An application of augmented Lagrangian differential evolution algorithm for optimizing the speed of inland ships sailing on the Yangtze River

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An application of augmented Lagrangian differential evolution algorithm for optimizing the speed of inland ships sailing on the Yangtze River

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Longhui Zhang / Xiuyan Peng / Zhengfeng Liu / Naxin Wei / Fei Wang

As an effective way to achieve energy conservation and emission reduction, the speed optimization of inland ships has received increasing attention. In this study, a modeling method for the speed optimization of inland ships sailing on the Yangtze River and a heuristic algorithm called Augmented Lagrangian Differential Evolution (ALDE) method are proposed. The ALDE introduces adaptive control parameters, an elite strategy, and opposition-based learning technology for optimization. To validate the performance of ALDE, it is compared with six other algorithms to solve several well-known constrained optimization problems, and the results show that the ALDE outperforms these six algorithms. To construct a speed optimization model, a discretization model for the Yangtze River waterway is established by segmenting the waterway. Considering the relationship among the environment, ship, engine, and propeller, a fuel consumption prediction model is built, and then, the objective optimization function for inland ship speed optimization is established by integrating the discrete model as well as the fuel consumption model. Finally, numerical simulation results show that the ALDE can realize less fuel consumption for an inland ship sailing from Yibin Port to Jiangyin Port, verifying the feasibility of the ALDE for speed optimization of inland ships.