Particle swarm optimizer(PSO) may easily get trapped in a local optimum, when it comes to solving complex
multimodal problems. Therefore, this paper presents dynamic multi-swarm particle swarm optimizer based on K-means
clustering(KDMSPSO). In KDMSPSO, the population is divided into several sub-swarms by using K-means clustering. In
order to increase the message exchange of sub-swarms, the sub-swarm is dynamically constructed, and the velocity of each
particle is adjusted by clustering center that it belongs to and all particles in its neighborhood including itself. In benchmark
function and actual application, the experimental results show that the KDMSPSO algorithm can achieve better solutions
than other PSO algorithms.