Gannet Optimization Algorithm has the characteristics of easy to understand and high optimization efficiency, and it is well suited to deal with optimization problems, but the problem of its large memory consumption at each iteration still exists. In order to achieve the goal of saving memory and improving optimization efficiency, this paper proposes a compact Gannet Optimization Algorithm with auxiliary particle improvement (ICGOA). The algorithm uses a Gaussian distribution-based compact strategy for improvement to reduce the memory occupation during iteration and introduces PSO auxiliary particles to improve the optimization efficiency of the algorithm. In order to demonstrate its excellent performance, this paper compares the improved algorithm with several other algorithms that have been improved by the compact strategy on the cec2013 benchmark function, and the results show that the ICGOA has better performance.