Whale Optimization Algorithm for Ship Path Optimization in Large-Scale Complex Marine Environment

Han, Qilong; Yang, Xiao Dong; Song, Hongtao; Sui, Shanshan; Zhang, Hui; Yang, Zhong-Jian

doi:10.1109/access.2020.2982617

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Han et al. introduced a meta-heuristic Whale Optimization Algorithm (WOA), which can help ships find safe routes with low energy consumption in large and complex ocean environments [10].…”

Section: A Global Route Planningmentioning

confidence: 99%

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PE-A* Algorithm for Ship Route Planning Based on Field Theory

Liu

Wang

2022

IEEE Access

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Route planning is the key to safe, efficient and intelligent navigation of maritime traffic. Autonomous route planning is a complex optimization problem, which requires both global route planning and local collision avoidance. In this paper, we propose an optimization algorithm which can consider both global route planning and local collision avoidance. Firstly, nonlinear constraint optimization models of obstacle limitation, safe water depth limitation and ship steering limitation are established. Then, the PE-A * algorithm and route planning framework are proposed by using potential energy field to accurately express the environment. Finally, the safety and feasibility of the route planned by PE-A * algorithm are discussed through simulation experiments. Simulation results show that PE-A * algorithm can realize both global route planning and local collision avoidance, and the safety and feasibility of the planned route are greatly improved. From the perspective of potential energy, this work proposes an automatic route planning algorithm and establishes a flexible mathematical model, which can add fuel consumption, time and other related engineering requirements into the model to plan the optimal route meeting the requirements.INDEX TERMS Ship, potential energy, A * algorithm, route planning.

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“…Han et al. introduced a meta-heuristic Whale Optimization Algorithm (WOA), which can help ships find safe routes with low energy consumption in large and complex ocean environments [10].…”

Section: A Global Route Planningmentioning

confidence: 99%

“…On the contrary, there is collision. The model is: E α x min , y min < E safe (10) E α (x min , y min ) is the minimum safe potential energy value of the section α of the safe potential energy field, E safe represents the threshold of the ship's safe potential energy.…”

Section: Potential Nergy Definition Of Collisionmentioning

confidence: 99%

PE-A* Algorithm for Ship Route Planning Based on Field Theory

Liu

Wang

2022

IEEE Access

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“…Heuristic algorithms can solve various optimization problems including the feature selection. Due to their effectiveness and simplicity, many heuristic algorithms have been proposed for solving the feature selection problems, e.g., GA [15], PSO [16], GWO [17], flower pollination algorithm (FPA) [18], artificial bee colony (ABC) [19], bacterial foraging optimization (BFO) [20], BA [21], cuckoo search (CS) [22], firefly algorithm (FA) [23], whale optimization algorithm (WOA) [24], grasshopper optimization algorithm (GOA) [25]. Recently, more and more heuristic algorithms are proposed to deal with many kinds of optimization problems.…”

Section: Related Workmentioning

confidence: 99%

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

Kang

Sun

et al. 2020

IEEE Access

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Feature selection is an effective method to eliminate irrelevant, redundant and noisy features, which improves the performance of classification and reduces the computational burden in machine learning. In this paper, an improved binary dragonfly algorithm (IBDA) which extends from the conventional dragonfly algorithm (DA) is proposed as a search strategy to design a wrapper-based feature selection method. First, a novel evolutionary population dynamics (EPD) strategy is introduced in IBDA to enhance the exploitation ability while ensuring population diversity of the algorithm. Second, IBDA proposes a novel crossover operator which establishes connections between the crossover rates and iterations so that making the algorithm can adjust the crossover rates of solutions dynamically, thereby balancing the exploitation and exploration of the algorithm. Finally, a binary mechanism is proposed to make the algorithm suitable for the binary feature selection problems. Simulations are conducted on 27 classical datasets from the UC Irvine Machine Learning Repository, and the results demonstrate that the proposed IBDA has better performance than some other comparison algorithms. Moreover, the effectiveness and performance of the proposed improved factors are evaluated by tests.

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“…The continuous improvement in WOA optimization performance has led to WOA being used in a wide range of research areas. Currently, domestic and foreign researchers and scholars have applied the whale optimization algorithm to path planning [ 16 ], battery charging [ 17 ], optimal reactive power scheduling [ 18 ], load prediction [ 19 ], fault location [ 20 ] and other fields [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Multistrategy Improved Whale Optimization Algorithm and Its Application

Liu

Zhang

2022

Computational Intelligence and Neuroscience

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To address the shortcomings of the whale optimization algorithm (WOA) in terms of insufficient global search ability and slow convergence speed, a differential evolution chaotic whale optimization algorithm (DECWOA) is proposed in this paper. Firstly, the initial population is generated by introducing the Sine chaos theory at the beginning of the algorithm to increase the population diversity. Secondly, new adaptive inertia weights are introduced into the individual whale position update formula to lay the foundation for the global search and improve the optimization performance of the algorithm. Finally, the differential variance algorithm is fused to improve the global search speed and accuracy of the whale optimization algorithm. The impact of various improvement strategies on the performance of the algorithm is analyzed using different kinds of test functions that are randomly selected. The particle swarm optimization algorithm (PSO), butterfly optimization algorithm (BOA), WOA, chaotic feedback adaptive whale optimization algorithm (CFAWOA), and DECWOA algorithm are compared for the optimal search performance. Experimental simulations are performed using MATLAB software, and the results show that the improved whale optimization algorithm has a better global optimization-seeking capability. The improved whale optimization algorithm is applied to the distribution network fault location of IEEE-33 nodes, and the effectiveness and accuracy of the distribution network fault zone location based on the multistrategy improved whale optimization algorithm is verified.

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Whale Optimization Algorithm for Ship Path Optimization in Large-Scale Complex Marine Environment

Cited by 9 publications

References 26 publications

PE-A* Algorithm for Ship Route Planning Based on Field Theory

PE-A* Algorithm for Ship Route Planning Based on Field Theory

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

Multistrategy Improved Whale Optimization Algorithm and Its Application

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