Expert Systems with Applications最新文献_第2页

Goal-driven long-term marine vessel trajectory prediction with a memory-enhanced network 利用记忆增强网络进行目标驱动的长期海洋船舶轨迹预测

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2024-11-12 DOI: 10.1016/j.eswa.2024.125715

Xiliang Zhang , Jin Liu , Chengcheng Chen , Lai Wei , Zhongdai Wu , Wenjuan Dai

Enhancing the precision of marine vessel trajectory prediction (VTP) is crucial for collision avoidance, intelligent navigation, and crisis alert in maritime safety. Most RNN-based methods typically face memory weakening issues during long-sequence propagation, leading to the discarding of some key features and significant predictive error accumulation over extended time intervals. Moreover, they struggle to forecast those complex trajectories involving abnormal maneuvers such as sudden acceleration or deceleration, sharp turns, or U-turns, resulting in poor generalization capabilities. To address these pivotal challenges, this paper proposes a novel Memory-Enhanced Network (MENet) for VTP, catering to intricate sailing intention modeling with long-term motion pattern perception. Specifically, we design an embeddable memory-enhanced block (MEB) that adaptively aggregates memory vectors across multiple temporal scales to assist in better prediction without disrupting the original backbone structure. Also, a goal-driven vessel trajectory decoder (GD-VTD) is developed to facilitate reliable model inferences by combining vessel type and destination variables as guidance information. Furthermore, we reconstruct the traditional loss function based on relative distance metrics, incorporating predicted headings into the optimization process to generate consistent trajectories that comply with realistic vessel dynamics. Ultimately, MENet could learn diverse sailing intentions by assembling the above parts to predict long-term marine vessel trajectories. Extensive experimental results on Automatic Identification System (AIS) datasets from three coastal regions in the US demonstrate that our model exhibits superior accuracy and robustness compared to other baselines. Specifically, on the Everglades Port (EP) dataset, our method reduces MAE, RMSE, and MAPE errors by 7.25%, 7.82%, and 7.62%, respectively, compared to the existing best results during this experiment. This is another piece of evidence for the effectiveness of goal-driven trajectory prediction in real-world maritime settings.

提高海洋船舶轨迹预测（VTP）的精度对于避免碰撞、智能导航和海上安全危机预警至关重要。大多数基于 RNN 的方法在长序列传播过程中通常会面临记忆弱化问题，导致一些关键特征被丢弃，并在较长的时间间隔内积累大量预测误差。此外，它们很难预测那些涉及异常机动（如突然加速或减速、急转弯或 U 形转弯）的复杂轨迹，导致泛化能力差。为了应对这些关键挑战，本文提出了一种用于 VTP 的新型记忆增强网络（MENet），以满足复杂的航行意图建模和长期运动模式感知的需要。具体来说，我们设计了一个可嵌入的记忆增强块（MEB），它能在多个时间尺度上自适应地聚合记忆向量，从而在不破坏原始主干结构的情况下帮助进行更好的预测。此外，我们还开发了目标驱动的船只轨迹解码器（GD-VTD），通过将船只类型和目的地变量作为指导信息，促进可靠的模型推断。此外，我们基于相对距离指标重建了传统的损失函数，将预测航向纳入优化过程，以生成符合现实船舶动态的一致轨迹。最终，MENet 可以通过组合上述部分来学习不同的航行意图，从而预测长期的海洋船舶轨迹。在美国三个沿海地区的自动识别系统（AIS）数据集上进行的大量实验结果表明，与其他基准相比，我们的模型具有更高的准确性和鲁棒性。具体而言，在 Everglades 港口（EP）数据集上，与该实验中现有的最佳结果相比，我们的方法将 MAE、RMSE 和 MAPE 误差分别降低了 7.25%、7.82% 和 7.62%。这再次证明了目标驱动轨迹预测在实际海事环境中的有效性。

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引用次数: 0

LRMM: Low rank multi-scale multi-modal fusion for person re-identification based on RGB-NI-TI LRMM：基于 RGB-NI-TI 的低等级多尺度多模态融合技术用于人员再识别

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2024-11-12 DOI: 10.1016/j.eswa.2024.125716

Di Wu , Zhihui Liu , Zihan Chen , Shenglong Gan , Kaiwen Tan , Qin Wan , Yaonan Wang

Person Re-identification is a crucial task in video surveillance, aiming to match person images from non-overlapping camera views. Recent methods introduce the Near-Infrared (NI) modality to alleviate the limitations of traditional single visible light modality under low-light conditions, while they overlook the importance of modality-related information. To incorporate more additional complementary information to assist traditional person re-identification tasks, in this paper, a novel RGB-NI-TI multi-modal person re-identification approach is proposed. First, we design a multi-scale multi-modal interaction module to facilitate cross-modal information fusion across multiple scales. Secondly, we propose a low-rank multi-modal fusion module that leverages the feature and weight parallel decomposition and then employs low-rank modality-specific factors for multimodal fusion. It aims to make the model more efficient in fusing multiple modal features while reducing complexity. Finally, we propose a multiple modalities prototype loss to supervise the network jointly with the cross-entropy loss, enforcing the network to learn modality-specific information by improving the intra-class cross-modality similarity and expanding the inter-class difference. The experimental results on benchmark multi-modal Re-ID datasets (RGBNT201, RGBNT100, MSVR310) and constructed person Re-ID datasets (multimodal version Market1501, PRW) validate the effectiveness of the proposed approach compared with the state-of-the-art methods.

人员再识别是视频监控中的一项重要任务，旨在匹配非重叠摄像机视图中的人员图像。最近的方法引入了近红外（NI）模态，以缓解传统单一可见光模态在弱光条件下的局限性，但这些方法忽略了模态相关信息的重要性。为了加入更多补充信息来辅助传统的人员再识别任务，本文提出了一种新颖的 RGB-NI-TI 多模态人员再识别方法。首先，我们设计了一个多尺度多模态交互模块，以促进跨尺度的跨模态信息融合。其次，我们提出了低阶多模态融合模块，该模块利用特征和权重平行分解，然后采用低阶模态特定因子进行多模态融合。其目的是使模型在融合多模态特征时更加高效，同时降低复杂性。最后，我们提出了一种多模态原型损失，与交叉熵损失共同监督网络，通过提高类内交叉模态相似度和扩大类间差异来强制网络学习特定模态信息。在基准多模态再识别数据集（RGBNT201、RGBNT100、MSVR310）和构建的人物再识别数据集（多模态版本 Market1501、PRW）上的实验结果验证了所提出的方法与最先进方法相比的有效性。

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引用次数: 0

To disclose or to conceal? Comparison of different disclosure policies in queues with loss-averse customers 披露还是隐瞒？比较有损失规避型顾客的排队过程中的不同披露政策

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2024-11-12 DOI: 10.1016/j.eswa.2024.125635

Jian Cao , Yongjiang Guo

In many service industries, information disclosure about the product can alleviate customers’ loss aversion induced by uncertain product valuation. In this paper, we consider a single-server queueing system in which the manager who privately learns the valuation information discloses the valuation information strategically to loss-averse customers. We investigate the impact of the customers’ loss aversion on the system’s equilibrium arrival rate and the manager’s optimal disclosure policy. We find that loss aversion restrains customers from joining the queue. Surprisingly, we find that there is no one disclosure policy that always prevails over other disclosure policies. Specifically, the full disclosure policy is optimal only when the valuation is large and the degree of loss aversion is moderate. The full non-disclosure policy is optimal when the degree of loss aversion is too large or too small, or the valuation is small. The threshold disclosure policy is optimal when the valuation and the degree of loss aversion are moderate. Furthermore, under the threshold disclosure policy, the increasing degree of loss aversion makes managers be more reluctant to disclose the valuation.

在许多服务行业中，披露产品信息可以减轻客户因产品估值不确定而产生的损失厌恶情绪。在本文中，我们考虑了一个单服务器排队系统，在该系统中，私下了解估值信息的经理会策略性地向损失规避型客户披露估值信息。我们研究了客户的损失规避对系统均衡到达率和经理的最优披露策略的影响。我们发现，损失厌恶会抑制客户加入队列。令人惊讶的是，我们发现没有一种披露政策总是优于其他披露政策。具体来说，只有当估值较大且损失厌恶程度适中时，完全披露政策才是最优的。当损失规避程度过大或过小，或者估值较小时，完全不披露政策是最优的。当估值和损失规避程度适中时，阈值披露政策是最优的。此外，在临界披露政策下，损失规避程度的增加会使管理者更不愿意披露估值。

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引用次数: 0

AFDFusion: An adaptive frequency decoupling fusion network for multi-modality image AFDFusion：用于多模态图像的自适应频率解耦融合网络

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2024-11-12 DOI: 10.1016/j.eswa.2024.125694

Chengchao Wang , Zhengpeng Zhao , Qiuxia Yang , Rencan Nie , Jinde Cao , Yuanyuan Pu

The multi-modality image fusion goal is to create a single image that provides a comprehensive scene description and conforms to visual perception by integrating complementary information about the merits of the different modalities, e.g., salient intensities of infrared images and detail textures of visible images. Although some works explore decoupled representations of multi-modality images, they struggle with complex nonlinear relationships, fine modal decoupling, and noise handling. To cope with this issue, we propose an adaptive frequency decoupling module to perceive the associative invariant and inherent specific among cross-modality by dynamically adjusting the learnable low frequency weight of the kernel. Specifically, we utilize a contrastive learning loss for restricting the solution space of feature decoupling to learn representations of both the invariant and specific in the multi-modality images. The underlying idea is that: in decoupling, low frequency features, which are similar in the representation space, should be pulled closer to each other, signifying the associative invariant, while high frequencies are pushed farther away, also indicating the intrinsic specific. Additionally, a multi-stage training manner is introduced into our framework to achieve decoupling and fusion. Stage I, MixEncoder and MixDecoder with the same architecture but different parameters are trained to perform decoupling and reconstruction supervised by the contrastive self-supervised mechanism. Stage II, two feature fusion modules are added to integrate the invariant and specific features and output the fused image. Extensive experiments demonstrated the proposed method superiority over the state-of-the-art methods in both qualitative and quantitative evaluation on two multi-modal image fusion tasks.

多模态图像融合的目标是通过整合不同模态的互补信息（如红外图像的突出强度和可见光图像的细节纹理），创建一幅能够提供全面场景描述并符合视觉感知的图像。虽然有些研究探索了多模态图像的解耦表征，但它们在复杂的非线性关系、精细的模态解耦和噪声处理等方面都存在困难。为了解决这个问题，我们提出了一种自适应频率解耦模块，通过动态调整核的可学习低频权重，来感知跨模态之间的关联不变性和固有特异性。具体来说，我们利用对比学习损失来限制特征解耦的解空间，以学习多模态图像中的不变性和特异性表征。其基本思想是：在解耦过程中，在表征空间中相似的低频特征应被拉近，表示关联不变性，而高频特征则被推远，也表示内在特异性。此外，我们的框架还引入了多阶段训练方式，以实现解耦和融合。在第一阶段，对具有相同架构但不同参数的混合编码器（MixEncoder）和混合解码器（MixDecoder）进行训练，在对比度自监督机制的监督下进行解耦和重构。第二阶段，添加两个特征融合模块，以整合不变特征和特定特征，并输出融合图像。广泛的实验证明，在两个多模态图像融合任务的定性和定量评估中，所提出的方法都优于最先进的方法。

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引用次数: 0

A method based on hybrid cross-multiscale spectral-spatial transformer network for hyperspectral and multispectral image fusion 基于跨多尺度光谱-空间变换器混合网络的高光谱和多光谱图像融合方法

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2024-11-10 DOI: 10.1016/j.eswa.2024.125742

Yingxia Chen , Mingming Wei , Yan Chen

Convolutional neural networks (CNNs) have made a significant contribution to hyperspectral image (HSI) generation. However, capturing long-range dependencies can be challenging with CNNs due to the limitations of their local receptive fields, which can lead to distortions in fused images. Transformers excel at capturing long-range dependencies but have limited capacity for handling fine details. Additionally, prior work has often overlooked the extraction of global features during the image preprocessing stage, resulting in the potential loss of fine details. To address these issues, we propose a hybrid cross-multiscale spectral-spatial Transformer (HCMSST) that combines the advantages of CNNs in feature extraction and Transformers in capturing long-range dependencies. To fully extract and retain local and global information in the shallow feature extraction phase, the network incorporates CNNs with a staggered cascade-dense residual block (SCDRB). This block employs staggered residuals to establish direct connections both within and between branches and integrates attention modules to enhance the response to important features. This approach facilitates unrestricted information exchange and fosters deeper feature representations. To address the limitations of Transformer in processing fine details, we introduce multiscale spatial-spectral coding-decoding structures to obtain comprehensive spatial-spectral features, which are utilized to capture the long-range dependencies via the cross-multiscale spectral-spatial Transformer (CMSST). Further, the CMSST incorporates a cross-level dual-stream feature interaction strategy that integrates spatial and spectral features from different levels and then feeds the fused features back to their corresponding branches for information interaction. Experimental results indicate that the proposed HCMSST achieves superior performance compared to many state-of-the-art (SOTA) methods. Specifically, HCMSST reduces the ERGAS metric by 3.05% compared to the SOTA methods on the CAVE dataset, while on the Harvard dataset, it achieves a 2.69% reduction in ERGAS compared to the SOTA results.

卷积神经网络（CNN）为高光谱图像（HSI）生成做出了重大贡献。然而，由于其局部感受野的限制，使用卷积神经网络捕捉远距离相关性可能具有挑战性，这可能导致融合图像失真。变换器擅长捕捉长距离依赖关系，但处理精细细节的能力有限。此外，之前的工作往往忽略了在图像预处理阶段提取全局特征，从而可能导致精细细节的丢失。为了解决这些问题，我们提出了一种混合跨多尺度光谱空间变换器（HCMSST），它结合了 CNN 在特征提取方面的优势和变换器在捕捉长距离相关性方面的优势。为了在浅层特征提取阶段充分提取并保留局部和全局信息，该网络结合了带有交错级联密集残差块（SCDRB）的 CNN。该块采用交错残差，在分支内部和分支之间建立直接连接，并集成注意力模块，以增强对重要特征的响应。这种方法有利于无限制的信息交换，并促进更深入的特征表征。为了解决变换器在处理精细细节方面的局限性，我们引入了多尺度空间-光谱编码-解码结构，以获得全面的空间-光谱特征，并通过跨多尺度光谱-空间变换器（CMSST）利用这些特征捕捉长程依赖关系。此外，CMSST 还采用了跨级别双流特征交互策略，将来自不同级别的空间和频谱特征整合在一起，然后将融合后的特征反馈给相应的分支机构进行信息交互。实验结果表明，与许多最先进的（SOTA）方法相比，所提出的 HCMSST 实现了更优越的性能。具体来说，在 CAVE 数据集上，与 SOTA 方法相比，HCMSST 的 ERGAS 指标降低了 3.05%；而在哈佛数据集上，与 SOTA 方法相比，HCMSST 的 ERGAS 指标降低了 2.69%。

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引用次数: 0

Negative sampling strategy based on multi-hop neighbors for graph representation learning 基于多跳邻居的负采样策略用于图表示学习

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2024-11-10 DOI: 10.1016/j.eswa.2024.125688

Kaiyu Zhang, Guoming Sang, Junkai Cheng, Zhi Liu, Yijia Zhang

Contrastive learning (CL) has recently achieved significant success in the field of recommendation system. However, current studies mainly focus on obtaining high-quality positive samples and focus less on selecting negative samples. In existing recommendation system based on graph contrastive learning, most methods select negative samples by randomly selecting samples that have not interacted with the target node. Although random negative sampling is easy to implement and has wide applicability, it may lead to problems such as unbalanced data distribution and selection of false negative samples, which can degrade model performance. To address the above issues, we propose a novel negative sampling strategy called the Multi-hop Neighbors Negative Sampling method, named NSHN. Specifically, we select the information of 3-hop neighbors of each node as candidate negative samples. In addition, to reduce the impact of false negative noise on negative samples, we propose an adaptive denoising training strategy that adaptively prunes noise interactions during training. Experimental results demonstrate that our method performs well on four datasets and outperforms graph contrastive learning methods that use random negative sampling. The source code is available at: https://github.com/zhangkaiyu-zky/NSHN

对比学习（CL）最近在推荐系统领域取得了巨大成功。然而，目前的研究主要集中在获取高质量的正样本上，而较少关注负样本的选择。在现有的基于图对比学习的推荐系统中，大多数方法都是通过随机抽取与目标节点没有交互的样本来选择负样本。虽然随机负抽样易于实现且适用性广，但它可能会导致数据分布不平衡和选择假负样本等问题，从而降低模型性能。为解决上述问题，我们提出了一种新颖的负采样策略，称为多跳邻居负采样法（NSHN）。具体来说，我们选择每个节点的 3 跳邻居信息作为候选负样本。此外，为了减少假负噪声对负样本的影响，我们提出了一种自适应去噪训练策略，在训练过程中自适应地修剪噪声交互。实验结果表明，我们的方法在四个数据集上表现良好，优于使用随机负采样的图对比学习方法。源代码可在以下网址获取： https://github.com/zhangkaiyu-zky/NSHN

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引用次数: 0

TSIDS: Spatial–temporal fusion gating Multilayer Perceptron for network intrusion detection TSIDS：用于网络入侵检测的时空融合门控多层感知器

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2024-11-10 DOI: 10.1016/j.eswa.2024.125687

Jie Fu , Lina Wang , Jianpeng Ke , Kang Yang , Rongwei Yu

Due to the heterogeneous and dynamic nature of networks, modeling spatiotemporal correlations has become a trend. Although spatiotemporal-based network intrusion detection systems (NIDSs) enhance the performance of intrusion classification, they still suffer from inadequacies in the multi-classification of intrusions and model generalization ability. First, the static attack topologies of network traffic always ignore some important information; Second, the interaction between spatial and temporal dimensions is rarely considered. To mitigate these issues, this paper proposes TSIDS, a spatiotemporal analysis-based approach that extracts the interaction of network behaviors for intrusion detection. TSIDS combines the spatial analysis module to extract spatial information between different events, and the temporal analysis module to learn the temporal dependencies from historical traffic data. To model spatial correlations of temporal features, we propose a feature fusion module based on our customized gating Multilayer Perceptron (cgMLP). The experimental results on four datasets show that our work is effective in intrusion detection, especially multi-classification, and outperforms other baseline methods.

由于网络的异构性和动态性，时空关联建模已成为一种趋势。基于时空的网络入侵检测系统（NIDS）虽然提高了入侵分类的性能，但在入侵的多分类和模型泛化能力方面仍存在不足。首先，网络流量的静态攻击拓扑总是会忽略一些重要信息；其次，很少考虑空间维度和时间维度之间的交互作用。为了解决这些问题，本文提出了一种基于时空分析的方法--TSIDS，它能提取网络行为的交互作用，用于入侵检测。TSIDS 结合了空间分析模块和时间分析模块，空间分析模块用于提取不同事件之间的空间信息，时间分析模块用于从历史流量数据中学习时间依赖关系。为了对时间特征的空间相关性进行建模，我们提出了基于定制门控多层感知器（cgMLP）的特征融合模块。在四个数据集上的实验结果表明，我们的工作在入侵检测（尤其是多分类）方面非常有效，并且优于其他基线方法。

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引用次数: 0

CoSEF-DBP: Convolution scope expanding fusion network for identifying DNA-binding proteins through bilingual representations CoSEF-DBP：通过双语表征识别 DNA 结合蛋白的卷积范围扩展融合网络

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2024-11-10 DOI: 10.1016/j.eswa.2024.125763

Hua Zhang , Xiaoqi Yang , Pengliang Chen , Cheng Yang , Bi Chen , Bo Jiang , Guogen Shan

Precisely recognizing DNA-binding proteins (DBPs) from sequences is crucial for a profound comprehension of the mechanisms governing protein-DNA interactions in various cellular processes. However, traditional in-silico methods for DBP identification encounter several challenges, such as time-consuming evolutionary modeling based on multiple sequence alignments, and intricate feature engineering associated with machine or deep learning approaches. In this paper, we introduce a novel end-to-end predictor for identifying DNA-binding proteins without intricate feature engineering, which innovatively enriches the semantics of amino acid sequences through the fusion of bilingual representations derived from distinct language models. We further design a convolution scope expanding (CoSE) module to widen the receptive fields of convolution kernels, thereby forming protein-level CoSE representation sequences. These representations are subsequently integrated via BiLSTM in conjunction with a simplified capsule network, enhancing the hierarchical feature extraction capability. Extensive experiments confirm that our model surpasses existing baselines across diverse benchmark datasets, notably achieving at least a 5.1% improvement in MCC value on the UniSwiss dataset.

从序列中精确识别 DNA 结合蛋白（DBP）对于深入理解各种细胞过程中蛋白质-DNA 的相互作用机制至关重要。然而，传统的用于识别 DBP 的内测方法遇到了一些挑战，例如基于多序列比对的耗时进化建模，以及与机器或深度学习方法相关的复杂特征工程。在本文中，我们介绍了一种无需复杂特征工程就能识别 DNA 结合蛋白的新型端到端预测器，该预测器通过融合从不同语言模型中提取的双语表征，创新性地丰富了氨基酸序列的语义。我们进一步设计了一个卷积范围扩展（CoSE）模块，以拓宽卷积核的感受野，从而形成蛋白质级的 CoSE 表示序列。这些表示序列随后通过 BiLSTM 与简化的胶囊网络进行整合，从而增强了分层特征提取能力。广泛的实验证实，我们的模型在各种基准数据集上超越了现有的基线，尤其是在 UniSwiss 数据集上的 MCC 值至少提高了 5.1%。

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引用次数: 0

A cost-minimized two-stage three-way dynamic consensus mechanism for social network-large scale group decision-making: Utilizing K-nearest neighbors for incomplete fuzzy preference relations 用于社会网络大规模群体决策的成本最小化两阶段三向动态共识机制：利用 K 近邻处理不完整模糊偏好关系

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2024-11-10 DOI: 10.1016/j.eswa.2024.125705

Jiaxin Zhan, Mingjie Cai

In the era of big data, large scale group decision-making (LSGDM) with social networks (SNs) (namely, SN-LSGDM) has become a hot topic in the field of decision science. Faced with the explosive growth of information, decision-makers (DMs) face immense challenges in processing and integrating vast amounts of data, often finding it difficult to fully comprehend all the information, leading to potentially incomplete expressions of their fuzzy preference relations (FPRs). This limitation in information processing not only affects the quality of decision-making but also increases the difficulty and cost of reaching a consensus. To overcome these challenges and enhance the efficiency and accuracy of decision-making, this paper designs a consensus model that minimizes adjustment costs in light of a dynamic trust network. Firstly, we introduce a measurement method based on

K

-nearest neighbor (KNN) information, which comprehensively considers the trust level of DMs and the similarity of preference relations, effectively filling in missing preference information and improving the completeness and accuracy of decision-making. In addition, an improved

k

-means clustering algorithm is adopted, which takes into account the mutual influences between DMs and the cost of unit adjustment. On this basis, a two-stage minimum adjustment cost consensus reaching mechanism based on three-way decision (TWD) is proposed, using comprehensive adjustment priority as the criterion for division, to achieve feedback adjustment at the individual and subgroup levels, ensuring the coordination and consistency of the decision-making plan. At the same time, an optimization model is introduced to achieve cost minimization. Through detailed case studies and comparative analysis, the feasibility and superiority of this method in practical applications have been demonstrated.

在大数据时代，利用社会网络（SN）进行大规模群体决策（LSGDM）（即 SN-LSGDM）已成为决策科学领域的热门话题。面对爆炸式增长的信息，决策者（DMs）在处理和整合海量数据时面临巨大挑战，往往难以完全理解所有信息，导致其模糊偏好关系（FPRs）的表达可能不完整。这种信息处理的局限性不仅会影响决策质量，还会增加达成共识的难度和成本。为了克服这些挑战，提高决策的效率和准确性，本文设计了一种基于动态信任网络的、调整成本最小化的共识模型。首先，我们引入了一种基于 K 近邻（KNN）信息的测量方法，该方法综合考虑了 DM 的信任程度和偏好关系的相似性，有效填补了缺失的偏好信息，提高了决策的完整性和准确性。此外，还采用了改进的 k-means 聚类算法，考虑了 DM 之间的相互影响和单位调整成本。在此基础上，提出了基于三向决策（TWD）的两阶段最小调整成本共识达成机制，以综合调整优先级为划分标准，实现个体和分组层面的反馈调整，确保决策方案的协调性和一致性。同时，引入优化模型，实现成本最小化。通过详细的案例研究和对比分析，证明了该方法在实际应用中的可行性和优越性。

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引用次数: 0

Exploring cluster-dependent isomorphism in multi-objective evolutionary optimization 探索多目标进化优化中的集群同构性

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2024-11-09 DOI: 10.1016/j.eswa.2024.125684

Wei Zheng , Jianyong Sun

In this paper, a Two-Round learning-based Algorithm for Continuous box-constrained multi-objective Evolutionary optimization (TRACE) under the decomposition framework is proposed, in which the isomorphism relationship between the clustered Pareto Front and Pareto solution set is explored and a new time-varying adaptive crossover operator is developed. The learning process involves two stages. In the first stage, the

K

-means is applied to cluster the population of objective vectors. By exploring the property of cluster-dependent isomorphism between the objective space and the decision space, a parent individual for each individual is selected from the corresponding clusters in the decision space. The time-varying adaptive crossover operator is then used together with the classical polynomial mutation operator to generate a new solution based on the selected parent individuals. As part of the environmental selection process, the

K

-means is applied again to the combination of parent and offspring individuals in the objective space to assist in the selection of suitable solutions for each decomposed subspace. TRACE is compared with 11 state-of-the-art multi-objective evolutionary algorithms on totally 43 difficult problems with different characteristics. Furthermore, TRACE is compared with three promising multi-objective evolutionary algorithms for community detection in attribute networks. Extensive experiments show that TRACE significantly outperforms the compared algorithms in most instances.

本文提出了一种分解框架下基于两轮学习的连续盒约束多目标进化优化算法（TRACE），其中探索了聚类帕雷托前沿与帕雷托解集之间的同构关系，并开发了一种新的时变自适应交叉算子。学习过程包括两个阶段。在第一阶段，应用 K-means 对目标向量群进行聚类。通过探索目标空间和决策空间之间依赖于聚类的同构性，从决策空间的相应聚类中为每个个体选择一个父个体。然后，时变自适应交叉算子与经典的多项式突变算子一起使用，根据选定的父个体生成新的解决方案。作为环境选择过程的一部分，K-means 方法再次应用于目标空间中的父个体和子个体的组合，以帮助为每个分解子空间选择合适的解决方案。TRACE 与 11 种最先进的多目标进化算法在 43 个具有不同特征的难题上进行了比较。此外，TRACE 还与三种有前途的多目标进化算法进行了比较，这些算法用于属性网络中的群落检测。大量实验表明，TRACE 在大多数情况下都明显优于所比较的算法。

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