  Evolutionary Algorithms are naturally inspired approximation optimisation
algorithms that usually interfere with science problems when common
mathematical methods are unable to provide a good solution or finding the exact
solution requires an unreasonable amount of time using traditional exhaustive
search algorithms. The success of these population-based frameworks is mainly
due to their flexibility and ease of adaptation to the most different and
complex optimisation problems. This paper presents a metaheuristic algorithm
called Stochastic Fractal Search, inspired by the natural phenomenon of growth
based on a mathematical concept called the fractal, which is shown to be able
to explore the search space more efficiently. This paper also focuses on the
algorithm steps and some example applications of engineering design
optimisation problems commonly used in the literature being applied to the
proposed algorithm.
