Fang Chuncheng, Cai Yanguang, Wu Yanlin
School of Automation, Guangdong University of Technology, Guangzhou, 510006, China.
Department of Mechanical and Electrical Engineering, Jieyang Polytechnic, Jieyang, 522051, China.
Sci Rep. 2024 Sep 11;14(1):21277. doi: 10.1038/s41598-024-72242-0.
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.
野马优化器（WHO）是一种新颖的元启发式算法，已成功应用于解决连续工程问题。考虑到野马优化器的特点，提出了一种离散版本的算法，即离散野马优化器（DWHO），用于解决容量车辆路径问题（CVRP）。通过结合三种局部搜索策略——交换操作、反向操作和插入操作——并引入最大订单值（LOV）解码技术，提高了求解的精度和质量。在44个基准实例上进行的实验结果表明，在大多数测试案例中，离散野马优化器的求解能力超过了基本野马优化器（BWHO）、混合萤火虫算法、动态空间缩减蚁群优化（DSRACO）和基于离散人工生态系统的优化（DAEO）。离散野马优化器为解决容量车辆路径问题提供了一种新颖的方法，也为解决其他离散问题提供了新的视角。
