A Survey On (Stochastic Fractal Search) Algorithm
It addresses optimization challenges in engineering design, but appears incremental as it builds on existing evolutionary algorithm frameworks.
This paper introduces Stochastic Fractal Search, a metaheuristic algorithm inspired by fractals for optimization problems, and demonstrates its application to engineering design optimization examples.
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.