Abstract: When solving high-dimensional equations, the CS (Cuckoo Search) has the drawback of falling into local optima. To address this deficiency, an improved hybrid cuckoo search is proposed. Firstly, the population is initialized using chaotic mapping and reversed learning mechanisms. Then the search mechanisms of TLBO (Teaching Learning Based Optimization) and CS are performed alternately. Finally, the discovery probability and embeds DE (Differential Evolution) are dynamically adjusted to comprehensively improve the algorithm's performance. The comparative results of simulation experiments with 6 benchmark functions and 1 optimized grating coupler design show that this algorithm is better for solving high-dimensional equations and effectively avoids the CS algorithm getting stuck in local optima.

Key words: cuckoo search, teaching learning based optimization, differential evolution, chaotic mapping