimal_temperature.to_s) unless minimal_temperature.kind_of?(BigDecimal)
  maximum_number_of_rejections = maximum_number_of_rejections.to_i unless maximum_number_of_rejections.kind_of?(Integer)
  maximum_number_of_runs = maximum_number_of_runs.to_i unless maximum_number_of_runs.kind_of?(Integer)
  maximum_number_of_acceptances = maximum_number_of_acceptances.to_i unless maximum_number_of_acceptances.kind_of?(Integer)
  bolzmann_constant = BigDecimal(bolzmann_constant.to_s) unless bolzmann_constant.kind_of?(BigDecimal)
  cooling_factor = BigDecimal(cooling_factor.to_s) unless cooling_factor.kind_of?(BigDecimal)
  energy_norm = BigDecimal(energy_norm.to_s) unless energy_norm.kind_of?(BigDecimal)
  standard_diviation_for_estimation = BigDecimal(standard_diviation_for_estimation.to_s) unless standard_diviation_for_estimation.kind_of?(BigDecimal)
  ratio_of_energy_delta_over_evaluation_delta = BigDecimal(ratio_of_energy_delta_over_evaluation_delta.to_s) unless ratio_of_energy_delta_over_evaluation_delta.kind_of?(BigDecimal)

  number_of_runs = 0
  number_of_rejections = 0
  number_of_acceptances = 0
  total_evaluations = 0

  initial_estimates = @function_wrapper.initial_decision_variable_value_estimates

  best_evaluation = @function_wrapper.objective_function_value(initial_estimates)

  best_solution = initial_estimates      

  # TODO: Add the code to check with minimal_function_value if looking for the minimal value: 
  while temperature > minimal_temperature && number_of_rejections <= maximum_number_of_rejections

    number_of_runs = number_of_runs + 1

    if number_of_runs >= maximum_number_of_runs || number_of_acceptances > maximum_number_of_acceptances

      temperature = cooling_factor * temperature
      total_evaluations = total_evaluations + 1
      number_of_runs = 1
      number_of_acceptances = 1

    end

    new_estimates = estimate_solution(initial_estimates, standard_diviation_for_estimation)
    new_evaluation = @function_wrapper.objective_function_value(new_estimates)

    evaluation_delta = ratio_of_energy_delta_over_evaluation_delta * (new_evaluation - best_evaluation)

    # Accept if improved:
    # When objective = :maximization, "if evaluation_delta > 0 && evaluation_delta > energy_norm"
    # When objective = :minimization, "if evaluation_delta < 0 && -evaluation_delta > energy_norm"
    if evaluation_delta.send(@objective_comparison_operator, BigDecimal('0')) && evaluation_delta.abs > energy_norm
      best_solution = new_estimates
      best_evaluation = new_evaluation
      number_of_acceptances = number_of_acceptances + 1
      number_of_rejections = 0
    # Accept with a small probability if not improved
    elsif acceptance_probability(evaluation_delta, temperature, bolzmann_constant) > bigdecimal_rand
      best_solution = new_estimates
      best_evaluation = new_evaluation
      number_of_acceptances = number_of_acceptances + 1            
    else
      number_of_rejections = number_of_rejections + 1
    end

  end

  { best_decision_variable_values: best_solution, best_objective_function_value: best_evaluation }

end