Transit Search Optimization Algorithm:

This repository contains Python code implementing an optimization algorithm, Transit Search (TS), a cutting-edge optimization algorithm that draws inspiration from the remarkable method of exoplanet detection known as transit. The TS presents a novel astrophysics-inspired meta-heuristic approach to solving complex scientific problems. With more than 3800 planets detected using the transit technique by space telescopes, this algorithm harnesses the power of transit exploration and adapts it to the realm of optimization.

Structure:

Main File: The primary script that executes the optimization algorithm. It calls the Cost_Function and TransitSearchmodules.
Cost_Function: Defines the cost function to be minimized during the optimization process. This file includes detailed documentation of the cost function, its parameters, and any relevant assumptions.
TransitSearch: The core of the optimization algorithm, implementing the search strategy inspired by the TS algorithm. This module is called by the Main File.

Definition of the considered cost function:

Name: Branin
Best Cost: 0.397887
Best Solution: (-pi,12.275); (pi,2.275); (9.42478,2.475)
Dimension: 2

Citation:

Mirrashid, Masoomeh, and Hosein Naderpour. "Transit search: An optimization algorithm based on exoplanet exploration." Results in Control and Optimization 7 (2022): 100127. https://doi.org/10.1016/j.rico.2022.100127

Requirements:

Python (version 3.8.19)
List of required Python libraries: NumPy, Math, Matplotlib

Contributions:

Contributions are welcome! Please feel free to fork this repository and submit pull requests.