A comparative study of several metaheuristic algorithms for optimization problems
Reddad Hakima, Maria Zemzami, El Hami Norelislam, Nabil Hmina
Abstract
This article presents a study of a recent metaheuristic optimization method, the search and rescue algorithm (SAR), against four known metaheuristic optimization algorithms, the Salp Swarm Algorithm (SSA), the Cuckoo Search Algorithm (CSA), the Firefly Algorithm (FA), and the Grey Wolf Optimization Algorithm (GWO). An evaluation of its performance against the other algorithms will be performed by the means of thirteen mathematical benchmarks functions, afterwards a study of the optimization of five multi-dimensional mathematical problems will be investigated, the optimization of the Dejoung function, the Cosine Mixture function, the Griewank function, the Rastrigin function, and the Rosenbrok function, while the dimension of these problems increases from five to thirty. Furthermore, a discussion and a conclusion about the results obtained by each algorithm face to the resolution of these complex multi-dimensional problems will be drawn.