A Bi-swarm Particle Swarm Optimizer with novel neighborhood topology strategy (BPSO-NT) is proposed in this paper. The increase of its population diversity helps to improve its global search ability. The strategy of updating neighborhood topology that plays a vital role in the particle swarm optimization algorithm (PSO) is studied by leveraging link prediction techniques. Different learning strategies are utilized to update the velocity of individuals in the two swarms of BPSO-NT. From comparison results with the state-of-the-art PSO variants on ten benchmark functions, the superiority of the proposed algorithm is demonstrated. Furthermore, BPSO-NT is applied to the intermodal transportation planning, and statistical results show that BPSO-NT outperforms other PSO variants in this practical optimization problem.