We propose multi-objective social leaing pigeon-inspired optimization (MSLPIO) and apply it to obstacle avoidance for unmanned aerial vehicle (UAV) formation. In the algorithm, each pigeon leas from the better pigeon but not necessarily the global best one in the update process. A social leaing factor is added to the map and compass operator and the landmark operator. In addition, a dimension-dependent parameter setting method is adopted to improve the blindness of parameter setting. We sim-ulate the flight process of five UAVs in a complex obstacle environment. Results verify the effectiveness of the proposed method. MSLPIO has better convergence performance compared with the improved multi-objective pigeon-inspired optimization and the improved non-dominated sorting genetic algorithm.