Museum-Path-Optimization

Citation

K T Prajwal Prathiksh, Apurva Kulkarni, Arsh Khan, Harshal Kataria, Miloni Atal, Mridul Agarwal, Patel Joy Pravin Kumar, Nakul Randad, Souvik Kumar Dolui, and Umang Goel. “The Museum Optimization Problem”. Zenodo, April 24, 2021. doi:10.5281/zenodo.4717673.

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• Museum-Path-Optimization
  • Group Members
  • Description
    • Data Input
    • Branch and Bound
    • Ant Colony Optimization
    • Genetic Algorithm
    • Simulated Annealing

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Group Members

In alphabetical order:

Apurva Kulkarni, Arsh Khan, Harshal Kataria, K T Prajwal Prathiksh, Miloni Atal, Mridul Agarwal, Patel Joy Pravin Kumar, Nakul Randad, Souvik Kumar Dolui, Umang Goel

Description

Contains code meant to optimize the route for a tourist visiting the Louvre Museum, such that the satisfaction level is maximised by visiting all/select exhibits in a single working day.

This repository represents the work done as part of the course project for AE - 755: Optimization for Engineering Design (Spring 2020), Prof. Abhijit Gogulapati, Indian Institute of Technology Bombay.

Instructions on running specific algorithms are mentioned below:

Note: All of the commands mentioned below support CLI. Use the argument -h for help in each case.

Data Input

Author: Apurva Kulkarni

To generate and store the cost matrices of all the test cases, do the following from root:

$ python code/data_input/base_input.py

Branch and Bound

Author: Patel Joy Pravin Kumar, Nakul Randad, Umang Goel

To run the branch and bound algorithm, do the following from root:

$ python code/branch_and_bound/time_opti.py

Run the following to get all the command-line arguments:

$ python code/branch_and_bound/time_opti.py -h

Ant Colony Optimization

Author: Arsh Khan, Harshal Kataria

To run the ant colony optimization algorithm, do the following from root:

$ python code\ant_colony\ant_colony_code.py

Genetic Algorithm

Author: Apurva Kulkarni, Mridul Agarwal

Simple Algorithm To run the simple genetic algorithm, do the following from root:

$ python code\genetic\genetic_p1_2.py

Complex Algorithm

To run the complex genetic algorithm, do the following from root:

$ python code\genetic\genetic_p3.py

Simulated Annealing

Author: K T Prajwal Prathiksh, Miloni Atal

Simple Algorithm

To run the simple simulated annealing algorithm, do the following from root:

$ python code/simulated_annealing/simple_simulated_annealing.py

Complex Algorithm

To run the complex simulated annealing algorithm, do the following from root:

$ python code/simulated_annealing/complex_simulated_annealing.py

Automator

To run the automator file, do the following from root:

$ python code\simulated_annealing\automate.py