Golden Sine SABO Integrating Multiple Strategies

In order to solve the problem of the subtraction-average-based optimizer (SABO), which is difficult to effectively balance the local development and global search capability, golden sine SABO integrating multiple strategies (GSABO) is proposed. The improved Sine chaos mapping is introduced to refine the population initialization strategy of SABO in order to enrich the diversity of the population and improve the algorithm's search accuracy and speed. The position update method of SABO is improved by the golden sine algorithm to further enhance the local development and global search capability of SABO. The specific implementation steps of GSABO are described in detail, and the optimization ability of GSABO is tested using 23 benchmark functions. The test results show that the GSABO method is able to solve the optimization problem effectively compared with the current novel optimization algorithms, and has more excellent performance under most benchmark functions.