Dual-information guided ant colony optimization algorithm for green multi-compartment vehicle routing problem

被引：0
作者：
Guo N. [1 ,2 ]
Shen Q.-Y. [1 ,3 ]
Qian B. [1 ,2 ,3 ]
Na J. [2 ]
Hu R. [1 ,2 ,3 ]
Mao J.-L. [1 ]
机构：
[1] Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Yunnan, Kunming
[2] Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Yunnan, Kunming
[3] Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Yunnan, Kunming
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2024年 / 41卷 / 06期
基金：
中国国家自然科学基金;
关键词：
ant colony optimization algorithm; dual-information guided; green; multi-compartment vehicle routing problem; pheromone concentration balance mechanism;
D O I：
10.7641/CTA.2023.20930
中图分类号：
学科分类号：
摘要：
For dealing with the green multi-compartment vehicle routing problem (GMCVRP) widely existing in actual transportation, a dual-information guided ant colony optimization algorithm (DIACO) is proposed to solve it. First, in the global search stage of DIACO, the pheromone concentration matrix (PCM) in the traditional ant colony optimization algorithm (TACO) is reconstructed. The reconstructed PCM contains both customer block information and customer sequence information. That is, the dual-information PCM (DIPCM) is established so as to more comprehensively learn and accumulate high-quality solution information. Three types of heuristic methods are adopted to generate higher quality individuals for initializing DIPCM, which can guide the algorithm to search for high-quality regions in the solution space quickly. Second, in the local search stage of DIACO, multiple variable neighborhood operations combined with adaptive strategy are designed to perform in-depth searches on high-quality regions of the solution space. Third, the pheromone concentration balance mechanism is proposed to prevent the search from stagnating. Last, simulation tests and algorithm comparisons are carried out with different scale examples. The results show that DIACO is an effective algorithm for solving the GMCVRP. © 2024 South China University of Technology. All rights reserved.
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收藏
页码：1067 / 1078
页数：11
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