Attraction-Repulsion Optimization Algorithm (AROA) is a recently proposed meta-heuristic algorithm known for its simplicity, ease of implementation, and robustness. However, AROA may converge to local optima when applied to complex optimization problems. To address this limitation, we propose an enhanced version called the Differential Cauchy Tangent Attraction-Repulsion Optimization Algorithm (DCTAROA). First, we propose a mutation operator based on a tangent flight mutation strategy and a dimension decision mechanism using the inverse cumulative distribution function of the Cauchy distribution. The tangent flight mutation enhances the local search capability and accelerates convergence, while the dimension decision strategy of the Cauchy distribution inverse cumulative function increases population diversity and improves exploration efficiency. Subsequently, we integrate Differential Evolution (DE) as a local search mechanism to strengthen the global optimization performance of AROA. To evaluate the proposed algorithm, we compare it with 15 state-of-the-art algorithms on 29 CEC2017 benchmark functions across various dimensions. Experimental results demonstrate that DCTAROA outperforms the compared algorithms in terms of solution accuracy, stability, convergence speed, and statistical significance based on the Wilcoxon rank-sum test. Furthermore, we apply DCTAROAto three practical engineering design problems. The results confirm that DCTAROA effectively explores the search space and yields competitive solutions, thereby validating its practical applicability.
Fang Feng, Kuan-Ching Li, Mingjiang Cai et al.· International Journal of Int...· 0 citations
Multimodal Sentiment Analysis leverages the fusion of heterogeneous data to achieve fine-grained emotional understanding, which finds extensive application in large-scale public opinion monitoring and data mining. However, existing methods face two key challenges: (1) cross-modal alignment suffers from redundancy and semantic drift without explicit modeling of sentiment-critical cues, inducing spurious correlations; and (2) heterogeneous representation spaces lead to imbalanced modality contributions, particularly under weak image–text correlation or sentiment inconsistency. To address these challenges, we propose an explicit sentiment-guided alignment and multi-dimensional cross-modal mutual supervisionbased model for multimodal sentiment analysis. The model primarily employs a fine-grained sentiment–saliency directed alignment mechanism, which leverages bidirectional cross-attention to couple textual sentiment cues with visual saliency, enabling precise localization of sentiment-relevant regions. Furthermore, we introduce a tripartite strong contrastive learning strategy to mitigate distribution discrepancies between heterogeneous modalities within a shared latent space, thereby enhancing cross-modal coherence and complementarity. Finally, we design a noiserobust gating-based fusion module, which, together with text augmentation and deep supervision, facilitates effective joint optimization. Experimental results show that SAMS-M obtains the best results on MVSA-Single and MSD and remains competitive on the noisier MVSA-Multiple benchmark; thus, the evidence supports strong but dataset-dependent performance rather than uniform state-of-the-art superiority.
Shi-Shu Qi, Yulei Zhang, Siyang Zhang et al.· Computer Science and Informa...· 0 citations
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