Short-term prediction of ocean significant wave height is crucial for human maritime activities. However, historical wave height data possess characteristics such as high complexity and strong randomness. To address these issues, this paper proposes a hybrid model that combines Ensemble Empirical Mode Decomposition (EEMD) and Generative Adversarial Networks (GANs). The model first decomposes the raw wave height data using EEMD to select effective sub-series for reconstruction and implements denoising of the original data. Then, the reconstructed wave height sequence and relevant features are fed into a GAN-based prediction model. The generator in the model comprises of Long Short-Term Memory Network (LSTM) networks, while the discriminator consists of Convolutional Neural Networks (CNNs). Finally, the generator and discriminator are trained adversarially using North Pacific oceanographic data to achieve short-term predictions of ocean wave height. This proposed model is then compared with three baseline models, and the results show that our model performs the best in all three evaluation metrics, making it a valuable tool for ocean engineering.
The orienteering problem (OP) is one of the most classical optimization problems, and the objective is to find a tour of some of the given nodes to obtain as much value as possible, with a maximum tour length constraint. Since the OP is an NP-hard problem, how to deal with it efficiently and effectively is always challenging. This study proposes to solve it through a greedy strategy based iterative local search algorithm (ILS-G). In detail, the greedy strategy is used to generate a good initial solution for accelerating the search process, and the iterative local search algorithm is proposed to further optimize the initial solution. An insertion operator, a 2-OPT operator, and a deletion operator are introduced in the iterative local search algorithm for updating the solution. Experimental results on instances with different problem sizes demonstrate the effectiveness and the efficiency of the proposed ILS-G.
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