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You-Gan Wang

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2 papers

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EAAI Journal 2022 Journal Article

An opposition learning and spiral modelling based arithmetic optimization algorithm for global continuous optimization problems

Yang Yang
Yuchao Gao
Shuang Tan
Shangrui Zhao
Jinran Wu
Shangce Gao
Tengfei Zhang
Yu-Chu Tian

In engineering applications, many real-world optimization problems are nonlinear with multiple local optimums. Traditional algorithms that require gradients are not suitable for these problems. Meta-heuristic algorithms are popularly employed to deal with these problems because they can promisingly jump out of local optima and do not need any gradient information. The arithmetic optimization algorithm (AOA), a recently developed meta-heuristic algorithm, uses arithmetic operators (multiplication, division, subtraction, and addition) to solve optimization problems including nonlinear ones. However, the exploration and exploitation of AOA are not effective to handle some complex optimization problems. In this paper, an opposition learning and spiral modelling based AOA, namely OSAOA, is proposed for enhancing the optimization performance. It improves AOA from two perspectives. In the first perspective, the opposition-based learning (OBL) is committed to taking both candidate solutions and their opposite solutions into consideration for improving the global search with a high probability of jumping out of local minima. Then, the spiral modelling is introduced as the second perspective, which is particularly useful in getting the solutions gathering faster and accelerating the convergence speed in the later stage. In addition, OSAOA is compared with other existing advanced meta-heuristic algorithms based on 23 benchmark functions and four engineering problems: the three-bar truss design, the cantilever beam design, the pressure vessel design, and the tubular column design. From our simulations and engineering applications, the proposed OSAOA can provide better optimization results in dealing with these real-world optimization problems.

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EAAI Journal 2021 Journal Article

A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability

Shaotong Zhang
Jinran Wu
Yonggang Jia
You-Gan Wang
Yaqi Zhang
Qibin Duan

In situ observations of suspended sediment concentration (SSC) and hydrodynamics were conducted in the subaqueous Yellow River Delta, China. With the dataset, a new least absolute shrinkage and selection operator (LASSO) regression model with temporal autocorrelation incorporated (temporal LASSO) is proposed for SSC prediction and mechanism investigation in coastal oceans. The model is concise and practical, effectively shrinking the interrelated variables into representative ones, while also achieving one-hour ahead forecasting with both higher accuracy and better interpretability than other data-driven methods. The model interpretability is further validated with direct data analysis from a physical perspective. Specifically, Empirical Mode Decomposition is employed to decouple the measured SSC into intrinsic mode functions (IMFs) and a residual. The periods of each subseries estimated from both zero-crossing and spectrum analysis show that IMF1 physically corresponds to the sediment resuspension by M4 tidal currents, IM F 2 is the M2 tidal advection, IMF3-IMF5 are the resuspension by wind waves, IM F 6 is the spring–neap tidal pumping of sediments. The contributions estimated with the ratio of variance are 12 %, 14 %, 63 %, and 10 %, respectively, over the observation period. The residual is the seasonal variations which can be taken as the background SSC thus not included for variance contribution. Waves make the dominant contribution which verifies the rationality of the LASSO shrinkage and confirms the model interpretability. The temporal LASSO model is shown to be a potential tool for emergency forecasting and mechanism explanation of SSC to benefit ocean environmental engineering management.

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