Modified sine-cosine metaheuristic аlgorithm for multidimensional global optimization problems

The computational model of the sine-cosine metaheuristic algorithm is investigated. A modified algorithm is proposed that includes 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.