OptAlgFramework

Opt-Alg-Framework is a framework to work and build optimization algorithm. The basic idea is: you will have a codificated problem with a method to calculate its fitnes, an algorithm to improve the this fitness and operators used with the algorithm to build new solutions.

What is already implemented:

  • Problems:

    • Flow Shop Permutation

  • Algorithms:

    • Local Searches:
      • Hill-Climbing
      • Simulated Annealing
      • Tabu Search

  • Operators

    • Crossover:
      • Two Point Crossover (permutational)
    • Selector:
      • Tournament Selection
    • Tweak:
      • Random Swap

Installation

Add this line to your application's Gemfile:

gem 'opt_alg_framework'

And then execute:

$ bundle

Or install it yourself as:

$ gem install opt_alg_framework

Usage

Example: read two instances of FSP problem (about the instances format, read README in problem directory!) and get its best results with Simulated Annealing algorithm, using RandomSwap operator.

operator = Operator::Tweak::RandomSwap.new
problem = Problem::FSP.new
problem.load_schedule(path)
algorithm = Algorithm::LocalSearch::SimulatedAnnealing.new max_iterations: 10,
                                                           cooling_rate: 0.009,
                                                           problem: problem,
                                                           tweak_operator: operator

puts algorithm.start[:fitness]

Development

After checking out the repo, run bin/setup to install dependencies. Then, run bin/console or, alternatively, bundle 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 to create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.

Some conventions must be followed:

  • All problem classes need to have its fitness method acessible with the name fitness;
  • All swap operator classes need to have its swap method acessible with the name tweak;
  • All crossover operator classes need to have its crossover method named cross;
  • All selector operator classes need to have its selection method names select;
  • All algorithm classes need to have its main method names start;
  • In the algorithms, a solution is represent with a hash structure, and it's mandatory have at least the pair key-value :solution with the representation of the solution and :fitness with the fitness of the solution.

TODO:

  • Treatments to verify if the conventions are being followed;
  • General implementations, like:
    • Local search algorithms;
    • Population based algorithms;
    • Operators (crossover, swap, selector) to any type of problem (permutational, binary, etc);
    • Different type of problems.

Contributing

  1. Fork it ( https://github.com/[my-github-username]/opt_alg_framework/fork )
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Commit your changes (git commit -am 'Add some feature')
  4. Push to the branch (git push origin my-new-feature)
  5. Create a new Pull Request