Enhancing Sparrow Search Algorithm with Hybrid Strategy

This paper proposes a hybrid strategy-based improved sparrow search algorithm (HISSA). Tent chaotic mapping is used in the initialization of sparrow population to make the distribution of sparrow population more uniform in the solution space and increase the diversity of sparrow population. An adaptive parameter strategy is proposed to adjust the proportion of producers, danger perceivers, and environmental security thresholds, effectively balancing the global search and local development of the algorithm. Then, in the producer position update stage, the exploration strategy of the golf optimization algorithm is integrated to improve the optimization accuracy and speed of the algorithm. In addition, a boundary contraction mechanism is introduced to allocate individuals beyond the boundary to an effective local range, avoiding the aggregation of boundary sparrow individuals and increasing the diversity of sparrow populations during the search process. Finally, simulation experiments are conducted on benchmarks. Compared with other algorithms, the HISSA has good accuracy and convergence performance.