Patterns of Upper Layer Circulation Variability in the South China Sea from Satellite Altimetry Using the Self-Organizing Map

Patterns of the South China Sea (SCS) circulation variability are extracted from merged satellite altimetry data from October 1992 through August 2004 by using the self-organizing map (SOM). The annual cycle, seasonal and inter-annual variations of the SCS surface circulation are identified through the evolution of the characteristic circulation patterns. The annual cycle of the SCS general circulation patterns is described as a change between two opposite basin-scale SW-NE oriented gyres embedded with eddies: low sea surface height anomaly (SSHA) (cyclonic) in winter and high SSHA (anticyclonic) in summer half year. The transition starts from July - August (January - February) with a high (low) SSHA tongue east of Vietnam around 12°-14° N, which develops into a big anticyclonic (cyclonic) gyre while moving eastward to the deep basin. During the transitions, a dipole structure, cyclonic (anticyclonic) in the north and anticyclonic (cyclonic) in the south, may be formed southeast off Vietnam with a strong zonal jet around 10° - 12° N. The seasonal variation is modulated by the interannual-variations. Besides the strong 1997/1998 event in response to the peak Pacific El Niño in 1997, the overall SCS sea level is found to have a significant rise during 1999-2001, however, in summer 2004 the overall SCS sea level is lower and the basin-wide anticyclonic gyre becomes weaker than the other years

Patterns of Upper Layer Circulation Variability in the South China Sea from Satellite Altimetry Using the Self-Organizing Map

Patterns of the South China Sea (SCS) circulation variability are extracted from merged satellite altimetry data from October 1992 through August 2004 by using the self-organizing map (SOM). The annual cycle, seasonal and inter-annual variations of the SCS surface circulation are identified through the evolution of the characteristic circulation patterns. The annual cycle of the SCS general circulation patterns is described as a change between two opposite basin-scale SW-NE oriented gyres embedded with eddies: low sea surface height anomaly (SSHA) (cyclonic) in winter and high SSHA (anticyclonic) in summer half year. The transition starts from July - August (January - February) with a high (low) SSHA tongue east of Vietnam around 12°-14° N, which develops into a big anticyclonic (cyclonic) gyre while moving eastward to the deep basin. During the transitions, a dipole structure, cyclonic (anticyclonic) in the north and anticyclonic (cyclonic) in the south, may be formed southeast off Vietnam with a strong zonal jet around 10° - 12° N. The seasonal variation is modulated by the interannual-variations. Besides the strong 1997/1998 event in response to the peak Pacific El Niño in 1997, the overall SCS sea level is found to have a significant rise during 1999-2001, however, in summer 2004 the overall SCS sea level is lower and the basin-wide anticyclonic gyre becomes weaker than the other years

Surrogate-Assisted Evolutionary -Learning for Black-Box Dynamic Time-Linkage Optimization Problems

Zhang T, Wang H, Yuan B, Jin Y, Yao X. Surrogate-Assisted Evolutionary -Learning for Black-Box Dynamic Time-Linkage Optimization Problems. IEEE Transactions on Evolutionary Computation. 2023;27(5):1162-1176.Dynamic time-linkage optimization problems (DTPs) are special dynamic optimization problems (DOPs) with the time-linkage property. The environment of DTPs changes not only over time but also depends on the previous applied solutions. DTPs are hardly solved by existing dynamic evolutionary algorithms because they ignore the time-linkage property. In fact, they can be viewed as multiple decision-making problems and solved by reinforcement learning (RL). However, only some discrete DTPs are solved by RL-based evolutionary optimization algorithms with the assumption of observable objective functions. In this work, we propose a dynamic evolutionary optimization algorithm using surrogate-assisted Q -learning for continuous black-box DTPs. To observe the states of black-box DTPs, the state extraction and prediction methods are applied after the search process at each time step. Based on the learned information, a surrogate-assisted Q -learning is introduced to evaluate and select candidate solutions in the continuous decision space in a long-term consideration. We evaluate the components of our proposed algorithm on various benchmark problems to study their behaviors. Results of comparative experiments indicate that the proposed algorithm outperforms other compared algorithms and performs robustly on DTPs with up to 30 decision variables and different dynamic changes

An efficient evolutionary algorithm for high-speed train rescheduling under a partial station blockage

Wang R, Zhang Q, Dai X, et al. An efficient evolutionary algorithm for high-speed train rescheduling under a partial station blockage. Applied Soft Computing. 2023;145: 110590.This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. Firstly, a HSTR model subject to train operation constraints is established to minimize the total train delay. A permutation-based encoding method is developed to define an efficient search space based on the train departure sequence. A heuristic decoding method is employed to eliminate all train operation constraints and output the rescheduled timetable. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of PS-SEGA. Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced evolutionary algorithms and their improved variants also using the improvements of PS-SEGA, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX cannot obtain optimal solutions within 10 min, PS-SEGA can provide quasi-optimal solutions in real time. Furthermore, compared with the other two heuristics algorithms (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can give the train departure sequence with a smaller total train delay