Sine-Cosine Metaheuristics for Multidimensional Global Optimization Problems

A computational model of the sine-cosine metaheuristic algorithm is investigated. A modified algorithm is proposed, including computational mechanisms to maintain a balance between the convergence rate of the algorithm and the diversification of the solution search space. The effectiveness of the algorithm is analyzed using a series of experiments for the tasks of finding a global minimum in a set of multidimensional test functions. The statistical significance of the obtained results is checked.