This paper proposes the Hierarchical Learning Backtracking Search Algorithm (HLBSA) for global optimization. HLBSA divides the population into three subpopulations: elite, ordinary, and inferior groups. In comparison to four variant Backtracking Search Algorithms (BSA) on the CEC2017 benchmark suite. The experimental results demonstrate that the proposed HLBSA algorithm outperforms other comparative BSA algorithms in terms of solution search efficiency.