Transit Search Optimization Algorithm (2022): MATLAB Codes

Version 1.1.1 (45.6 MB) by M Mirrashid
The codes of a novel astrophysics-inspired meta-heuristic optimization algorithm, namely Transit Search (TS)


Updated 14 Sep 2022

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These codes are extracted from a novel astrophysics-inspired meta-heuristic optimization algorithm, which is based on a famous exoplanet exploration method, namely Transit Search (TS). The algorithm has been published in 2022.
In the transit algorithm, by studying the light received from the stars at certain intervals, the changes in luminosity are examined and if a decrease in the amount of the received light is observed, it indicates that a planet passes from the star front.
In order to evaluate the capability of the proposed algorithm, 73 constrained and unconstrained problems are considered and the results have been compared with 13 well- known optimization algorithms. This set of examples includes a wide range of types of problems including mathematical functions (28 high-dimensional and 15 low-dimensional problems), CEC functions (10 problems), constrained mathematical benchmark problems (G01–G13), as well as 7 constrained engineering problems.
The results indicated that the overall average error for the proposed algorithm is the lowest amount for the benchmark problems in comparison with the other efficient algorithms

Cite As

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

MATLAB Release Compatibility
Created with R2022a
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Transit Search Optimization Codes/TS Code for Constrained Problems

Transit Search Optimization Codes/TS Code for Unconstrained Problems

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