main.py

import sys
import os
from typing import List, Union, Tuple, Dict
from MMOEASA.mmoeasaSolution import MMOEASASolution
from Ombuki.ombukiSolution import OmbukiSolution
from FIGA.figaSolution import FIGASolution
from problemInstance import ProblemInstance
from data import open_problem_instance
from MMOEASA.mmoeasa import MMOEASA
from Ombuki.ombuki import Ombuki
from evaluation import calculate_area
from FIGA.figa import FIGA

def execute_MMOEASA(problem_instance: ProblemInstance) -> Tuple[List[Union[MMOEASASolution, OmbukiSolution]], Dict[str, int]]:
    from MMOEASA.parameters import POPULATION_SIZE, MULTI_STARTS, TERMINATION_CONDITION_ITERATIONS, TERMINATION_CONDITION_SECONDS, TERMINATION_CONDITION_TYPE, CROSSOVER_PROBABILITY, MUTATION_PROBABILITY, TEMPERATURE_MAX, TEMPERATURE_MIN, TEMPERATURE_STOP
    return MMOEASA(problem_instance, POPULATION_SIZE, MULTI_STARTS, TERMINATION_CONDITION_SECONDS, TERMINATION_CONDITION_TYPE, CROSSOVER_PROBABILITY, MUTATION_PROBABILITY, TEMPERATURE_MAX, TEMPERATURE_MIN, TEMPERATURE_STOP)

def execute_Ombuki(problem_instance: ProblemInstance, use_original: bool) -> Tuple[List[Union[OmbukiSolution, MMOEASASolution]], Dict[str, int]]:
    from Ombuki.parameters import POPULATION_SIZE, TERMINATION_CONDITION_ITERATIONS, TERMINATION_CONDITION_SECONDS, TERMINATION_CONDITION_TYPE, CROSSOVER_PROBABILITY, MUTATION_PROBABILITY
    return Ombuki(problem_instance, POPULATION_SIZE, TERMINATION_CONDITION_SECONDS, TERMINATION_CONDITION_TYPE, CROSSOVER_PROBABILITY, MUTATION_PROBABILITY, use_original)

def execute_FIGA(problem_instance: ProblemInstance) -> Tuple[List[FIGASolution], Dict[str, int]]:
    from FIGA.parameters import POPULATION_SIZE, TERMINATION_CONDITION_ITERATIONS, TERMINATION_CONDITION_SECONDS, TERMINATION_CONDITION_TYPE, CROSSOVER_PROBABILITY, MUTATION_PROBABILITY
    return FIGA(problem_instance, POPULATION_SIZE, TERMINATION_CONDITION_SECONDS, TERMINATION_CONDITION_TYPE, CROSSOVER_PROBABILITY, MUTATION_PROBABILITY)

if __name__ == '__main__':
    argc = len(sys.argv)
    if not 2 <= argc <= 4 or (argc == 2 and sys.argv[1] not in {"--help", "-h"}):
        print("If you're unsure how to use the application, give the argument -h (--help) for information")
    elif sys.argv[1] in {"--help", "-h"}: # if the user gave one of these arguments on the command line then a help message is outputted
        if argc == 2:
            print(f"Format: main(.py) [ algorithm ] [ problem instance ] [ acceptance criteria ]{os.linesep}{os.linesep}"
                  f"The algorithms and acceptance criteria available to solve the problem are:{os.linesep}"
                  f" - MMOEASA{os.linesep}"
                  f" - Ombuki (note that \"Ombuki-Original\" is also an algorithm; it contains features that seem to be anomalous from the original research paper){os.linesep}"
                  f" - FIGA (algorithm \"FIGA\" should not be used as and does not accept an alternative acceptance criterion; see example commands){os.linesep}{os.linesep}"
                  f"There's multiple types of problems in Solomon's instances, and here's what they are:{os.linesep}"
                  f" - Number of customers:{os.linesep}"
                  f"   - 100 - 100 customers{os.linesep}"
                  f"   - Archived (and, therefore, out of order):{os.linesep}"
                  f"     - 25 - 25 customers{os.linesep}"
                  f"     - 50 - 50 customers{os.linesep}"
                  f" - Customers' location:{os.linesep}"
                  f"   - C - clustered customers{os.linesep}"
                  f"   - R - uniformly distributed customers{os.linesep}"
                  f"   - RC - a mix of R and C{os.linesep}"
                  f" - Width of deliveries' time windows:{os.linesep}"
                  f"   - 1 - destinations with narrow time windows{os.linesep}"
                  f"   - 2 - destinations with wide time windows{os.linesep}{os.linesep}"
                  f"To execute a problem set, please enter a problem's filename. The details required, and the argument format, are:{os.linesep}"
                  f" - solomon_[ number of customers ]/[ customers' location ][ width of time windows ]XX.txt{os.linesep}"
                  f" - Where XX is the instance number; see the folder \"solomon_[ number of customers ]\" for available instances{os.linesep}{os.linesep}"
                  f"Example commands are:{os.linesep}"
                  f" - \"main.py MMOEASA solomon_100/C101.txt MMOEASA\"{os.linesep}"
                  f" - \"main.py FIGA solomon_100/C101.txt\"{os.linesep}"
                  f" - \"main.py Ombuki solomon_100/C101.txt MMOEASA\"")
        else:
            print("Argument \"-h\"/\"--help\" does not take any arguments")
    else:
        if sys.argv[1].upper() == "FIGA":
            if argc == 3:
                sys.argv.append("Ombuki")
            else:
                exc = ValueError("FIGA should not be given a pre-determined acceptance criterion; it only has one")
                raise exc

        if len(sys.argv) != 4 or sys.argv[3].upper() not in {"MMOEASA", "OMBUKI"}:
            exc = ValueError(f"Acceptance criterion \"{'' if len(sys.argv) != 4 else sys.argv[3]}\" was not recognised")
            raise exc
        problem_instance = open_problem_instance(sys.argv[2], sys.argv[3])

        nondominated_set, statistics = None, None
        if sys.argv[1].upper() == "MMOEASA":
            nondominated_set, statistics = execute_MMOEASA(problem_instance)
        elif sys.argv[1].upper() == "OMBUKI-ORIGINAL":
            nondominated_set, statistics = execute_Ombuki(problem_instance, True)
        elif sys.argv[1].upper() == "OMBUKI":
            nondominated_set, statistics = execute_Ombuki(problem_instance, False)
        elif sys.argv[1].upper() == "FIGA":
            nondominated_set, statistics = execute_FIGA(problem_instance)
        else:
            exc = ValueError(f"Algorithm \"{sys.argv[1]}\" was not recognised")
            raise exc

        """# uncomment this code if you'd like a solution to be written to a CSV
        # solutions can be plotted on a scatter graph in Excel as the x and y coordinates of each vehicle's destinations are outputted and in the order that they are serviced
        if nondominated_set:
            write_solution_for_graph(nondominated_set[0])"""

        for solution in nondominated_set:
            print(f"{os.linesep + str(solution)}")
        print(f"{os.linesep}Algorithm \"{sys.argv[1]}'s\" statistics:")
        for statistic, value in statistics.items():
            print(f" - {statistic}: {value}")
        print(f"{os.linesep + str(problem_instance)}")
        calculate_area(problem_instance, nondominated_set, sys.argv[3])