文献类型：期刊文献

英文题名：An Exponential Entropy-based Hybrid Ant Colony Algorithm for Vehicle Routing Optimization

作者：Qi, Chengming[1];Li, Ping[2]

第一作者：亓呈明

通讯作者：Qi, CM[1]

机构：[1]Beijing Union Univ, Coll Automat, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Inst Logist, Beijing 100101, Peoples R China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Automat, Beijing 100101, Peoples R China.|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

年份：2014

卷号：8

期号：6

起止页码：3167-3173

外文期刊名：APPLIED MATHEMATICS & INFORMATION SCIENCES

收录：;Scopus(收录号：2-s2.0-84904568937);WOS:【SCI-EXPANDED(收录号:WOS:000338123900058)】；

语种：英文

外文关键词：Ant Colony System; Information Entropy; Iterated Local Search; Logistics vehicle Routing

摘要：Vehicle routing problem(VRP) is important combinatorial optimization problems which have received considerable attention in the last decades. The optimization of vehicle routing problem is a well-known research problem in the logistics distribution. In order to overcome the prematurity of Ant Colony Algorithm (ACA) for logistics distribution routing optimization, a hybrid algorithm combining improved ACA with Iterated Local Search (ILS) is proposed. The proposed algorithm adjusts the pheromone trail to balance the convergence rate and diversification of solutions self-adaptively. The exponential entropy is used to control the path selection and pheromone updating strategy. Combining with ILS is to avoid local best solutions and accelerate the search. Computational results denote the efficiency of the proposed algorithm on some standard benchmark problems.