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学者姓名：冀俊忠
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Abstract ：
Solving dynamic multiobjective optimization problems (DMOPs) is extremely challenging due to the need to address multiple conflicting objectives that change over time. Transfer prediction- based strategies typically leverage solutions from historical environments to generate an initial population for a new environment. However, these strategies often overlook the similarity between the historical and new environments, which can negatively impact the quality of the initial population. To address this issue, we propose a similar environment transfer strategy. Firstly, we select Pareto-optimal solutions from a randomly generated population in the new environment to form a prior Pareto set (PS). The prior PS is expand by oversampling sparse solutions. Then, we apply the maximum mean discrepancy (MMD) to measure the discrepancy between the prior PS and the PS from each historical environment. The historical environment with the smallest MMD is identified as the similar environment. Finally, we use solutions from this similar environment to establish a kernelized easy transfer learning model, which is employed to predict the quality of random solutions in the new environment. The initial population is formed by combining excellent solutions predicted by the model with the prior PS. Experimental results demonstrate that the proposed strategy significantly outperforms several state-of-the-art strategies.
Keyword ：
Dynamic multiobjective optimization Dynamic multiobjective optimization Transfer learning Transfer learning Prediction-based strategy Prediction-based strategy Maximum mean discrepancy Maximum mean discrepancy
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| GB/T 7714 | Ji, Junzhong , Zhang, Xiaoyu , Yang, Cuicui et al. A similar environment transfer strategy for dynamic multiobjective optimization [J]. | INFORMATION SCIENCES , 2025 , 707 . |
| MLA | Ji, Junzhong et al. "A similar environment transfer strategy for dynamic multiobjective optimization" . | INFORMATION SCIENCES 707 (2025) . |
| APA | Ji, Junzhong , Zhang, Xiaoyu , Yang, Cuicui , Li, Xiang , Sui, Guangyuan . A similar environment transfer strategy for dynamic multiobjective optimization . | INFORMATION SCIENCES , 2025 , 707 . |
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Abstract ：
Multi-head attention (MA), which allows the model to jointly attend to crucial information from diverse representation subspaces through its heads, has yielded remarkable achievement in image captioning. However, there is no explicit mechanism to ensure MA attends to appropriate positions in diverse subspaces, resulting in overfocused attention for each head and redundancy between heads. In this paper, we propose a novel Intra- and Inter-Head Orthogonal Attention (I(2)OA) to efficiently improve MA in image captioning by introducing a concise orthogonal regularization to heads. Specifically, Intra-Head Orthogonal Attention enhances the attention learning of MA by introducing orthogonal constraint to each head, which decentralizes the object-centric attention to more comprehensive content-aware attention. Inter-Head Orthogonal Attention reduces the heads redundancy by applying orthogonal constraint between heads, which enlarges the diversity of representation subspaces and improves the representation ability for MA. Moreover, the proposed I(2)OA is flexible to combine with various multi-head attention based image captioning methods and improve the performances without increasing model complexity and parameters. Experiments on the MS COCO dataset demonstrate the effectiveness of the proposed model.
Keyword ：
Visualization Visualization Transformers Transformers multi-head attention (MA) multi-head attention (MA) Redundancy Redundancy Accuracy Accuracy orthogonal constraint orthogonal constraint Head Head Decoding Decoding Feature extraction Feature extraction Optimization Optimization Dogs Dogs Correlation Correlation Image captioning Image captioning
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| GB/T 7714 | Zhang, Xiaodan , Jia, Aozhe , Ji, Junzhong et al. Intra- and Inter-Head Orthogonal Attention for Image Captioning [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2025 , 34 : 594-607 . |
| MLA | Zhang, Xiaodan et al. "Intra- and Inter-Head Orthogonal Attention for Image Captioning" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 34 (2025) : 594-607 . |
| APA | Zhang, Xiaodan , Jia, Aozhe , Ji, Junzhong , Qu, Liangqiong , Ye, Qixiang . Intra- and Inter-Head Orthogonal Attention for Image Captioning . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2025 , 34 , 594-607 . |
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Abstract ：
In recent years, the discovery of brain effective connectivity (EC) networks through computational analysis of functional magnetic resonance imaging (fMRI) data has gained prominence in neuroscience and neuroimaging. However, owing to the influence of diverse factors during data collection and processing, fMRI data typically exhibit high noise and limited sample characteristics, consequently leading to the suboptimal performance of current methods. In this paper, we propose a novel brain effective connectivity discovery method based on meta-reinforcement learning, called MetaRLEC. The method mainly consists of three modules: actor, critic, and meta-critic. MetaRLEC first employs an encoder-decoder framework: The encoder utilizing a transformer converts noisy fMRI data into a state embedding, and the decoder employing bidirectional LSTM discovers brain region dependencies from the state and generates actions (EC networks). Then, a critic network evaluates these actions, incentivizing the actor to learn higher-reward actions amidst the high-noise setting. Finally, a meta-critic framework facilitates online learning of historical state-action pairs, integrating an action-value neural network and supplementary training losses to enhance the model's adaptability to small-sample fMRI data. We conduct comprehensive experiments on both simulated and real-world data to demonstrate the efficacy of our proposed method.
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| GB/T 7714 | Zhang, Zuozhen , Ji, Junzhong , Liu, Jinduo . MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity [J]. | THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 9 , 2024 : 10261-10269 . |
| MLA | Zhang, Zuozhen et al. "MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity" . | THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 9 (2024) : 10261-10269 . |
| APA | Zhang, Zuozhen , Ji, Junzhong , Liu, Jinduo . MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity . | THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 9 , 2024 , 10261-10269 . |
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Abstract ：
Using deep models to classify brain functional networks (BFNs) for the auxiliary diagnosis and treatment of brain diseases has become increasingly popular. However, the unexplainability of deep models has seriously hindered their applications in computer-aided diagnosis. In addition, current explanation methods mostly focus on natural images, which cannot be directly used to explain the deep model for BFN classification. In this paper, we propose a novel concept-level causal explanation method for BFN classification called CLCEM. First, CLCEM employs the causal learning method to extract concepts that are meaningful to humans from BFNs. Second, it aggregates the same concepts to obtain the contribution of each concept to the model output. Finally, CLCEM adds the contribution of each concept to make a diagnosis. The experimental results show that our CLCEM can not only accurately identify brain regions related to specific brain diseases but also make decisions based on the concepts of these brain regions, which enables humans to understand the decision-making process without performance degradation.
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| GB/T 7714 | Liu, Jinduo , Wang, Feipeng , Ji, Junzhong . Concept-Level Causal Explanation Method for Brain Function Network Classification [J]. | PROCEEDINGS OF THE THIRTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2024 , 2024 : 3087-3096 . |
| MLA | Liu, Jinduo et al. "Concept-Level Causal Explanation Method for Brain Function Network Classification" . | PROCEEDINGS OF THE THIRTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2024 (2024) : 3087-3096 . |
| APA | Liu, Jinduo , Wang, Feipeng , Ji, Junzhong . Concept-Level Causal Explanation Method for Brain Function Network Classification . | PROCEEDINGS OF THE THIRTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2024 , 2024 , 3087-3096 . |
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Abstract ：
近年来,基于深度学习的脑功能连接分类方法成为脑科学中的研究热点。为了进一步获得具有判别性的大脑连接特征,进而提升脑功能连接的分类准确率,本文提出了一种基于对比学习的卷积神经网络脑功能连接分类方法。对比学习是一种特殊的自监督学习框架,通过在特征空间中对正样本与负样本进行对比,充分挖掘不同样本间的差异性。首先,提出一种融合多视角信息的对比学习框架,采用实例-实例的对比学习挖掘样本间的差异性,并利用实例-原型的对比学习挖掘样本与样本簇之间的差异性。其次,将多视角对比学习与目标分类任务联合训练,增强所学特征的判别性,提升卷积神经网络的分类性能。本文使用ABIDE-I数据集进行实验,结果表明,所提方法能够使脑功能连接的分类效果得到有效提升,并且能够对重要的功能连接与脑区进行准确识别。
Keyword ：
对比学习 对比学习 脑功能连接分类 脑功能连接分类 卷积神经网络 卷积神经网络
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| GB/T 7714 | 何庆钊 , 于乐 , 冀俊忠 et al. 基于对比学习的卷积神经网络脑功能连接分类方法 [C] //2023中国自动化大会 . 2023 . |
| MLA | 何庆钊 et al. "基于对比学习的卷积神经网络脑功能连接分类方法" 2023中国自动化大会 . (2023) . |
| APA | 何庆钊 , 于乐 , 冀俊忠 , 雷名龙 . 基于对比学习的卷积神经网络脑功能连接分类方法 2023中国自动化大会 . (2023) . |
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Abstract ：
A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.
Keyword ：
causal protein signaling networks causal protein signaling networks CBNs fusion CBNs fusion causal biological networks causal biological networks parallel ant colony optimization parallel ant colony optimization pheromone fusion pheromone fusion causal brain networks causal brain networks
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| GB/T 7714 | Zhai, Jihao , Ji, Junzhong , Liu, Jinduo . Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm [J]. | BIOENGINEERING-BASEL , 2023 , 10 (8) . |
| MLA | Zhai, Jihao et al. "Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm" . | BIOENGINEERING-BASEL 10 . 8 (2023) . |
| APA | Zhai, Jihao , Ji, Junzhong , Liu, Jinduo . Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm . | BIOENGINEERING-BASEL , 2023 , 10 (8) . |
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Abstract ：
Deep forest is a new multi-layer ensemble model, where the high time costs and storage requirements inhibit its large-scale application. However, current deep forest pruning methods used to alleviate these drawbacks do not consider its cascade coupling characteristics. Therefore, we propose a tri-objective optimization-based cascade ensemble pruning (TOOCEP) algorithm for it. Concretely, we first present a tri-objective optimization-based single-layer pruning (TOOSLP) method to prune its single-layer by simultaneously optimizing three objectives, namely accuracy, independent diversity, and coupled diversity. Particularly, the coupled diversity is designed for deep forest to deal with the coupling relationships between its adjacent layers. Then, we perform TOOSLP in a cascade framework to prune the deep forest layer-by-layer. Experimental results on 15 UCI datasets show that TOOCEP outperforms several state-ofthe-art methods in accuracy and pruned rate, which significantly reduces the storage space and accelerate the prediction speed of deep forest. & COPY; 2023 Elsevier Ltd. All rights reserved.
Keyword ：
Deep forest Deep forest Ensemble pruning Ensemble pruning Ensemble learning Ensemble learning Coupled diversity Coupled diversity Multi-objective optimization Multi-objective optimization
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| GB/T 7714 | Ji, Junzhong , Li, Junwei . Tri-objective optimization-based cascade ensemble pruning for deep forest [J]. | PATTERN RECOGNITION , 2023 , 143 . |
| MLA | Ji, Junzhong et al. "Tri-objective optimization-based cascade ensemble pruning for deep forest" . | PATTERN RECOGNITION 143 (2023) . |
| APA | Ji, Junzhong , Li, Junwei . Tri-objective optimization-based cascade ensemble pruning for deep forest . | PATTERN RECOGNITION , 2023 , 143 . |
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Abstract ：
本发明公开了一种基于蚁群优化算法的电动汽车充电站选址方法，基于覆盖优先级的解构造策略，使蚁群获得解元素的全局构造顺序，从而合理安排选址对充电需求的覆盖顺序，避免显性选址重叠；采用基于轨迹覆盖关系的启发信息调整策略，在迭代中根据历史最优解来调整启发信息降低出现隐性选址重叠的概率；在每轮迭代中，通过CP‑SC构造充电设施选址问题的解，每构造一个可行解，都会进行一次局部信息素更新；在每轮迭代结束前，根据历史最优解的目标函数值即充电需求强度来进行全局信息素更新，并通过TCHA策略调整启发信息；迭代达到终止条件后，输出历史最优解。本方法不仅降低选址重叠率，还提高了充电需求的覆盖量。
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| GB/T 7714 | 冀俊忠 , 刘钺锋 , 杨翠翠 . 一种基于蚁群优化算法的电动汽车充电站选址方法 : CN202310759998.2[P]. | 2023-06-27 . |
| MLA | 冀俊忠 et al. "一种基于蚁群优化算法的电动汽车充电站选址方法" : CN202310759998.2. | 2023-06-27 . |
| APA | 冀俊忠 , 刘钺锋 , 杨翠翠 . 一种基于蚁群优化算法的电动汽车充电站选址方法 : CN202310759998.2. | 2023-06-27 . |
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Abstract ：
本发明公开了一种基于深度哈希互学习的脑网络分类方法，包括：数据预处理和脑功能网络构建；脑网络数据划分；基于深度哈希学习的个体特征提取；基于深度哈希学习的群体特征提取；基于哈希码的互学习；基于哈希码的分类。本发明首次考虑到群体脑网络中的表型标签差异，采用表型标签构建群体脑网络关系图，提出一种基于GCN的深度哈希学习模型提取脑网络的群体特征；并考虑到脑网络个体特征和群体特征间的关系，采用基于深度哈希互学习的脑网络分类方法通过个体特征和群体特征之间的互学习来增强特征的辨别能力。本方法与其他方法相比，分类性能更优。
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| GB/T 7714 | 冀俊忠 , 张雅琴 . 一种基于深度哈希互学习的脑网络分类方法 : CN202310522896.9[P]. | 2023-05-10 . |
| MLA | 冀俊忠 et al. "一种基于深度哈希互学习的脑网络分类方法" : CN202310522896.9. | 2023-05-10 . |
| APA | 冀俊忠 , 张雅琴 . 一种基于深度哈希互学习的脑网络分类方法 : CN202310522896.9. | 2023-05-10 . |
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Abstract ：
本发明公开了一种基于超维计算辅助的车辆耐撞性多目标优化方法，首先将所有已被评估的车辆结构均匀划分为规模相同的“优质结构集合”和“劣质结构集合”，为构建分类模型提供平衡的训练数据。使用一种近似结构厚度值编码方式，将所有结构编码为相应的超向量。将这些超向量根据所属类别按位相加来构建分类模型，得到表示“优质结构”和“劣质结构”的类别超向量。最后，使用遗传算子新生成的候选结构以相同的方式编码为超向量，并使用余弦相似度计算与类别超向量的相似性来预测候选结构的类别。预测类别为“优质结构”的候选结构会被筛选出来并被真实的目标函数评估。本方法在求解标准测试问题集和车辆耐撞性优化问题时有着更好的效果。
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| GB/T 7714 | 冀俊忠 , 吴同轩 , 杨翠翠 . 一种基于超维计算辅助的车辆耐撞性多目标优化方法 : CN202310600838.3[P]. | 2023-05-25 . |
| MLA | 冀俊忠 et al. "一种基于超维计算辅助的车辆耐撞性多目标优化方法" : CN202310600838.3. | 2023-05-25 . |
| APA | 冀俊忠 , 吴同轩 , 杨翠翠 . 一种基于超维计算辅助的车辆耐撞性多目标优化方法 : CN202310600838.3. | 2023-05-25 . |
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