MetaheuristicAlgorithms

Various metaheuristic algorithms implemented in Ruby.

Add this line to your application's Gemfile:

gem 'metaheuristic_algorithms'

And then execute:

$ bundle

Or install it yourself as:

$ gem install metaheuristic_algorithms

• MRI Ruby 2.2 or above.

• JRuby 9.0.0.0 or above. (In an attempt to have better speed, if not fast.)

In alphabetical order:

• Firefly Algorithm MetaheuristicAlgorithms::FireflyAlgorithm
• Genetic Algorithm MetaheuristicAlgorithms::GeneticAlgorithm
• Harmony Search MetaheuristicAlgorithms::HarmonySearch
• Simplified Particle Swarm Optimization MetaheuristicAlgorithms::SimplifiedParticleSwarmOptimization
• Simulated Annealing MetaheuristicAlgorithms::SimulatedAnnealing

Algorithms under construction:

• Ant Colony Optimization
• Virtual Bee Algorithm

Step 1. Create a Function Wrapper for your objective function by extending MetaheuristicAlgorithms::FunctionWrappers::AbstractWrapper

Example: Rosenbrook's Function: f(x,y) = (1 - x)^2 + 100(y - x^2)^2

   require 'metaheuristic_algorithms'

   class RosenbrookFunctionWrapper < MetaheuristicAlgorithms::FunctionWrappers::AbstractWrapper

      def maximum_decision_variable_values
        [5, 5]
      end

      def minimum_decision_variable_values
        [-5, -5]
      end

      def objective_function_value(decision_variable_values)
        (1 - decision_variable_values[0])**2 + 100 * (decision_variable_values[1] - decision_variable_values[0]**2)**2
      end

      # For the algorithm that requires initial estimate that is depending on the particular objective function:
      def initial_decision_variable_value_estimates
        [2, 2]
      end

    end

Step 2. Instantiate the created Function Wrapper and pass it as the first argument of the Algorithm instantiation. Also specify the number of variables and objective (:maximization or :minimization) Then call the search method passing the algorithm specific parameters.

Example: Harmony Search for the glocal minimum value for Rosenbrook's Function

   require 'metaheuristic_algorithms'

   rosenbrook_function_wrapper = RosenbrookFunctionWrapper.new

   harmony_search = MetaheuristicAlgorithms::HarmonySearch.new(rosenbrook_function_wrapper, number_of_variables: 2, objective: :minimization)

   maximum_attempt = 25000
   pitch_adjusting_range = 100
   harmony_search_size = 20
   harmony_memory_acceping_rate = 0.95
   pitch_adjusting_rate = 0.7    

   result = harmony_search.search(maximum_attempt: maximum_attempt, pitch_adjusting_range: pitch_adjusting_range, 
                                  harmony_search_size: harmony_search_size, harmony_memory_acceping_rate: harmony_memory_acceping_rate, 
                                  pitch_adjusting_rate: pitch_adjusting_rate)

   puts result[:best_decision_variable_values][0] # x value: Example: 1.0112
   puts result[:best_decision_variable_values][1] # y value: Example: 0.9988
   puts result[:best_objective_function_value]    # f(x,y) value: Example: 0.0563    

After checking out the repo, run bin/setup to install dependencies. Then, run rake rspec to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.

To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.

Bug reports and pull requests are welcome on GitHub at https://github.com/tadatoshi/metaheuristic_algorithms. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.

The gem is available as open source under the terms of the MIT License.