A Modified Sine Cosine Algorithm for Solving Optimization Problems

Abstract

The sine cosine algorithm (SCA) is a newly emerging optimization algorithm. It is easy for sine cosine algorithm (SCA) to sink into premature of the algorithm and obtain a slower convergence rate when solving the complicated optimization problems, especially highly ill-posed problems. A novel modified sine cosine algorithm (MSCA) is put forward for solving the optimization problems. To our limited knowledge, the linear searching path and empirical parameter have not been applied to the related improved sine cosine algorithm. The proposed MSCA improves the search path of original SCA by introducing linear searching path and empirical parameter, effectively avoiding sinking into the local optimal. In addition, the proposed algorithm changes the definition of convergence factor. Two kinds of tests, including 23 benchmark functions test and actual engineering problem tests are adopted to prove the performance of the MSCA. In addition, the performance of the proposed MSCA is compared with SCA by using benchmark functions on different dimensional (D = 30, 50,100 and 500). As expected, the result of comparisons show that the proposed MSCA can better avoid the local optima than both the SCA and other population-based algorithms. And MSCA can obtain the faster convergence than SCA on different dimensions.

Keywords

• Sine cosine algorithm
• linear searching path
• nature-inspired algorithm
• optimization