https://github.com/agrover112/fliscopt
Algorithms for flight scheduling optimization.
https://github.com/agrover112/fliscopt
genetic-algorithm hacktoberfest hacktoberfest2021 heuristic-search-algorithms optimization-algorithms pypy3 python python3
Last synced: 7 months ago
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Algorithms for flight scheduling optimization.
- Host: GitHub
- URL: https://github.com/agrover112/fliscopt
- Owner: Agrover112
- License: mit
- Created: 2021-03-14T19:56:46.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-07-13T15:58:42.000Z (over 2 years ago)
- Last Synced: 2025-04-20T14:51:51.803Z (7 months ago)
- Topics: genetic-algorithm, hacktoberfest, hacktoberfest2021, heuristic-search-algorithms, optimization-algorithms, pypy3, python, python3
- Language: Python
- Homepage: https://pypi.org/project/fliscopt/
- Size: 70.3 MB
- Stars: 42
- Watchers: 2
- Forks: 12
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
# Fliscopt
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FLIght SCheduling OPTimization 🛫 or *fliscopt* is a simple optimization library for flight scheduling and related problems in the discrete domain. Flight scheduling opt here refers to generating schedules of optimal cost for round-trips.
The library supports plotting, asynchronous multiprocessing, and unimodal optimization benchmarks.
The following repository contains code for the paper "XYZ". The experiments were performed in **PyPy3.7** and **CPython 3.8.10.**
Following algorithms have been implemented and test as of date:
**Algorithms**:
- Hill Climbing
- Random Search
- Simulated Annealing
- Genetic Algorithm
- Genetic Algorithm in Reverse Mode
- Genetic Algorithm with Reversals
- Genetic Algorithm with Random Search as a Reversal/Reverse Process
- Iterated Chaining
Take a look at the [docs](https://gizmotronn.github.io/fliscopt/docs) or the [Appendix](https://github.com/Agrover112/fliscopt/blob/master/Supplementaty%20Material%20and%20Appendix.pdf) for more details.
# Getting Started
Install the library using pip:
```bash
pip install fliscopt
```
Or for unreleased versions:
```bash
pip install git+https://github.com/Agrover112/fliscopt/fliscopt@branchname
```
Or for development:
```bash
git clone https://github.com/Agrover112/fliscopt.git
cd fliscopt
pip install .
```
Download the flights.txt file from the following [link](https://drive.google.com/file/d/1-wxzUMLloeF1tGYEVHvBG_Dh6jfZ-pzR/view) and add it to a data/ directory within your parent directory.
A sample code demonstrating how to use fliscopt:
```python
from fliscopt.utils.util import print_schedule, read_file,plot_scores
from fliscopt.rs import RandomSearch
from fliscopt.ga import GA, ReverseGA, GAReversals, GARSReversals
from fliscopt.hc import HillClimb
from fliscopt.chaining import IteratedChaining
from fliscopt.fitness import fitness_function,domain,griewank
read_file('flights.txt')
sga=GAReversals(seed_init=False,search=False,n_k=250,number_generations=1000)
soln, cost, scores, nfe, seed = sga.run(domain=domain['domain'], fitness_function=fitness_function,seed=5)
plot_scores(scores, sga.get_base(),fname='flight_scheduling', save_fig=False)
sga2=GARSReversals(seed_init=False,search=False,n_k=250,number_generations=1000)
soln, cost, scores, nfe, seed = sga2.run(domain=domain['domain'], fitness_function=fitness_function,seed=5)
plot_scores(scores, sga2.get_base(),fname='flight_scheduling', save_fig=False)
```
This results in the following two plots:
Checkout out the examples in the [examples](https://github.com/Agrover112/fliscopt/tree/master/examples) directory or run in [Google Collab](https://colab.research.google.com/drive/1C9tPvDvauUPxxkL4ItGYP1Azlg6NUBaW?usp=sharing)
## For PyPy users
The instructions for setup are mentioned in the setup directory. Alternatively, you can set up using this bash script. A requirements file is provided just in case.
The script creates and activates a PyPy Conda environment with all libraries and dependencies.
```bash
cd ./setup.sh
source setup.sh
```
Then install using:
```bash
pypy -mpip install fliscopt
```
# Testing
After adding any new algorithm, you can run the tests to check if the code is working properly.
```bash
./run_tests.sh
```
# Results
## Experimental Results
Results were compared by using the same seeds. The following table shows the results of the experiments. Flight scheduling results have been shown below.
| Algorithm | Mean cost | Std.dev | Min cost | Max cost | n.fe. | Time(millsecond) |
|-----------|-----------|---------|----------|----------|-------|------------------|
| SGA | 2780.9 | 205.75 | 2356 | 3081 | 1000 | 9.36 |
| GAwRo | 2629.8 | 213.79 | 2356 | 3004 | 1000 | 9.66 |
| GAwR | 2593 | 183.89 | 2356 | 2973 | 1099 | 10.16 |
| HC | 4177.7 | 817.72 | 2759 | 5839 | 328 | 0.33 |
| RS | 4545.3 | 271.95 | 4143 | 5165 | 100 | 0.17 |
| SA | 3726.5 | 578.16 | 2759 | 4679 | 512 | 0.24 |
| RS+HC | 3050.7 | 399.72 | 2356 | 3771 | 1657 | 2.23 |
| GARSRev | 2592.9 | 168.45 | 2356 | 2888 | 1099 | 9.97 |
## Accessing results
After running the experiments, the results are stored in the results directory. The results are stored in the following format in subdirectories:
```
.
├── multi_proc
│ ├── ackley_N2
│ │ ├── genetic_algorithm_results.csv
│ │ ├── genetic_algorithm_reversed_results.csv
│ │ ├── genetic_algorithm_with_reversals_results.csv
│ │ ├── hill_climb_results.csv
│ │ ├── random_search_results.csv
│ │ └── simulated_annealing_results.csv
│ ├── booth/...
| |
| |
│ └── zakharov
│ ├── genetic_algorithm_results.csv
│ ├── genetic_algorithm_reversed_results
│ ├── genetic_algorithm_with_reversals_results.csv
│ ├── random_search_results.csv
│ └── simulated_annealing_results.csv
├── plots
│ ├── ackley_N2
│ ├── fitness_function
│ │ ├── hill_climb.png
│ │ └── random_search.png
│ ├── flight_scheduling
│ │ ├── simulated_annealing.png
│ │ ├── sol_chaining.png
│ │ └── sol_chaining_a1.png
│ └── griewank
```
# References
Read and Cite the following References for detailed understanding and use of our project.
[1] [Grover, A., Yadav, V., Alicea, B. (2023). Flipping the Switch on Local Exploration: Genetic Algorithms with Reversals. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol 608. Springer, Singapore](https://doi.org/10.1007/978-981-19-9225-4_52)
[2] [Alicea B., Grover A., Lim A. ,Parent J, Unified Theory of Switching. Flash Talk presented at: 4th Neuromatch Conference; December 1 - 2, 2021](https://youtu.be/aTqgPbKQUD8)
# Contributing Guidelines
Refer [Contributing.md](./CONTRIBUTING.md) and Project Board for mode details.
This repository follows [conventional commits](https://www.conventionalcommits.org/en/v1.0.0/)!