Metadata-Version: 1.1
Name: metaheuristic_algorithms_python
Version: 0.1.4
Summary: Various metaheuristic algorithms implemented in Python
Home-page: https://github.com/tadatoshi/metaheuristic_algorithms_python
Author: Tadatoshi Takahashi
Author-email: tadatoshi@gmail.com
License: MIT
Description: # MetaheuristicAlgorithmsPython
        
        Various metaheuristic algorithms implemented in Python.
        
        This is equivalent to MetaheuristicAlgorithms written in Ruby (https://github.com/tadatoshi/metaheuristic_algorithms). The reason why I wrote it in Python is that I would like to potentially utilize Python's Scientific Computing libraries. 
        
        As a programming lanugage, I prefer Ruby, because it's fully Object-Oriented programming language (also Dynamic language) and because it has a community with the culture of writing unit tests. Both of these characteristics lead to cleaner, well structured, easy-to-maintain codes. Also it's easier to understand the other people's codes written in such a way. 
        
        But scientists use Python for their activities such as Scientific Computing, Optimization, Data Science, Data Mining, Machine Learning etc. In other words, Python has a community of scientists.  
        
        ## Installation
        
        Use ``pip3``:
        
        ```
        pip3 install metaheuristic_algorithms_python
        ```
        
        ## Supported Platforms
        
        * Python 3.5. 
        
        It's not tested on Python 2.6 or 2.7 yet. 
        
        ## Available Algorithms
        
        * Harmony Search
        
        * Simplified Particle Swarm Optimization
        
        * Simulated Annealing
        
        * Firefly Algorithm
        
        Algorithms under construction:
        
        * Genetic Algorithm
        
        ## Usage
        
        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
        
        ```python
            from metaheuristic_algorithms.function_wrappers.abstract_wrapper import AbstractWrapper
        
            class RosenbrookFunctionWrapper(AbstractWrapper):
        
                def maximum_decision_variable_values(self):
                    return [5, 5]
        
                def minimum_decision_variable_values(self):
                    return [-5, -5]
        
                def objective_function_value(self, decision_variable_values):
                    return (1 - decision_variable_values[0])**2 + 100 * (decision_variable_values[1] - decision_variable_values[0]**2)**2
        
                def initial_decision_variable_value_estimates(self):
                    return [2, 2]
        ```
        
        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
        
        ```python
            from metaheuristic_algorithms.harmony_search import HarmonySearch
            from metaheuristic_algorithms.function_wrappers.rosenbrook_function_wrapper import RosenbrookFunctionWrapper
        
            rosenbrook_function_wrapper = RosenbrookFunctionWrapper()
        
            number_of_variables = 2
            objective = "minimization"
        
            harmony_search = HarmonySearch(rosenbrook_function_wrapper, number_of_variables, objective)
        
            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)
        
            print(result["best_decision_variable_values"][0]) # x value: Example: 1.0112
            print(result["best_decision_variable_values"][1]) # y value: Example: 0.9988
            print(result["best_objective_function_value"])    # f(x,y) value: Example: 0.0563    
        ```
        
        ## Development
        
        
        
        ## Contributing
        
        Bug reports and pull requests are welcome on GitHub at https://github.com/tadatoshi/metaheuristic_algorithms_python. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the [Contributor Covenant](contributor-covenant.org) code of conduct.
        
        
        ## License
        
        The project is available as open source under the terms of the [MIT License](http://opensource.org/licenses/MIT).
        
        ## References
        
        X.-S. Yang, Engineering Optimization : An Introduction with Metaheuristic Applications. Hoboken, New Jersey, United States: John Wiley & Sons, 2010.
        
        
Keywords: control optimization engineering
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
