Traditional Particle Swarm Optimization (PSO) is likely to converge to local optima when applied to multimodal problems, with low search efficiency. In this study, a novel multi-swarm PSO algorithm based on swarm relations and repulsion factors is proposed, called Swarm-Relation-Based PSO (SRB-PSO). Three swarm relations, including dominance, equivalence, and weakness, are defined according to the search results. The search diversity is guaranteed by introducing repulsion factors among equivalent populations and the search efficiency is increased by dominance and weakness relations. Thus, the global search ability of the algorithm is enhanced and the solution quality is improved. The new algorithm and several other versions of PSO are compared on a set of benchmark functions. The results show that the algorithm proposed in this study can well maintain the particle diversity and has outstanding global search ability. The proposed algorithm outperforms the other algorithms when solving multimodal problems.