We present a new swarm intelligent optimization algorithm called popcorn algorithm. The popcorn algorithm learns from the advantage of the explosion mechanism of the fireworks algorithm and takes advantage of the individual particle’s fitness value in the optimization process to adjust the number of offspring dynamically. The better the individual particle’s fitness value is, the larger of the offspring population, and the more of the offspring searching in the vicinity of the individual particle. The algorithm adjusts the number of offspring dynamically to control the balance between local search and global search. In addition, it uses the memory mechanism of the particle swarm optimization algorithm as reference, and introduces the best individual particle and the best global particle to construct a new explosion radius, so that it can adjust the step size dynamically in the optimization process. The Gaussian perturbation is performed on the best global particle to increase the diversity of the population. Experimental results show that compared with other optimization algorithms such as the bat algorithm, standard particle swarm optimization algorithm and fireworks algorithm, the overall performance of the proposed popcorn algorithm is better.