Open Access

ARTICLE

T_GRASP: Optimization Algorithm of Ship Avoiding Typhoon Route

by Yingxian Huang, Xueyan Ding, Yanan Zhang, Leiming Yan^*

Nanjing University of Information Science & Technology, Nanjing, 210044, China

* Corresponding Author: Leiming Yan. Email:

Journal of Quantum Computing 2022, 4(2), 85-95. https://doi.org/10.32604/jqc.2022.031436

Received 18 April 2022; Accepted 23 March 2023; Issue published 16 May 2023

Abstract

A GRASP-based algorithm called T_GRASP for avoiding typhoon route optimization is suggested to increase the security and effectiveness of ship navigation. One of the worst natural calamities that can disrupt a ship’s navigation and result in numerous safety mishaps is a typhoon. Currently, the captains manually review the collected weather data and steer clear of typhoons using their navigational expertise. The distribution of heavy winds and waves produced by the typhoon also changes dynamically as a result of the surrounding large-scale air pressure distribution, which significantly enhances the challenge of the captain’s preparation for avoiding typhoon navigation. It is now necessary to find a solution to the challenge of designing a high-safety and effective ship navigation path to avoid typhoons. The T_GRASP algorithm is suggested to optimize the candidate set’s structure based on the GRASP algorithm’s frame. The algorithm can guarantee the safety of the ship to avoid typhoons and assure high route efficiency by using the lowest risk function, the shortest sailing time, and the least fuel consumption as the objective functions and the ship speed and highest safety as the model constraints. The outcomes of the simulation demonstrate the superiority of the suggested T_GRASP algorithm over both the conventional A^* algorithm and the ant colony algorithm. In addition to addressing issues with the traditional A^* algorithm, like its wide search space and poor efficiency, the proposed algorithm also addresses issues with the ant colony algorithm, like its excessive iterations and sluggish convergence.

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