Pub Date : 2025-02-24DOI: 10.1109/TETCI.2025.3537938
Junpeng Li;Jiahe Qin;Changchun Hua;Yana Yang
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 M-tuple similarity-confidence (Msconf) learning, a novel framework that extends Sconf learning to $M$-tuples of varying sizes. The proposed method includes a detailed process for generating $M$-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 Msconf learning framework.
{"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</i>, a novel framework that extends <italic>Sconf learning</i> 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</i> 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}
Pub Date : 2025-02-21DOI: 10.1109/TETCI.2025.3537943
Guoxing Wen;Yanfen Song;Zijun Li;Bin Li
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.
{"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}
Pub Date : 2025-02-21DOI: 10.1109/TETCI.2025.3540419
Haonan Zheng;Xinyang Deng;Wen Jiang
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.
{"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}
Pub Date : 2025-02-21DOI: 10.1109/TETCI.2025.3540408
Guobin Shen;Dongcheng Zhao;Yi Zeng
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.
尖峰神经网络（SNNs）利用离散尖峰序列来传递信息，这在很大程度上模仿了大脑的信息传递。尽管这种二值化的表示形式极大地提高了snn的能量效率和鲁棒性，但snn的性能与基于实值的人工神经网络之间存在很大差距。大脑中有许多不同的脉冲模式，这些脉冲模式的动态协同极大地丰富了表征能力。受生物神经元的尖峰模式的启发，引入动态Burst模式，从网络信息容量的角度设计了能够在短时性能和动态时间性能之间进行权衡的Leaky Integrate and Fire or Burst （IF&B）神经元。IF&B神经元表现为静息、规则峰和突发峰三种模式。神经元的爆发密度可以自适应调整，极大地增强了表征能力。我们还提出了一种解耦方法，可以将IF&B神经元无损解耦为等效的LIF神经元，这表明IF&B神经元可以有效地在神经形态硬件上实现。我们在静态数据集CIFAR10、CIFAR100和ImageNet上进行了实验，结果表明我们极大地提高了snn的性能，同时显著降低了网络延迟。我们还在神经形态数据集DVS-CIFAR10和NCALTECH101上进行了实验，结果表明我们用一个小的网络结构达到了最先进的水平。
{"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}
Pub Date : 2025-02-19DOI: 10.1109/TETCI.2025.3537942
Jiayu Ye;Dan Pan;An Zeng;Yiqun Zhang;Qiuping Chen;Yang Liu
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.
{"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}
Pub Date : 2025-02-19DOI: 10.1109/TETCI.2025.3526333
Jun Ning;Yu Wang;C. L. Philip Chen;Tieshan Li
This paper concerned with the network observer based adaptive trajectory tracking control strategy of Unmanned Surface Vehicle with event-triggered mechanisms and signal quantization. In expound upon input quantization, this paper introduces a linear analytical model enabling controller design without necessitating prior knowledge of the input quantization parameters. Meanwhile, the quantized state variables are estimated through the neural network-based observer. As a result, the quantized feedback controller is designed to use the observer's estimation results, through a combination of backstepping, dynamic surface techniques, and event-triggered mechanisms. The stability of the formulated closed-loop system is demonstrated through the application of Lyapunov stability theory principles. Ultimately, the effectiveness of the proposed control strategy is substantiated through simulation experiments.
{"title":"Neural Network Observer Based Adaptive Trajectory Tracking Control Strategy of Unmanned Surface Vehicle With Event-Triggered Mechanisms and Signal Quantization","authors":"Jun Ning;Yu Wang;C. L. Philip Chen;Tieshan Li","doi":"10.1109/TETCI.2025.3526333","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3526333","url":null,"abstract":"This paper concerned with the network observer based adaptive trajectory tracking control strategy of Unmanned Surface Vehicle with event-triggered mechanisms and signal quantization. In expound upon input quantization, this paper introduces a linear analytical model enabling controller design without necessitating prior knowledge of the input quantization parameters. Meanwhile, the quantized state variables are estimated through the neural network-based observer. As a result, the quantized feedback controller is designed to use the observer's estimation results, through a combination of backstepping, dynamic surface techniques, and event-triggered mechanisms. The stability of the formulated closed-loop system is demonstrated through the application of Lyapunov stability theory principles. Ultimately, the effectiveness of the proposed control strategy is substantiated through simulation experiments.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3136-3146"},"PeriodicalIF":5.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687732","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}
Pub Date : 2025-02-19DOI: 10.1109/TETCI.2025.3529876
Yanghe Zou;Peilan Xu;Hao Dai;Heng Song;Wenjian Luo
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 $varepsilon$-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.
{"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 (I<sup>2</sup>HCSO) 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 I<sup>2</sup>HCSO in a set of cases consisting of 25 BACAPs. Compared with the several typical optimizers with experimental results, I<sup>2</sup>HCSO 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}
Dynamic constrained multiobjective optimization problems include irregular and discontinuous feasible regions, segmented true Pareto front, and dynamic environments. To address these problems, we design a dynamic constrained multiobjective optimization algorithm based on dual-population evolution. This algorithm includes two populations, P1 and P2, based on the feasibility of solutions. It utilizes valuable information from infeasible solutions to drive the populations toward the feasible regions and the true Pareto front. At the same time, we propose a mating selection operator to facilitate information exchange between populations and generate promising offspring solutions. To respond to environmental changes, we design a strategy that combines new solutions obtained by the sampling-selection-resampling method and updated old ones, rapidly generating a promising population in a new environment. Additionally, we also design a test suit that can effectively present the discontinuous feasible regions and the irregular changes of true Pareto front in practical appcation problems. The results from experiments demonstrate the efficacy of the test suit, and the proposed algorithm exhibits competitiveness compared to other algorithms.
{"title":"Dual-Population Evolution Based Dynamic Constrained Multiobjective Optimization With Discontinuous and Irregular Feasible Regions","authors":"Xiaoxu Jiang;Qingda Chen;Jinliang Ding;Xingyi Zhang","doi":"10.1109/TETCI.2025.3529882","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529882","url":null,"abstract":"Dynamic constrained multiobjective optimization problems include irregular and discontinuous feasible regions, segmented true Pareto front, and dynamic environments. To address these problems, we design a dynamic constrained multiobjective optimization algorithm based on dual-population evolution. This algorithm includes two populations, P<sub>1</sub> and P<sub>2</sub>, based on the feasibility of solutions. It utilizes valuable information from infeasible solutions to drive the populations toward the feasible regions and the true Pareto front. At the same time, we propose a mating selection operator to facilitate information exchange between populations and generate promising offspring solutions. To respond to environmental changes, we design a strategy that combines new solutions obtained by the sampling-selection-resampling method and updated old ones, rapidly generating a promising population in a new environment. Additionally, we also design a test suit that can effectively present the discontinuous feasible regions and the irregular changes of true Pareto front in practical appcation problems. The results from experiments demonstrate the efficacy of the test suit, and the proposed algorithm exhibits competitiveness compared to other algorithms.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1352-1366"},"PeriodicalIF":5.3,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716520","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}
Pub Date : 2025-02-17DOI: 10.1109/TETCI.2025.3537916
Huangke Chen;Guohua Wu;Rui Wang;Witold Pedrycz
Solving multi-objective optimization problems with scalable decision variables and objectives is an ongoing challenging task. This study proposes a new evolutionary framework that a series of continuously generated subpopulations are used to approximate the entire Pareto-optimal front. These dynamic subpopulations are abstracted as a population stream. In this framework, one subpopulation is only responsible for searching for a Pareto-optimal solution. Diversity is emphasized among converged solutions coming from different subpopulations, striving to alleviate the conflict between diversity and convergence. To improve the convergence of the newly generated subpopulations, the polynomial fitting method is performed on the obtained solutions to model the relationships among decision variables, which are then used to assist in the generation of new subpopulations. Moreover, an adaptive granularity grid-based environmental selection strategy is proposed to maintain a set of well-diversifying converged solutions. Lastly, extensive experiments are conducted to demonstrate the proposal's superiority by comparing it with 19 representative algorithms in 45 test instances with 3-15 objectives and 300-1500 decision variables.
{"title":"Population Stream-Driven Scalable Evolutionary Many-Objective Optimization","authors":"Huangke Chen;Guohua Wu;Rui Wang;Witold Pedrycz","doi":"10.1109/TETCI.2025.3537916","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3537916","url":null,"abstract":"Solving multi-objective optimization problems with scalable decision variables and objectives is an ongoing challenging task. This study proposes a new evolutionary framework that a series of continuously generated subpopulations are used to approximate the entire Pareto-optimal front. These dynamic subpopulations are abstracted as a population stream. In this framework, one subpopulation is only responsible for searching for a Pareto-optimal solution. Diversity is emphasized among converged solutions coming from different subpopulations, striving to alleviate the conflict between diversity and convergence. To improve the convergence of the newly generated subpopulations, the polynomial fitting method is performed on the obtained solutions to model the relationships among decision variables, which are then used to assist in the generation of new subpopulations. Moreover, an adaptive granularity grid-based environmental selection strategy is proposed to maintain a set of well-diversifying converged solutions. Lastly, extensive experiments are conducted to demonstrate the proposal's superiority by comparing it with 19 representative algorithms in 45 test instances with 3-15 objectives and 300-1500 decision variables.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1406-1417"},"PeriodicalIF":5.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706666","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}
Pub Date : 2025-02-17DOI: 10.1109/TETCI.2025.3537940
Qiaomei Han;Xianbin Wang;Weiming Shen;Yanjun Shi
Recent advancements in Federated Learning (FL) have enabled the widespread deployment of distributed computing resources across connected devices, enhancing data processing capabilities and facilitating collaborative decision-making while maintaining user privacy. However, in Internet of Things (IoT) systems, the heterogeneity of devices and unstable network connections present significant challenges to the effective and efficient execution of FL tasks in real-world environments. To address these challenges, we propose an Adaptive Federated Meta Learning Framework with Multi-Objectives and Context-Awareness (AdaFML). This framework aims to achieve multiple objectives, including improving the performance of the FL global model, optimizing time efficiency, and enabling local model adaptation in dynamic and heterogeneous environments. Specifically, AdaFML extracts contextual information from each device, including its data distribution, computation, and communication conditions, to train a multimodal model that optimizes the FL task and time cost estimation, enhancing global model performance and time efficiency. Moreover, AdaFML fine-tunes two critical meta-learning parameters: the mixture ratio between local and global models and the selection weights for model aggregation. This enables adaptive local model updates across different devices while improving global model performance. Experimental results demonstrate that AdaFML boosts the effectiveness, efficiency, and adaptability of FL task execution in dynamic and heterogeneous environments.
{"title":"AdaFML: Adaptive Federated Meta Learning With Multi-Objectives and Context-Awareness in Dynamic Heterogeneous Networks","authors":"Qiaomei Han;Xianbin Wang;Weiming Shen;Yanjun Shi","doi":"10.1109/TETCI.2025.3537940","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3537940","url":null,"abstract":"Recent advancements in Federated Learning (FL) have enabled the widespread deployment of distributed computing resources across connected devices, enhancing data processing capabilities and facilitating collaborative decision-making while maintaining user privacy. However, in Internet of Things (IoT) systems, the heterogeneity of devices and unstable network connections present significant challenges to the effective and efficient execution of FL tasks in real-world environments. To address these challenges, we propose an Adaptive Federated Meta Learning Framework with Multi-Objectives and Context-Awareness (AdaFML). This framework aims to achieve multiple objectives, including improving the performance of the FL global model, optimizing time efficiency, and enabling local model adaptation in dynamic and heterogeneous environments. Specifically, AdaFML extracts contextual information from each device, including its data distribution, computation, and communication conditions, to train a multimodal model that optimizes the FL task and time cost estimation, enhancing global model performance and time efficiency. Moreover, AdaFML fine-tunes two critical meta-learning parameters: the mixture ratio between local and global models and the selection weights for model aggregation. This enables adaptive local model updates across different devices while improving global model performance. Experimental results demonstrate that AdaFML boosts the effectiveness, efficiency, and adaptability of FL task execution in dynamic and heterogeneous environments.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1428-1440"},"PeriodicalIF":5.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706633","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}
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