论文标题
混合2阶段帝国主义竞争算法，具有蚂蚁菌落优化，用于解决多动用车辆路线问题
Hybrid 2-stage Imperialist Competitive Algorithm with Ant Colony Optimization for Solving Multi-Depot Vehicle Routing Problem
论文作者
论文摘要
多部门车辆路由问题（MDVRP）是简单车辆路由问题（VRP）的现实世界模型，它考虑如何满足众多仓库的多个客户需求。本文介绍了一种基于两种基于人群的算法的混合2阶段方法 - 蚂蚁菌落优化（ACO），该算法模仿了自然界中的蚂蚁行为和基于国家之间地缘政治关系的帝国主义竞争算法（ICA）。在拟议的混合算法中，ICA负责在ACO路由和测序客户时对仓库的客户分配。将算法与非杂交ACO和ICA以及23个常见脐带基准实例中的其他四种最先进的方法进行了比较。结果显示出对简单ACO和ICA的明显改善，与其他竞争对手算法相比，结果表现出非常具竞争力的结果。
The Multi-Depot Vehicle Routing Problem (MDVRP) is a real-world model of the simplistic Vehicle Routing Problem (VRP) that considers how to satisfy multiple customer demands from numerous depots. This paper introduces a hybrid 2-stage approach based on two population-based algorithms - Ant Colony Optimization (ACO) that mimics ant behaviour in nature and the Imperialist Competitive Algorithm (ICA) that is based on geopolitical relationships between countries. In the proposed hybrid algorithm, ICA is responsible for customer assignment to the depots while ACO is routing and sequencing the customers. The algorithm is compared to non-hybrid ACO and ICA as well as four other state-of-the-art methods across 23 common Cordreaus benchmark instances. Results show clear improvement over simple ACO and ICA and demonstrate very competitive results when compared to other rival algorithms.
