Journal
JOURNAL OF OCEAN UNIVERSITY OF CHINA
Volume 20, Issue 1, Pages 45-55Publisher
OCEAN UNIV CHINA
DOI: 10.1007/s11802-021-4436-6
Keywords
multi-objective optimization; weather routing; ACO algorithm; fuel consumption
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This paper presents a novel intelligent and effective method based on an improved ant colony optimization algorithm to solve the multi-objective ship weather routing optimization problem, while also using a black-box model method to study ship fuel consumption during a voyage. The improved algorithm shows effectiveness in solving multi-objective weather routing optimization problems, and the black-box model method demonstrates higher accuracy and applicability compared to other fuel consumption calculation methods.
This paper presents a novel intelligent and effective method based on an improved ant colony optimization (ACO) algorithm to solve the multi-objective ship weather routing optimization problem, considering the navigation safety, fuel consumption, and sailing time. Here the improvement of the ACO algorithm is mainly reflected in two aspects. First, to make the classical ACO algorithm more suitable for long-distance ship weather routing and plan a smoother route, the basic parameters of the algorithm are improved, and new control factors are introduced. Second, to improve the situation of too few Pareto non-dominated solutions generated by the algorithm for solving multi-objective problems, the related operations of crossover, recombination, and mutation in the genetic algorithm are introduced in the improved ACO algorithm. The final simulation results prove the effectiveness of the improved algorithm in solving multi-objective weather routing optimization problems. In addition, the black-box model method was used to study the ship fuel consumption during a voyage; the model was constructed based on an artificial neural network. The parameters of the neural network model were refined repeatedly through the historical navigation data of the test ship, and then the trained black-box model was used to predict the future fuel consumption of the test ship. Compared with other fuel consumption calculation methods, the black-box model method showed higher accuracy and applicability.
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