文献类型：期刊文献

英文题名：A Novel Energy-Saving Route Planning Algorithm for Marine Vehicles

作者：Ding, Ying[1];Li, Ronghui[2,3];Shen, Haiqing[4];Li, Jiawen[2];Cao, Liang[2]

机构：[1]Guangdong Ocean Univ, Math & Comp Sci Coll, Zhanjiang 524000, Peoples R China;[2]Guangdong Ocean Univ, Maritime Coll, Zhanjiang 524000, Peoples R China;[3]Tech Res Ctr Ship Intelligence & Safety Engn Guan, Zhanjiang 524000, Peoples R China;[4]Guangzhou Maritime Univ, Sch Nav, Guangzhou 510725, Peoples R China

年份：2022

卷号：12

期号：12

外文期刊名：APPLIED SCIENCES-BASEL

收录：SCI-EXPANDED(收录号：WOS:000817581400001)、、Scopus(收录号：2-s2.0-85132287986)、WOS

基金：This research was funded by the National Natural Science Foundation of China (52171346), the Natural Science Foundation of Guangdong Province (2021A1515012618), the special projects of key fields (Artificial Intelligence) of Universities in Guangdong Province (2019KZDZX1035), the scientific research program for university of Guangzhou Education Bureau (202032779), and program for scientific research start-up funds of Guangdong Ocean University (R19055).

语种：英文

外文关键词：marine vehicles; energy-saving route planning; improved ant colony algorithm

外文摘要：Recently, the ship energy consumption issue has been in the spotlight. Meanwhile, new conventions and standards of ship energy conservation as well as ship emission reduction have been continuously introduced by the International Maritime Organization and governments around the world. Energy conservation is a crucial factor in ship route planning. This paper proposes an improved ant colony algorithm that can handle a complicated ocean current environment and find an optimal energy-saving path. Firstly, this paper focuses on both the path length and energy consumption. To reduce energy consumption, a comprehensive energy cost function is introduced by a conventional ant colony algorithm. In addition, this paper analyzes the influence of the ocean current speed and setting on a marine vehicle and establishes an improved ant colony algorithm model based on the influence of the ocean current. Finally, simulation tests are carried out in Lianyungang and its surrounding areas to test the energy consumption performance of the improved ant colony algorithm. The simulation result shows that the improved ant colony algorithm can significantly reduce the energy consumption by 3.326% compared with the traditional algorithm.