IEEE Transactions on Emerging Topics in Computational Intelligence最新文献

IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-26 DOI: 10.1109/TETCI.2025.3548334

引用次数: 0

IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-26 DOI: 10.1109/TETCI.2025.3548330

引用次数: 0

IEEE Computational Intelligence Society Information

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-26 DOI: 10.1109/TETCI.2025.3548332

引用次数: 0

Memetic Differential Evolution With Adaptive Niching Selection and Diversity-Driven Strategies for Multimodal Optimization

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-28 DOI: 10.1109/TETCI.2025.3529903

Yufeng Feng;Weiguo Sheng;Zidong Wang;Gang Xiao;Qi Li;Li Li;Zuling Wang

{"title":"Memetic Differential Evolution With Adaptive Niching Selection and Diversity-Driven Strategies for Multimodal Optimization","authors":"Yufeng Feng;Weiguo Sheng;Zidong Wang;Gang Xiao;Qi Li;Li Li;Zuling Wang","doi":"10.1109/TETCI.2025.3529903","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529903","url":null,"abstract":"Simultaneously identifying a set of optimal solutions within the landscape of multimodal optimization problem presents a significant challenge. In this work, a differential evolution algorithm with adaptive niching selection, diversity-driven exploration and adaptive local search strategies is proposed to tackle the challenge. In the proposed method, an adaptive niching selection strategy is devised to dynamically select appropriate niching methods from a diverse pool to evolve the population. The pool encompasses niching methods with varying search properties and is dynamically updated during evolution. Further, to enhance exploration, a diversity-driven exploration strategy, which leverages redundant individuals from convergence regions to explore the solution space, is introduced. Additionally, an adaptive local search operation, in which the probability of applying local search and corresponding sampling area are dynamically determined based on the potential of solutions as well as the stage of evolution, is developed to fine-tune promising solutions. The effectiveness of proposed method has been demonstrated on 20 test functions from CEC2013 benchmark suite. Experimental results confirm the effectiveness of our method, demonstrating its superiority compared to related algorithms.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1322-1339"},"PeriodicalIF":5.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Binary Classification From $M$-Tuple Similarity-Confidence Data

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-24 DOI: 10.1109/TETCI.2025.3537938

Junpeng Li;Jiahe Qin;Changchun Hua;Yana Yang

{"title":"Binary Classification From $M$-Tuple Similarity-Confidence Data","authors":"Junpeng Li;Jiahe Qin;Changchun Hua;Yana Yang","doi":"10.1109/TETCI.2025.3537938","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3537938","url":null,"abstract":"A recent advancement in weakly-supervised learning utilizes pairwise similarity-confidence (Sconf) data, allowing the training of binary classifiers using unlabeled data pairs with confidence scores indicating similarity. However, extending this approach to handle high-order tuple data (e.g., triplets, quadruplets, quintuplets) with similarity-confidence scores presents significant challenges. To address these issues, this paper introduces <italic>M-tuple similarity-confidence (Msconf) learning, a novel framework that extends <italic>Sconf learning to <inline-formula><tex-math>$M$</tex-math></inline-formula>-tuples of varying sizes. The proposed method includes a detailed process for generating <inline-formula><tex-math>$M$</tex-math></inline-formula>-tuple similarity-confidence data and deriving an unbiased risk estimator to train classifiers effectively. Additionally, risk correction models are implemented to reduce potential overfitting, and a theoretical generalization bound is established. Extensive experiments demonstrate the practical effectiveness and robustness of the proposed <italic>Msconf learning framework.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1418-1427"},"PeriodicalIF":5.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimized Leader-Follower Consensus Control of Multi-QUAV Attitude System Using Reinforcement Learning and Backstepping

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-21 DOI: 10.1109/TETCI.2025.3537943

Guoxing Wen;Yanfen Song;Zijun Li;Bin Li

{"title":"Optimized Leader-Follower Consensus Control of Multi-QUAV Attitude System Using Reinforcement Learning and Backstepping","authors":"Guoxing Wen;Yanfen Song;Zijun Li;Bin Li","doi":"10.1109/TETCI.2025.3537943","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3537943","url":null,"abstract":"This work is to explore the optimized leader-follower attitude consensus scheme for the multi-quadrotor unmanned aerial vehicle (QUAV) system. Since the QUAV attitude dynamic is modeled by a second-order nonlinear differential equation, the optimized backstepping (OB) technique can be competent for this control design. To derive the optimized leader-follower attitude consensus control, the critic-actor reinforcement learning (RL) is performed in the final backstepping step. Different with the attitude control of single QUAV, the case of multi-QUAV is composed of multiple intercommunicated QUAV attitude individuals, so its control design is more complex and thorny. Moreover, the traditional RL optimizing controls deduce the critic or actor updating law from the negative gradient of approximated Hamilton–Jacobi–Bellman (HJB) equation' square, thus it leads to these algorithms very complexity. Hence the traditional optimizing control methods are implemented to multi-QUAV attitude system difficultly. However, since this optimized scheme deduces the RL training laws from a simple positive function of equivalent with HJB equation, it can obviously simplify algorithm for the smooth application in the multi-QUAV attitude system. Finally, theory and simulation certify the feasibility of this optimized consensus control.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1469-1479"},"PeriodicalIF":5.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Tunable Framework for Joint Trade-Off Between Accuracy and Multi-Norm Robustness

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-21 DOI: 10.1109/TETCI.2025.3540419

Haonan Zheng;Xinyang Deng;Wen Jiang

{"title":"A Tunable Framework for Joint Trade-Off Between Accuracy and Multi-Norm Robustness","authors":"Haonan Zheng;Xinyang Deng;Wen Jiang","doi":"10.1109/TETCI.2025.3540419","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3540419","url":null,"abstract":"Adversarial training enhances the robustness of deep networks at the cost of reduced natural accuracy. Moreover, networks fortified struggle to simultaneously defend against both sparse and dense perturbations. Thus, achieving a better trade-off between natural accuracy and robustness against both types of noise remains an open challenge. Many proposed approaches explore solutions based on network architecture optimization. But, in most cases, the additional parameters introduced are static, meaning that once network training is completed, the performance remains unchanged, and retraining is required to explore other potential trade-offs. We propose two dynamic auxiliary modules, CBNI and CCNI, which can fine-tune convolutional layers and BN layers, respectively, during the inference phase, so that the trained network can still adjust its emphasis on natural examples, sparse perturbations or dense perturbations. This means our network can achieve an appropriate balance to adapt to the operational environment in situ, without retraining. Furthermore, fully exploring natural capability and robustness limits is a complex and time-consuming problem. Our method can serve as an efficient research tool to examine the achievable trade-offs with just a single training. It is worth mentioning that CCNI is a linear adjustment and CBNI does not directly participate in the inference process. Therefore, both of them don't introduce redundant parameters and inference latency. Experiments indicate that our network can indeed achieve a complex trade-off between accuracy and adversarial robustness, producing performance that is comparable to or even better than existing methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1490-1501"},"PeriodicalIF":5.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploiting High Performance Spiking Neural Networks With Efficient Spiking Patterns

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-21 DOI: 10.1109/TETCI.2025.3540408

Guobin Shen;Dongcheng Zhao;Yi Zeng

{"title":"Exploiting High Performance Spiking Neural Networks With Efficient Spiking Patterns","authors":"Guobin Shen;Dongcheng Zhao;Yi Zeng","doi":"10.1109/TETCI.2025.3540408","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3540408","url":null,"abstract":"Spiking Neural Networks (SNNs) use discrete spike sequences to transmit information, which significantly mimics the information transmission of the brain. Although this binarized form of representation dramatically enhances the energy efficiency and robustness of SNNs, it also leaves a large gap between the performance of SNNs and Artificial Neural Networks based on real values. There are many different spike patterns in the brain, and the dynamic synergy of these spike patterns greatly enriches the representation capability. Inspired by spike patterns in biological neurons, this paper introduces the dynamic Burst pattern and designs the Leaky Integrate and Fire or Burst (IF&B) neuron that can make a trade-off between short-time performance and dynamic temporal performance from the perspective of network information capacity. IF&B neuron exhibits three modes, resting, Regular spike, and Burst spike. The burst density of the neuron can be adaptively adjusted, which significantly enriches the characterization capability. We also propose a decoupling method that can losslessly decouple IF&B neurons into equivalent LIF neurons, which demonstrates that IF&B neurons can be efficiently implemented on neuromorphic hardware. We conducted experiments on the static datasets CIFAR10, CIFAR100, and ImageNet, which showed that we greatly improved the performance of the SNNs while significantly reducing the network latency. We also conducted experiments on neuromorphic datasets DVS-CIFAR10 and NCALTECH101 and showed that we achieved state-of-the-art with a small network structure.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1480-1489"},"PeriodicalIF":5.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MssNet: An Efficient Spatial Attention Model for Early Recognition of Alzheimer's Disease

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-19 DOI: 10.1109/TETCI.2025.3537942

Jiayu Ye;Dan Pan;An Zeng;Yiqun Zhang;Qiuping Chen;Yang Liu

{"title":"MssNet: An Efficient Spatial Attention Model for Early Recognition of Alzheimer's Disease","authors":"Jiayu Ye;Dan Pan;An Zeng;Yiqun Zhang;Qiuping Chen;Yang Liu","doi":"10.1109/TETCI.2025.3537942","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3537942","url":null,"abstract":"Deep learning models are widely used in medical image-guided disease recognition and have achieved outstanding performance. Voxel-based models are typically the default choice for deep learning-based MRI analysis, which require high computational resources and large data volumes, making them inefficient for rapid disease screening. Simultaneously, the existing Alzheimer's disease (AD) recognition model is primarily comprised of Convolutional Neural Network (CNN) structures. With the increasing of the network depth, the fine-grained details of global features tend to be partially lost. Therefore, we propose a Multi-scale spatial self-attention Network (MssNet) that effectively captures both coarse-grained and fine-grained features. We design to select the target slice based on image entropy to achieve efficient slice-based AD recognition. To capture multi-level spatial information, a novel spatial attention mechanism and spatial self-attention attention are designed. The former is utilized to collect critical spatial information and identify areas that are likely to be lesions, the latter investigates the relationship between features in different image regions through spatial interaction by pure convolutional blocks. MssNet fully utilizes multi-scale information at different granularities for spatial feature interaction, providing it with strong modeling and information understanding capabilities. It has achieved excellent performance in the recognition tasks of Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets. Moreover, MssNet is a lightweight model involving lower scale parameters against the Voxel-based ones, while demonstrating strong generalization capability.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1454-1468"},"PeriodicalIF":5.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Swarm Optimization With Intra- and Inter-Hierarchical Competition for Large-Scale Berth Allocation and Crane Assignment

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-19 DOI: 10.1109/TETCI.2025.3529876

Yanghe Zou;Peilan Xu;Hao Dai;Heng Song;Wenjian Luo

{"title":"Swarm Optimization With Intra- and Inter-Hierarchical Competition for Large-Scale Berth Allocation and Crane Assignment","authors":"Yanghe Zou;Peilan Xu;Hao Dai;Heng Song;Wenjian Luo","doi":"10.1109/TETCI.2025.3529876","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529876","url":null,"abstract":"The trend of global economic integration has fostered the prosperity of the maritime transportation industry, which has placed higher demands on the construction of automated container terminals, and the optimization of the integrated berth allocation and crane assignment problems (BACAPs) is a key link. Currently, population-based computational intelligence methods have attracted attention on BACAPs, but on small-scale cases and simplified problem models. In this paper, we propose a novel swarm optimization with intra- and inter-hierarchical competition (I2HCSO) for addressing large-scale BACAPs, which is a major challenge in container terminals. First, we construct a hierarchical model with better particles at the higher hierarchy, and the populations at different hierarchies are divided into several sub-swarm. Then, we design an intra- and inter-hierarchical competitive mechanism to balance the exploration and exploitation of the population, in which intra-hierarchical competition is carried out within sub-swarm at any hierarchy, whereas inter-hierarchical competition occurs in different sub-swarms of neighboring hierarchies. Third, we consider optimizing the priorities of vessels for efficient use of resources berth and crane for the first time in BACAPs and employ <inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>-constraints to search for feasible regions. Additionally, we develop a local search operator as a repair strategy to improve the quality of the solution. Finally, we test I2HCSO in a set of cases consisting of 25 BACAPs. Compared with the several typical optimizers with experimental results, I2HCSO is more competitive on BACAPs with different scales.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1307-1321"},"PeriodicalIF":5.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0