An Improved Sparrow Search Algorithm for Global Optimization with Customization-Based Mechanism

To solve the problems of the original sparrow search algorithm's poor ability to jump out of local extremes and its insufficient ability to achieve global optimization, this paper simulates the different learning forms of students in each ranking segment in the class and proposes a customized learning method (CLSSA) based on multi-role thinking. Firstly, cube chaos mapping is introduced in the initialization stage to increase the inherent randomness and rationality of the distribution. Then, an improved spiral predation mechanism is proposed for acquiring better exploitation. Moreover, a customized learning strategy is designed after the follower phase to balance exploration and exploitation. A boundary processing mechanism based on the full utilization of important location information is used to improve the rationality of boundary processing. The CLSSA is tested on 21 benchmark optimization problems, and its robustness is verified on 12 high-dimensional functions. In addition, comprehensive search capability is further proven on the CEC2017 test functions, and an intuitive ranking is given by Friedman's statistical results. Finally, three benchmark engineering optimization problems are utilized to verify the effectiveness of the CLSSA in solving practical problems. The comparative analysis shows that the CLSSA can significantly improve the quality of the solution and can be considered an excellent SSA variant.