关键词: 正弦余弦算法, 转换参数, 全局探索和局部开发, 最优化

Abstract: Sine Cosine Algorithm（SCA） is a novel intelligent optimization algorithm. SCA finds the best solution by the changes in the values of sine and cosine functions. The conversion parameter can balance the global exploration and local exploitation abilities of SCA. It has an important influence on the algorithm. To improve the optimization performance of SCA, the setting of conversion parameter is analyzed firstly and then SCA with parabolic function decreasing conversion parameter and SCA with exponential function decreasing conversion parameter are proposed. The numerical experiments on benchmark functions are performed to compare the presented algorithms and conversion parameter linearly decreasing SCA. The results show that SCA with exponential function decreasing conversion parameter has higher calculation accuracy and faster convergence speed. Finally, the calculation of similarity function for collaborative filtering recommendation algorithm is used to further verify the feasibility and effectiveness of the algorithm.

Key words: sine cosine algorithm, conversion parameter, global exploration and local exploitation, optimization