Dimension by dimension dynamic sine cosine algorithm for global optimization problems

Abstract To solve global optimization problems, this paper proposed a novel improved version of sine cosine algorithm — the dimension by dimension dynamic sine cosine algorithm (DDSCA). In the update equation of sine cosine algorithm (SCA), the dimension by dimension strategy evaluates the solutions in each dimension, and the greedy strategy is used to form new solutions after combined them with other dimensions. Moreover, in order to balance the exploration and exploitation of SCA, a dynamic control parameter is designed to modify the position equation of this algorithm. To evaluate the effectiveness of DDSCA in solving global optimization problems, it is compared with state-of-art algorithms and modified SCA on 23 benchmark functions. The experimental results reveal the DDSCA has better robustness and efficiency. The IEEE CEC2010 large-scale functions are selected to solve high-dimensional optimization problem, the results show that the performance of the DDSCA is better than other algorithms. In addition, five engineering optimization problems are also verified the effectiveness of the DDSCA. The results of accuracy and speed show that the improved sine cosine algorithm (DDSCA) is competitive in solving global optimization problems.

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