Abstract: A new hybrid bird swarm algorithm (NHBSA) for solving no-idle flow-shop scheduling problem was proposed to minimize the makespan. Firstly, a Farahmand-Ruiz-Boroojerdian (FRB) heuristic was modified, the modified FRB heuristic and chaotic mapping were combined to ameliorate the quality and diversity of the population in the initialization phase. Secondly, the Smallest-Position-Value (SPV) rule was adopted to perform conversion between continuous position and discrete job permutation to make the algorithm suitable for dealing with discrete scheduling problems. In addition, to improve the convergence accuracy and the ability to avoid getting stuck in local optima of the algorithm, a local search method for the optimal job permutation of population was come up with by drawing on the ideas of variable neighborhood search and iterative greedy algorithm. Computational simulations and comparisons with several meta-heuristic algorithms for NFSP were carried out based on the widely used Taillard benchmark, the results show that the average percentage relative deviation (APRD) and the performance improvement percentage (PIP) obtained by NHBSA were reduced by 71.017% and 4.653%, respectively, under the premise of ensuring good stability.