A binary particle swarm optimization algorithm inspired by multi-level organizational learning behavior

► We propose a BPSO_HS, based on multi-level organizational learning behavior. ► In BPSO_HS, particles are divided into two classes according to their performances. ► Different evolutionary strategies are used in each class. ► Mutation operator is adopted to avoid the premature convergence. Recently, nature-inspired algorithms have increasingly attracted the attention of researchers. Due to the fact that in BPSO the position vectors consisting of ‘0’ and ‘1’ can be seen as a decision behavior (support or oppose), in this paper, we propose a BPSO with hierarchical structure (BPSO_HS for short), on the basis of multi-level organizational learning behavior. At each iteration of BPSO_HS, particles are divided into two classes, named ‘leaders’ and ‘followers’, and different evolutionary strategies are used in each class. In addition, the mutation strategy is adopted to overcome the premature convergence and slow convergent speed during the later stages of optimization. The algorithm was tested on two discrete optimization problems (Traveling Salesman and Bin Packing) as well as seven real-parameter functions. The experimental results showed that the performance of BPSO_HS was significantly better than several existing algorithms.