The wild horse optimizer (WHO) is a novel metaheuristic algorithm, which has been successfully applied to solving continuous engineering problems. Considering the characteristics of the wild horse optimizer, a discrete version of the algorithm, named discrete wild horse optimizer (DWHO), is proposed to solve the capacitated vehicle routing problem (CVRP). By incorporating three local search strategies-swap operation, reverse operation, and insertion operation-along with the introduction of the largest-order-value (LOV) decoding technique, the precision and quality of the solutions have been enhanced. Experimental results conducted on 44 benchmark instances indicate that, in most test cases, the solving capability of discrete wild horse optimizer surpasses that of basic wild horse optimizer (BWHO), hybrid firefly algorithm, dynamic space reduction ant colony optimization (DSRACO), and discrete artificial ecosystem-based optimization (DAEO). The discrete wild horse optimizer provides a novel approach for solving the capacitated vehicle routing problem and also offers a new perspective for addressing other discrete problems.

In extreme weather conditions, the use of unmanned ground vehicles (UGVs) for material distribution enhances safety. We introduce a two-echelon unmanned ground vehicle routing problem (2E-UGVRP) and proposes a hybrid Artificial Bee Colony–Wild Horse Optimizer (HABC-WHO) algorithm to solve it. In this approach, the optimal solution obtained from the Artificial Bee Colony algorithm replaces the worst solution of the Wild Horse Optimizer. To further improve the algorithm’s performance, strategies such as large neighborhood search, two-optimization (2-Opt) operation, and satellite subpath crossover are incorporated. The algorithm’s effectiveness is demonstrated through the solution of 43 benchmark instances, with performance comparisons against a Genetic Algorithm (GA), Discrete Wild Horse Optimizer (DWHO), and Discrete Artificial Bee Colony–Fixed Neighborhood Search (DABC-FNS). The results clearly show the significant superiority of the proposed algorithm. Additionally, the algorithm is applied to material distribution by two-echelon UGVs under extreme weather conditions, yielding promising results. Experimental findings indicate that the algorithm exhibits strong solving capability and high precision.