Harmony search (HS) is a form of stochastic meta-heuristic inspired by the improvisation process of musicians. In this study, a modified HS with a hybrid cuckoo search (CS) operator, HS-CS, is proposed to enhance global search ability while avoiding falling into local optima. First, the randomness of the HS pitch disturbance adjusting method is analyzed to generate an 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. Second, 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 optimum in the randomly generated harmony and update stage. Finally, a dynamic parameter adjustment mechanism is set to improve the efficiency of optimization. Three theorems are proved to reveal HS-CS as a global convergence meta-heuristic 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, high convergence speed, and high convergence accuracy. For further verification, HS-CS is used to optimize the back propagation neural network (BPNN) to extract weighted fuzzy production rules. 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 is proved to be effective.

和声搜索 (HS) 是一种随机元启发式方法，其灵感来自于音乐家的即兴创作过程。在这项研究中，提出了一种带有混合布谷鸟搜索（CS）算子HS-CS的改进HS，以增强全局搜索能力，同时避免陷入局部最优。首先，分析HS基音扰动调整方法的随机性，根据和声存储器中解的质量生成自适应惯性权重，并重构微调带宽优化。这是为了提高HS算法优化的效率和准确性。其次，引入CS算子，扩大解空间范围，提高种群密度，在随机生成的和谐更新阶段能够快速跳出局部最优。最后，设置动态参数调整机制，提高优化效率。三个定理被证明揭示了 HS-CS 作为一种全局收敛元启发式算法。此外，优化方案还选取了12个基准函数来验证HS-CS的性能。分析表明，HS-CS在优化高维问题方面明显优于其他算法，具有鲁棒性强、收敛速度快、收敛精度高等特点。为了进一步验证，使用HS-CS优化反向传播神经网络（BPNN）来提取加权模糊产生式规则。仿真结果表明，HS-CS优化后的BPNN可以获得较高的加权模糊产生式规则分类精度。因此，所提出的HS-CS被证明是有效的。