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Abstract: Harmony Search (HS) is a form of stochastic meta-heuristic that is inspired by the improvisation process of musicians. In this study, a modified HS with hybrid Cuckoo Search (CS) operator (HS-CS) is proposed to enhance global search ability but avoid falling into a local optimum. Firstly, a randomness of the HS pitch adjusting disturbance method is analyzed to generate adaptive inertia weight according to the quality of solutions in the harmony memory and to reconstruct the fine-tuning bandwidth optimization. This is to improve the efficiency and accuracy of HS algorithm optimization. Secondly, the CS operator is introduced to expand the scope of the solution space and improve the density of the population, which can quickly jump out of the local optimal in the randomly generated harmony and update stage. Finally, a dynamic adjustment parameter mechanism is set to accelerate the efficiency of optimization. Then, three theorems are proved to reveal the HS-CS as a global convergence metaheuristic algorithm. In addition, 12 benchmark functions are selected for the optimization solution to verify the performance of HS-CS. The analysis shows that HS-CS is significantly better than other algorithms in optimizing high-dimensional problems with strong robustness, convergence speed and convergence accuracy. For further verification, the HS-CS is used to optimize the back propagation neural network (BPNN) to extract weighted fuzzy production rules. The simulation results show that the BPNN optimized by HS-CS can obtain higher classification accuracy of weighted fuzzy production rules. Therefore, the proposed HS-CS proved to be effective.