Delivery With Drones

Execution and Results

Notebook

To get a full study on the problem, one can check the delivery.ipynb notebook.

Manual Configuration

Tu run the program with a desired configuration, it receives a JSON input file (for example python main.py genetic.json).

Required JSON Values

• input: the input file, required
• output: the output file, required

Genetic

{
    "input_file": "data/fouroo.in",
    "output_file": "test1.out",
    "algorithm": {
        "name": "genetic",
        "time": "20",
        "iterations": "2",
        "max_improveless_iterations": "30",
        "selection_method": "tournament",
        "tournament_size": "10",
        "crossover": "order",
        "population_size" : "30",
        "generational": "True",
        "mutation_probability": "0.2",
        "log": "True",
        "save_results": "False"
    }
}

• algorithm: required
• name: the algorithm, required, should be genetic
• time: the max time the algorithm can take in seconds
• iterations: the max possible number of iterations
• max_improveless_iterations: the max number of improveless iterations, default 20
• selection_method: the selection method, default is tournament, should be in {tournament, roullete}
• tournament_size: the size of the tournament (only in tournament selections)
• crossover: the crossover, default ir order, should be in {order, one_point}
• population_size : the size of the population, default is 30
• generational: if the algorithm runs through generations or iterations
• mutation_probability: the probability of mutations, default is 0.2
• log: if displays results in real time, default is true
• save_results: if saves the results to a file, default is false

Hill Climbing

{
    "input_file": "data/custom.in",
    "output_file": "test2.out",
    "algorithm": {
        "name": "hill_climbing",
        "max_iterations": "1000",
        "iteration_search": "20",
        "save_results": "True"
    }
}

• algorithm: required
• max_iterations: the max possible number of iterations
• iteration_search: the number of neighbours generated per iteration

Simulated Annealing

{
    "input_file": "data/custom.in",
    "output_file": "test3.out",
    "algorithm": {
        "name": "simulated_annealing",
        "max_iterations": "1000",
        "iteration_search": "20",
        "initial_temperature": "1000",
        "temperature_schedule": "0.9",
        "save_results": "True"
    }
}

• algorithm: required
• max_iterations: the max possible number of iterations
• iteration_search: the number of neighbours generated per iteration
• temperature_schedule: the time schedule, the factor that will define the rate of decreasing in temperature
• initial_temperature: the initial temperature

The genetic.json, hill_climbing.json and simulated_annealing.json are given in this repository.

Requirements

• Python 3.9 or higher
• Pandas (for Jupyter Notebook only)
• matplotlib (for Jupyter Notebook only)
• numpy (for Jupyter Notebook only)

Disclaimer

This repository contains the first project solved during the course unit. Note that it might contain errors and should not be used as a solution.

License

MIT