This study proposes MSATE-Net for next-day stock index forecasting. The model combines parallel one-dimensional convolutions with receptive fields of 3, 7, and 15 trading days, a bidirectional LSTM operating entirely inside a historical lookback window, sample-dependent temporal attention, and a residual regularized prediction head. Here, “bidirectional” denotes paired processing of the same observed window; it does not assert time-reversal invariance of financial prices or access to observations after the forecast origin. The globally learned attention temperature controls overall selectivity and is not described as a regime-specific adaptive parameter. Experiments use S&P 500, CSI 300, and Nikkei 225 data; persistence and drift benchmarks; recent forecasting architectures; five-seed uncertainty estimates; expanding-window tests; return and directional metrics; and Diebold–Mariano comparisons. The revised evidence supports lower price-level errors, while directional and significance results are mixed across markets. Because a separate model is fitted in each market, the findings establish cross-market consistency rather than transfer learning.
In recent years, constrained multi-objective optimization problems(CMOPs) remain challenging due to the complex structure of feasible regions, the difficulty of balancing convergence and diversity, and the lack of adaptive operator scheduling mechanisms. To address these issues, this paper proposes a hierarchical reinforcement learning–based subtask-coordinated scheduling method for constrained multi-objective evolutionary algorithm (HRL-SCMOE). The proposed framework employs a two-level architecture, where a high-level agent dynamically schedules subtasks–such as forward-oriented exploration, feasibility-driven exploitation, and diversity guidance–according to the environmental state, while a low-level agent adaptively selects variation operators tailored to each subtask. Both agents are trained using Double Deep Q-Networks (Double DQN) and Prioritized Experience Replay (PER) to enhance stability, sample efficiency, and value estimation reliability. Moreover, the algorithm constructs a set of collaborative information pools targeting different search objectives to maintain balanced exploration between feasible and infeasible regions. An adaptive reward mechanism and soft target updates are also incorporated to improve robustness in hierarchical policy learning. Experimental results on three benchmark test suites and four real-world application domains demonstrate that the proposed method consistently outperforms nine state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs) in terms of convergence, feasibility, and diversity, thereby confirming its effectiveness and strong general applicability.
Lu-Peng Hao, Yahui Shan, Guang-Yin Jin et al.· Journal of King Saud Univers...· 0 citations
Experimental evaluations under various IWSN environmental monitoring scenarios show that CCNCSO-CRP outperforms state-of-the-art protocols including LEACH-C, VSSLS-SIACR, and FOAEAUC-SARP, and provides reliable technical support for high-performance environmental parameter detection and intelligent early warning systems.
