Results in Control and Optimization 7 (2022) 100127
Contents lists available at ScienceDirect
Results in Control and Optimization
journal homepage: www.elsevier.com/locate/rico
Transit search: An optimization algorithm based on exoplanet
exploration
Masoomeh Mirrashid
1,
∗
, Hosein Naderpour
2
Faculty of Civil Engineering, Semnan University, Iran
A R T I C L E I N F O
Keywords:
Transit search
Optimization
Meta-heuristic
Astrophysics
Exoplanet exploration
A B S T R A C T
In this article, a novel astrophysics-inspired meta-heuristic optimization algorithm, namely
Transit Search (TS) is proposed based on a famous exoplanet exploration method. More than
3800 planets have been detected using transit technique by the database of the space telescopes.
Transit is a method that has shown more potential than the second well-known successful
method (radial velocity) with 915 discovered planets until 2022 March. It is difficult to detect
the planets because of their small dimension in the cosmos scale. Due to the high efficiency
of the transit method in astrophysics and its capabilities, it has been used to formulate an
optimization technique for this research. 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
1. Introduction
Optimization means finding the best global response for an objective function (maximum or minimum value of the function)
in the search space. There are two main approaches to do this: classical or meta-heuristic methods. The first group, although they
can guarantee the optimal response, lose their efficiency in complex and large-scale problems. In some cases, using the classical
methods to determine the optimal response can take hundreds of years. Therefore, the second approach, which includes a set of
meta-heuristic methods, has been considered by researchers. Although these techniques do not guarantee finding the best response,
they can approximate the optimal and acceptable answers in a suitable time. Optimization is one of the most important issues
that has many applications in various sciences. For example, their application in the industrial internet of things [1], parameter
extraction of electrolyte fuel cell stacks [2], traffic light control [3], wireless networks [4], feature selection [5], cloud computing
environments [6], radial distribution network in the presence of plug-in electric vehicles [7], vulnerability assessment of RC frames
subject to seismic sequences [8], shape optimization of rotating electric machines [9], diagnostic accuracy transformer faults [10],
∗
Corresponding author.
1
Postdoctoral Research Fellow
2
Professor
https://doi.org/10.1016/j.rico.2022.100127
Received 13 January 2022; Received in revised form 29 March 2022; Accepted 9 April 2022
Available online 18 April 2022
2666-7207/© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
评论0