https://github.com/prajwal-prathiksh/museum-path-optimization
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.
https://github.com/prajwal-prathiksh/museum-path-optimization
ant-colony-optimization branch-and-bound genetic-algorithm integer-programming museum-guide optimization optimization-algorithms path-optimization path-planning python simulated-annealing travelling-salesman-problem
Last synced: about 2 months ago
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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.
- Host: GitHub
- URL: https://github.com/prajwal-prathiksh/museum-path-optimization
- Owner: Prajwal-Prathiksh
- License: mit
- Created: 2021-03-09T15:46:06.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-04-24T14:19:54.000Z (over 4 years ago)
- Last Synced: 2024-02-27T20:25:47.155Z (over 1 year ago)
- Topics: ant-colony-optimization, branch-and-bound, genetic-algorithm, integer-programming, museum-guide, optimization, optimization-algorithms, path-optimization, path-planning, python, simulated-annealing, travelling-salesman-problem
- Language: Python
- Homepage:
- Size: 26.9 MB
- Stars: 7
- Watchers: 1
- Forks: 8
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Museum-Path-Optimization
**Citation**
[](https://doi.org/10.5281/zenodo.4717673)
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.
---
- [Museum-Path-Optimization](#museum-path-optimization)
- [Group Members](#group-members)
- [Description](#description)
- [Data Input](#data-input)
- [Branch and Bound](#branch-and-bound)
- [Ant Colony Optimization](#ant-colony-optimization)
- [Genetic Algorithm](#genetic-algorithm)
- [Simulated Annealing](#simulated-annealing)
---
## 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](https://www.aero.iitb.ac.in/home/people/faculty/abhijit), 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
```