{"id":23074640,"url":"https://github.com/prajwal-prathiksh/museum-path-optimization","last_synced_at":"2025-08-15T16:31:54.604Z","repository":{"id":52591086,"uuid":"346058368","full_name":"Prajwal-Prathiksh/Museum-Path-Optimization","owner":"Prajwal-Prathiksh","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. 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Zenodo, April 24, 2021. doi:10.5281/zenodo.4717673.\n\n---\n\n\n- [Museum-Path-Optimization](#museum-path-optimization)\n  - [Group Members](#group-members)\n  - [Description](#description)\n    - [Data Input](#data-input)\n    - [Branch and Bound](#branch-and-bound)\n    - [Ant Colony Optimization](#ant-colony-optimization)\n    - [Genetic Algorithm](#genetic-algorithm)\n    - [Simulated Annealing](#simulated-annealing)\n\n\n---\n\n## Group Members\n*In alphabetical order:*\n\n`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`\n\n## Description\nContains 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. \n\nThis repository represents the work done as part of the course project for AE - 755: Optimization for Engineering Design *(Spring 2020)*, [Prof. Abhijit Gogulapati](https://www.aero.iitb.ac.in/home/people/faculty/abhijit), Indian Institute of Technology Bombay.\n\nInstructions on running specific algorithms are mentioned below:\n\n*Note: All of the commands mentioned below support CLI. Use the argument `-h` for help in each case.*\n\n### Data Input\n`Author: Apurva Kulkarni`\n\nTo generate and store the cost matrices of all the test cases, do the following from root:\n```\n$ python code/data_input/base_input.py\n```\n\n### Branch and Bound\n`Author: Patel Joy Pravin Kumar, Nakul Randad, Umang Goel`\n\nTo run the branch and bound algorithm, do the following from root:\n```\n$ python code/branch_and_bound/time_opti.py\n```\nRun the following to get all the command-line arguments:\n```\n$ python code/branch_and_bound/time_opti.py -h\n```\n\n### Ant Colony Optimization\n`Author: Arsh Khan, Harshal Kataria`\n\nTo run the ant colony optimization algorithm, do the following from root:\n```\n$ python code\\ant_colony\\ant_colony_code.py\n```\n\n### Genetic Algorithm\n`Author: Apurva Kulkarni, Mridul Agarwal`\n\n**Simple Algorithm**\nTo run the simple genetic algorithm, do the following from root:\n```\n$ python code\\genetic\\genetic_p1_2.py\n```\n\n**Complex Algorithm**\n\nTo run the complex genetic algorithm, do the following from root:\n```\n$ python code\\genetic\\genetic_p3.py\n```\n\n### Simulated Annealing\n`Author: K T Prajwal Prathiksh, Miloni Atal`\n\n**Simple Algorithm**\n\nTo run the simple simulated annealing algorithm, do the following from root:\n```\n$ python code/simulated_annealing/simple_simulated_annealing.py\n```\n\n**Complex Algorithm**\n\nTo run the complex simulated annealing algorithm, do the following from root:\n```\n$ python code/simulated_annealing/complex_simulated_annealing.py\n```\n\n**Automator**\n\nTo run the automator file, do the following from root:\n```\n$ python code\\simulated_annealing\\automate.py\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprajwal-prathiksh%2Fmuseum-path-optimization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprajwal-prathiksh%2Fmuseum-path-optimization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprajwal-prathiksh%2Fmuseum-path-optimization/lists"}