A Novel Energy-Saving Route Planning Algorithm for Marine Vehicles
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Maritime Transport Emissions and Efficiency · Maritime Navigation and Safety · Maritime Ports and Logistics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Mathematics and Computer Science College, Guangdong Ocean University, Zhanjiang 524000, China
Maritime College, Guangdong Ocean University, Zhanjiang 524000, China
Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province, Zhanjiang 524000, China
School of Navigation, Guangzhou Maritime University, Guangzhou 510725, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Guangdong Province · special projects of key fields (Artificial Intelligence) of Universities in Guangdong Province · scientific research program for university of Guangzhou Education Bureau · program for scientific research start-up funds of Guangdong Ocean University
References (29)
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