Abstract: A new optimized algorithm was proposed based on the interval-particle swarm combination algorithm, which improved the low efficiency and difficulty in constructing acceleration tools of traditional interval algorithm and made the interval algorithm better to be used in high-dimensional model. The interval theory was used in the improved algorithm to guide the production of new particles, and the random search capability of particle swarm optimization was used to improve interval center position. With the increase of the iterative step, the variable intervals were continuously reduced and the optimal interval could be eventually obtained. Simulations were carried out for the high-dimensional and multi-peak global optimization. The results showed that the improved algorithm was more efficient than the traditional interval algorithm.