Improved Hyperbolic Sine and Cosine Optimizer that Fuses Golden Sine Algorithm and Tent Chaos Mapping

In view of the problems of the Sinh Cosh Optimizer (SCHO), such as uneven initial position, slow convergence and poor search accuracy, This paper presents an improved SCHO that combines the Golden Sine Algorithm(Gold-SA) and the Tent chaotic mapping (GTSCHO). First, the Tent chaotic mapping was used to replace the traditional random number initialization of the population, which improved the diversity of the population. Then, the Golden sine algorithm is added to the position updating process, which makes the algorithm's search and development have a better balance, and the optimization accuracy is improved. Through 13 benchmark test functions, the simulation and comparison test results show that: Compared with SCHO, the improved GTSCHO has better convergence speed, searching ability and stability.