Abstract

The focus of this paper is the recently proposed sine–cosine algorithm (Mirjalili, Knowl-Based Syst 96:120–1330, [23]) for nonlinear continuous function optimization. The purpose of this paper is to inspect the effect of the sine–cosine algorithm on solving large-scale optimization problems. For this purpose, the algorithm is implemented on five common scalable problems appearing in literature, namely, Ackley, Griewank, Rastrigin, Rosenbrock, and Sphere functions. The dimensions of these problems are varied from 100 to 1000, and results have been recorded for fixed 10,000 iterations. The results are presented in numerical and graphical form. These results indicate that sine–cosine algorithm is a powerful nature-inspired optimization algorithm for solving all of these problems, except Sphere and Rosenbrock functions. Furthermore, the applicability of this algorithm is demonstrated by solving a real-life problem, i.e., gear train design problem.

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