# A Novel Energy-Saving Route Planning Algorithm for Marine Vehicles
## 1 Introduction  
Most marine vehicles use fuel as their power source, and their voyage is limited by the fuel they carry [1]. Efficient employ of limited energy for long-time and long-distance voyages is the major challenge facing marine vehicles at present [2, 3]. To settle this problem, scholars have proposed various methods to reduce the energy consumption of marine vehicles. Among these methods, the route planning method is not only conducive to reducing the influence of complex ocean conditions on the power consumption of ships, but it also improves the safety of ships. Therefore, the ship route planning method has received attention from many researchers. In this aspect, the route planning of marine vehicles could be global or local. For global route planning, ships would identify all spaces with obstacles in the given chart in advance, and then to choose an energy-saving route without obstacles. In comparison, local route planning utilizes the obstacle avoidance algorithm to achieve the same result [4, 5, 6]. In global path planning, scholars have proposed many algorithms for optimal ship route planning tasks. The literature [7, 8, 9, 10, 11] has employed a genetic algorithm to search the ship's route. However, the genetic algorithm is a unstable and time-consuming algorithm. The literature [12, 13, 14] has employed a particle swarm optimization algorithm to achieve ship functions of obstacle avoidance and trajectory optimization. This algorithm is simple, with good robustness, and has a fast early-stage convergence speed, but the convergence speedbecomes slow in the later stage of the search. [PERSON] et al. [15] addressed the wolf pack algorithm to study the navigation and route planning of ships. This algorithm applies the wolf pack update mechanism to optimize the complex search space. Although the algorithm has good convergence, global optimization, and strong robustness, it is relatively complicated owing to more parameters to be set. Among recent research of route planning based on energy-saving, [PERSON] [16] proposed a constrained sampling A star (CSA*) algorithm for enhancing search effects. Meanwhile, adaptive quantum-behaved particle swarm optimization (AQPSO) is integrated with the CSA*, which could cut down the energy expenditure in case of currents influence. [PERSON] [17] proposed a novel double ant colony algorithm (NDACA) on the basis of dynamic feedback. The ant colony is grouped as exploratory and optimized ants according to the pheromone. A closed-cycle feedback tactic is applied to constantly adjust the number of ants in every colony, which could improve the resolution quality and speed up the convergence. NDACA could plan a route with less energy consumption and also take the influence of marine environment factors into account. [PERSON] [18] proposed a new genetic-ant compound algorithm. As a novel optimal integrated strategy, this algorithm increases the efficiency of integration. Meanwhile, taking into account the energy consumption under the currents influence, it sets up the merit function on the basis of minimized energy consumption. Compared with the above algorithms, the ant colony algorithm has the advantages of low requirements on initial route selection and strong robustness. It is vital that the ant colony algorithm has relatively fewer parameters and does not require manual adjustment, which is more suitable for ship route planning in complex marine environments [19]. In addition, the ant colony algorithm has great advantages in solving the TSP (traveling salesman problem) of marine vehicles [20; 21; 22]. Scholars have proposed many improved ant colony algorithms and applied them to the route optimization of marine vehicles [23]. [PERSON] [24] proposed a path planning algorithm considering ocean currents, which combines artificial potential field method and ant colony algorithm. It avoids the local extremum problem of artificial potential field method and improves the performance of ant colony algorithm.