A multi-strategy hybrid cuckoo search algorithm with specular reflection based on a population linear decreasing strategy

The cuckoo search algorithm (CS), an algorithm inspired by the nest-parasitic breeding behavior of cuckoos, has proved its own effectiveness as a problem-solving approach in many fields since it was proposed. Nevertheless, the cuckoo search algorithm still suffers from an imbalance between exploration and exploitation as well as a tendency to fall into local optimization. In this paper, we propose a new hybrid cuckoo search algorithm (LHCS) based on linear decreasing of populations, and in order to optimize the local search of the algorithm and make the algorithm converge quickly, we mix the solution updating strategy of the Grey Yours sincerely, wolf optimizer (GWO) and use the linear decreasing rule to adjust the calling ratio of the strategy in order to balance the global exploration and the local exploitation; Second, the addition of a specular reflection learning strategy enhances the algorithm's ability to jump out of local optima; Finally, the convergence ability of the algorithm on different intervals and the adaptive ability of population diversity are improved using a population linear decreasing strategy. The experimental results on 29 benchmark functions from the CEC2017 test set show that the LHCS algorithm has significant superiority and stability over other algorithms when the quality of all solutions is considered together. In order to further verify the performance of the proposed algorithm in this paper, we applied the algorithm to engineering problems, functional tests, and Wilcoxon test results show that the comprehensive performance of the LHCS algorithm outperforms the other 14 state-of-the-art algorithms. In several engineering optimization problems, the practicality and effectiveness of the LHCS algorithm are verified, and the design cost can be greatly reduced by applying it to real engineering problems.