This paper considers a formation control problem for a group of unmanned aerial vehicles (UAVs) employing consensus with different optimizers. UAVs can never accomplish difficult tasks without formation because disordered UAVs don’t manage to work better than a single UAV, while a single one is limited for its undeveloped intelligence and insufficient load. Among many formation methods, consensus has attracted much attention for its effectiveness and simplicity, however, at the beginning of the convergence, overshoot and oscillation are universal because of limitation of communication and lack of forecast, which are inborn shortcoming of consensus. It is a natural mind to modify this method with lots of optimizers. This paper first adopts particle swarm optimization (PSO), pigeon-inspired optimization (PIO) to modify consensus aiming at reducing overshoot and smoothing trajectories. PSO is a very popular optimizer, while PIO is a new method, both of them work but still reserve disadvantages such as remaining oscillation. As a result, it is necessary to modify PIO and a pigeon-inspired optimization with slow diving strategy (SD-PIO) is proposed. Convergence analysis is done for SD-PIO based on Banach fixed-point theorem and a sufficient condition for stability is purposed. Finally, a series of comparative simulations are conducted for verifying the feasibility and effectiveness of the proposed approach. This paper considers a formation control problem for a group of unmanned aerial vehicles (UAVs) employing consensus with different optimizers. UAVs can not accomplish difficult tasks without formation because disordered UAVs do not manage to work better than a single UAV, while a single one is limited for its undeveloped intelligence and insufficient load. Among many formation methods, consensus has attracted much attention for its effectiveness and simplicity, however, at the beginning of the convergence, overshoot and oscillation are universal because of limitation of communication and lack of forecast, It is a natural mind to modify this method with lots of optimizers. This paper first adoptive particle swarm optimization (PSO), pigeon-inspired optimization (PIO) to modify consensus aiming at reducing overshoot and smoothing trajectories. PSO is a very popular optimizer, while PIO is a new method, both of them work but still may disadv As a result, it is necessary to modify PIO and a pigeon-inspired optimization with slow diving strategy (SD-PIO) is proposed. Convergence analysis is done for SD-PIO based on Banach fixed-point theorem and Finally, a series of comparative simulations are conducted for verifying the feasibility and effectiveness of the proposed approach.