  This article proposes a new population-based optimization algorithm called
the Tangent Search Algorithm (TSA) to solve optimization problems. The TSA uses
a mathematical model based on the tangent function to move a given solution
toward a better solution. The tangent flight function has the advantage to
balance between the exploitation and the exploration search. Moreover, a novel
escape procedure is used to avoid to be trapped in local minima. Besides, an
adaptive variable step size is also integrated in this algorithm to enhance the
convergence capacity. The performance of TSA is assessed in three classes of
tests: classical tests, CEC benchmarks, and engineering optimization problems.
Moreover, several studies and metrics have been used to observe the behavior of
the proposed TSA. The experimental results show that TSA algorithm is capable
to provide very promising and competitive results on most benchmark functions
thanks to better balance between exploration and exploitation of the search
space. The main characteristics of this new optimization algorithm is its
simplicity and efficiency and it requires only a small number of user-defined
parameters.
