https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/88 2026-04-04T20:51:17Z 2026-04-04T20:51:17Z Wang, Yadi Guo, Xiaoding Jiang, Bingbing Ji, Jingyu Zhang, Jun https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/127373 2026-01-08T11:30:49Z 2026-06-01T00:00:00Z

Title: Adaptive group sparse multi-view classification method based on mutual information Authors: Wang, Yadi; Guo, Xiaoding; Jiang, Bingbing; Ji, Jingyu; Zhang, Jun Abstract: Multi-view classification has been widely utilized across numerous fields, such as image recognition. Existing methods typically integrate complementary information from multiple views by assigning specific weights to each view, thereby achieving multi-view fusion. However, these approaches generally impose identical penalties on all features, overlooking the varying importance of individual features within each view. Moreover, current multi-view learning approaches perform fusion at the view level, lacking effective utilization of intra-view information. To address these issues, we propose an adaptive group sparse multi-view classification method based on mutual information (AGSMC). In the proposed framework, features within each view are first grouped using class-label-specific mutual information. Subsequently, adaptive group weights and adaptive individual feature weights are derived to accurately reflect the significance of different groups and individual features for classification. Based on these adaptive weights, an adaptive group sparse penalty is formulated and integrated with the multi-view regression loss, thereby effectively promoting the roles of informative groups and discriminative features in multi-view data fusion. Additionally, a fast-converging iterative algorithm is developed to alternately optimize the proposed model efficiently. Extensive experimental evaluations demonstrate the stability and superior classification performance of the proposed AGSMC method compared to existing state-of-the-art techniques. © 2025 Elsevier Ltd

2026-06-01T00:00:00Z Qiu, Wen-Jin Chen, Wei-Neng Shi, Xuan-Li Zhang, Jun https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/127084 2025-11-18T01:30:33Z 2026-03-01T00:00:00Z

Title: A hybrid cooperative coevolution approach for robust medical supply chain logistics scheduling during an emerging epidemic Authors: Qiu, Wen-Jin; Chen, Wei-Neng; Shi, Xuan-Li; Zhang, Jun Abstract: Adequate medical supplies and prompt disposal of medical waste are crucial to effectively controlling an emerging epidemic. However, logistics scheduling within the medical supply chain can be severely impacted during such outbreaks. To optimize the distribution of medical supplies and the collection of resulting medical waste, we propose a hybrid cooperative coevolution approach for robust medical supply chain logistics scheduling during an emerging epidemic. First, we develop a hierarchical medical supply chain model in which various types of medical supplies flow from top to bottom. The susceptible-exposed-infected-vigilant epidemic model is also integrated into this model to predict the demand for medical supplies under conditions of uncertainty. Second, we hybridize a cooperative coevolution algorithm with an ant colony system algorithm to optimize the distribution of medical supplies and the collection of the resulting medical waste, respectively, while considering both operational cost and robustness. Finally, extensive experiments based on Monte Carlo simulations validate the effectiveness and efficiency of the proposed approach. The results indicate that the divide-and-conquer strategy employed by the cooperative coevolution algorithm can mitigate the impact of uncertainty while improving scalability.

2026-03-01T00:00:00Z Zhang, Xian-Rong Gong, Yue-Jiao Zhong, Yuan-Ting Huang, Ting Zhang, Jun https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/126815 2026-01-08T09:02:47Z 2026-02-01T00:00:00Z

Title: Large language model as meta-surrogate for offline data-driven many-task optimization: A proof-of-principle study Authors: Zhang, Xian-Rong; Gong, Yue-Jiao; Zhong, Yuan-Ting; Huang, Ting; Zhang, Jun Abstract: In offline data-driven optimization scenarios, where new evaluation data cannot be obtained in real time and each ground-truth evaluation is often costly, surrogate models become a key technology for reducing simulation or experimental overhead. This study proposes a novel meta-surrogate framework to assist many-task offline optimization, by leveraging the knowledge transfer strengths and emergent capabilities of large language models (LLMs). We formulate a unified framework for many-task fitness prediction, by defining a universal model with metadata to fit a group of problems. Fitness prediction is performed on metadata and decision variables, enabling efficient knowledge sharing across tasks and adaptability to new tasks. The LLM-based meta-surrogate treats fitness prediction as conditional probability estimation, employing a unified token sequence representation for task metadata, inputs, and outputs. This approach facilitates efficient inter-task knowledge sharing through shared token embeddings and captures complex task dependencies via many-task model training. Experimental results demonstrate the model's emergent generalization ability, including zero-shot performance on problems with unseen dimensions. When integrated into evolutionary transfer optimization (ETO), our framework supports dual-level knowledge transfer—at both the surrogate and individual levels—enhancing optimization efficiency and robustness. This work establishes a novel foundation for applying LLMs in surrogate modeling, offering a versatile solution for many-task optimization.

2026-02-01T00:00:00Z Wang, Chang-Long Wang, Zi-Jia Xue, Zhao-Feng Zhan, Zhi-Hui Kwong, Sam Zhang, Jun https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/126888 2026-01-05T02:00:27Z 2026-01-01T00:00:00Z

Title: Fuzzy Adaptive Multitask Optimization Authors: Wang, Chang-Long; Wang, Zi-Jia; Xue, Zhao-Feng; Zhan, Zhi-Hui; Kwong, Sam; Zhang, Jun Abstract: EMTO aims to optimize multiple tasks simultaneously. In recent years, various evolutionary multitask optimization (EMTO) algorithms based on knowledge transfer (KT) have been developed to utilize the information from other tasks and promote the optimization of the current task. However, most of them often use the fixed KT probability (ktp) and a single evolutionary search operator (ESO) during the evolution process, which lacks an adaption mechanism and cannot meet the different searching requirements among multiple tasks. Fuzzy system can effectively express the qualitative knowledge with unclear boundaries, which has good adaptability to nonindependent EMTO. Therefore, this article proposes a fuzzy adaptive multitask optimization (FAMTO), which employs a fuzzy adaptive transfer (FAT) strategy for intertask KT to achieve the adaptive adjustment of the ktp by designing a comprehensive evaluation in KT performance from two aspects, including the SR and the quality of transferred offspring. In FAT strategy, the fuzzy logical is employed to handle the interdependent relationships among multiple indicators, further achieving the more robust and adaptive ktp adjustment. In addition, an individual-based random selection (IRS) strategy is developed for each individual to choose the suitable ESO for intratask self-evolution in FAMTO. Experimental results show that FAMTO achieves significantly better performance than other state-of-the-art EMTO algorithms on two well-known multitask benchmarks, CEC17 and CEC22. Furthermore, FAMTO is applied to a real-world multitask planar kinematic arm control application, demonstrating its applicability. Finally, the extended experiments on many-task optimization problems (MaTOPs) illustrate the scalability of FAMTO.

2026-01-01T00:00:00Z