<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">RTED</journal-id><journal-title-group><journal-title>Research on Teacher Education and Development</journal-title></journal-title-group><issn>3066-8999</issn><eissn>3066-9006</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/RTED.2025030030</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于改进蚁群算法的散货船装卸作业路径规划研究</title><url>https://artdesignp.com/journal/RTED/1/3/10.61369/RTED.2025030030</url><author>胡义轩,朱永康,张唐文,朱成浩,申至庸,王恺,季明丽,李玉宝</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>1</volume><issue>3</issue><history><date date-type="pub"><published-time>2025-04-14</published-time></date></history><abstract>随着全球贸易的持续增长，散货船装卸作业效率成为影响港口吞吐能力的关键因素。本文针对散货船装卸作业路径规划问题，在综合分析两篇起重机路径规划研究论文的基础上，提出了一种改进的蚁群算法优化方案。通过引入动态启发因子、自适应信息素更新机制和三维路径分层规划策略，有效解决了传统算法在散货船复杂作业环境中存在的收敛速度慢、易陷入局部最优等问题。仿真实验结果表明，改进算法在路径长度、能耗效率和运行稳定性等方面均有显著提升，为散货船装卸作业的智能化提供了新的解决方案。</abstract><keywords>散货船,装卸作业,路径规划,蚁群算法,能耗优化</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] UNCTAD. Review of Maritime Transport 2022[R]. Geneva: United Nations, 2022.&amp;nbsp;[2] Zhang L, et al. Efficiency analysis of bulk cargo handling systems[J]. Maritime Policy &amp;amp; Management, 2021, 48(3): 412-428.&amp;nbsp;[3] IAPH. Green Port Guidelines[S]. 2021 Edition.&amp;nbsp;[4] 付雪青. 钢卷仓库三维建模及吊装路径规划[D]. 重庆大学, 2016.&amp;nbsp;[5] 沈舒杰. 基于改进蚁群算法的桥式起重机最优能耗路径规划方法[D]. 太原科技大学, 2024.&amp;nbsp;[6] Dorigo M, et al. Ant colony optimization: overview and recent advances[J]. Handbook of Metaheuristics, 2019: 311-351.&amp;nbsp;[7] 周浩 , 等 . 基于改进蚁群算法的桥式起重机路径规划问题研究[J]. 机械设计与制造, 2021(4): 133-136.&amp;nbsp;[8] 李敏 . 桥式起重机吊装避障路径规划方法研究[D]. 太原科技大学, 2020.&amp;nbsp;[9] Colorni A, et al. Distributed optimization by ant colonies[C]. ECAL91, 1991.&amp;nbsp;[10] 港口散货装卸工艺规范[S]. JTS/T 201-2020.&amp;nbsp;[11] Liu Y, et al. Anti-swing control for crane systems[J]. IEEE Transactions on Control Systems Technology, 2020, 28(3): 894-905.&amp;nbsp;[12] 王明 , 等 . 抓斗式卸船机能耗特性研究[J]. 起重运输机械, 2022(3): 45-49.&amp;nbsp;[13] 陈志梅 , 等. 基于改进蜂群算法的桥式起重机吊装路径规划[J]. 起重运输机械, 2022(8): 20-25.&amp;nbsp;[14] St&amp;uuml;tzle T, et al. ACO algorithms for the traveling salesman problem[J]. Evolutionary Computation, 2000: 1-10.&amp;nbsp;[15] 孔令德 . 计算几何算法与实现[M]. 电子工业出版社, 2017.&amp;nbsp;[16] Tao F, et al. Digital twins and cyber-physical systems toward smart manufacturing[J]. Robotics and Computer-Integrated Manufacturing, 2019, 61: 101837.&amp;nbsp;[17] 郭明翔 . 集装箱桥式起重机变频调速系统再生运行及节能研究[J]. 中国设备工程, 2018(23): 154-157.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
