Pub Date : 2024-03-24DOI: 10.1109/TETCI.2024.3398387
{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors","authors":"","doi":"10.1109/TETCI.2024.3398387","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3398387","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10538465","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-24DOI: 10.1109/TETCI.2024.3398385
{"title":"IEEE Computational Intelligence Society Information","authors":"","doi":"10.1109/TETCI.2024.3398385","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3398385","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10538449","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-24DOI: 10.1109/TETCI.2024.3398383
{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information","authors":"","doi":"10.1109/TETCI.2024.3398383","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3398383","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10538452","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-22DOI: 10.1109/TETCI.2024.3369485
Zhengjun Wang;Weifeng Gao;Genghui Li;Zhenkun Wang;Maoguo Gong
Unmanned aerial vehicles (UAVs) are widely used in urban search and rescue, where path planning plays a critical role. This paper proposes an approach using off-policy reinforcement learning (RL) with an improved exploration mechanism (IEM) based on prioritized experience replay (PER) and curiosity-driven exploration to address the time-constrained path planning problem for UAVs operating in complex unknown environments. Firstly, to meet the task's time constraints, we design a rollout algorithm based on PER to optimize the behavior policy and enhance sampling efficiency. Additionally, we address the issue that certain off-policy RL algorithms often get trapped in local optima in environments with sparse rewards by measuring curiosity using the states' unvisited time and generating intrinsic rewards to encourage exploration. Lastly, we introduce IEM into the sampling stage of various off-policy RL algorithms. Simulation experiments demonstrate that, compared to the original off-policy RL algorithms, the algorithms incorporating IEM can reduce the planning time required for rescuing paths and achieve the goal of rescuing all trapped individuals.
{"title":"Path Planning for Unmanned Aerial Vehicle via Off-Policy Reinforcement Learning With Enhanced Exploration","authors":"Zhengjun Wang;Weifeng Gao;Genghui Li;Zhenkun Wang;Maoguo Gong","doi":"10.1109/TETCI.2024.3369485","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369485","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are widely used in urban search and rescue, where path planning plays a critical role. This paper proposes an approach using off-policy reinforcement learning (RL) with an improved exploration mechanism (IEM) based on prioritized experience replay (PER) and curiosity-driven exploration to address the time-constrained path planning problem for UAVs operating in complex unknown environments. Firstly, to meet the task's time constraints, we design a rollout algorithm based on PER to optimize the behavior policy and enhance sampling efficiency. Additionally, we address the issue that certain off-policy RL algorithms often get trapped in local optima in environments with sparse rewards by measuring curiosity using the states' unvisited time and generating intrinsic rewards to encourage exploration. Lastly, we introduce IEM into the sampling stage of various off-policy RL algorithms. Simulation experiments demonstrate that, compared to the original off-policy RL algorithms, the algorithms incorporating IEM can reduce the planning time required for rescuing paths and achieve the goal of rescuing all trapped individuals.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096326","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 : 2024-03-21DOI: 10.1109/TETCI.2024.3372693
Ying Ma;Xiaoyan Zou;Qizheng Pan;Ming Yan;Guoqi Li
In the task of multi-label classification, it is a key challenge to determine the correlation between labels. One solution to this is the Target Embedding Autoencoder (TEA), but most TEA-based frameworks have numerous parameters, large models, and high complexity, which makes it difficult to deal with the problem of large-scale learning. To address this issue, we provide a Target Embedding Autoencoder framework based on Knowledge Distillation (KD-TEA) that compresses a Teacher model with large parameters into a small Student model through knowledge distillation. Specifically, KD-TEA transfers the dark knowledge learned from the Teacher model to the Student model. The dark knowledge can provide effective regularization to alleviate the over-fitting problem in the training process, thereby enhancing the generalization ability of the Student model, and better completing the multi-label task. In order to make the Student model learn the knowledge of the Teacher model directly, we improve the distillation loss: KD-TEA uses MSE loss instead of KL divergence loss to improve the performance of the model in multi-label tasks. Experiments on multiple datasets show that our KD-TEA framework is superior to the most advanced multi-label classification methods in both performance and efficiency.
{"title":"Target-Embedding Autoencoder With Knowledge Distillation for Multi-Label Classification","authors":"Ying Ma;Xiaoyan Zou;Qizheng Pan;Ming Yan;Guoqi Li","doi":"10.1109/TETCI.2024.3372693","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372693","url":null,"abstract":"In the task of multi-label classification, it is a key challenge to determine the correlation between labels. One solution to this is the Target Embedding Autoencoder (TEA), but most TEA-based frameworks have numerous parameters, large models, and high complexity, which makes it difficult to deal with the problem of large-scale learning. To address this issue, we provide a Target Embedding Autoencoder framework based on Knowledge Distillation (KD-TEA) that compresses a Teacher model with large parameters into a small Student model through knowledge distillation. Specifically, KD-TEA transfers the dark knowledge learned from the Teacher model to the Student model. The dark knowledge can provide effective regularization to alleviate the over-fitting problem in the training process, thereby enhancing the generalization ability of the Student model, and better completing the multi-label task. In order to make the Student model learn the knowledge of the Teacher model directly, we improve the distillation loss: KD-TEA uses MSE loss instead of KL divergence loss to improve the performance of the model in multi-label tasks. Experiments on multiple datasets show that our KD-TEA framework is superior to the most advanced multi-label classification methods in both performance and efficiency.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096306","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 : 2024-03-21DOI: 10.1109/TETCI.2024.3369998
Yu Xue;Kun Chen;Ferrante Neri
Generative adversarial networks (GANs) are machine learning algorithms that can efficiently generate data such as images. Although GANs are very popular, their training usually lacks stability, with the generator and discriminator networks failing to converge during the training process. To address this problem and improve the stability of GANs, in this paper, we automate the design of stable GANs architectures through a novel approach: differentiable architecture search with attention mechanisms for generative adversarial networks ( DAMGAN ). We construct a generator supernet and search for the optimal generator network within it. We propose incorporating two attention mechanisms between each pair of nodes in the supernet. The first attention mechanism, down attention, selects the optimal candidate operation of each edge in the supernet, while the second attention mechanism, up attention, improves the training stability of the supernet and limits the computational cost of the search by selecting the most important feature maps for the following candidate operations. Experimental results show that the architectures searched by our method obtain a state-of-the-art inception score (IS) of 8.99 and a very competitive Fréchet inception distance (FID) of 10.27 on the CIFAR-10 dataset. Competitive results were also obtained on the STL-10 dataset (IS = 10.35, FID = 22.18). Notably, our search time was only 0.09 GPU days.
{"title":"Differentiable Architecture Search With Attention Mechanisms for Generative Adversarial Networks","authors":"Yu Xue;Kun Chen;Ferrante Neri","doi":"10.1109/TETCI.2024.3369998","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369998","url":null,"abstract":"Generative adversarial networks (GANs) are machine learning algorithms that can efficiently generate data such as images. Although GANs are very popular, their training usually lacks stability, with the generator and discriminator networks failing to converge during the training process. To address this problem and improve the stability of GANs, in this paper, we automate the design of stable GANs architectures through a novel approach: differentiable architecture search with attention mechanisms for generative adversarial networks (\u0000<bold>DAMGAN</b>\u0000). We construct a generator supernet and search for the optimal generator network within it. We propose incorporating two attention mechanisms between each pair of nodes in the supernet. The first attention mechanism, down attention, selects the optimal candidate operation of each edge in the supernet, while the second attention mechanism, up attention, improves the training stability of the supernet and limits the computational cost of the search by selecting the most important feature maps for the following candidate operations. Experimental results show that the architectures searched by our method obtain a state-of-the-art inception score (IS) of 8.99 and a very competitive Fréchet inception distance (FID) of 10.27 on the CIFAR-10 dataset. Competitive results were also obtained on the STL-10 dataset (IS = 10.35, FID = 22.18). Notably, our search time was only 0.09 GPU days.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964788","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 : 2024-03-21DOI: 10.1109/TETCI.2024.3372406
Jianhang Zhou;Shuyi Li;Shaoning Zeng;Bob Zhang
The structural information is critical in image analysis as one of the most popular topics in the computational intelligence area. To capture the structural information of the given images, the nuclear-norm matrix regression (NMR) framework provides a natural way by successfully formulating the two-dimensional image error matrix into the image analysis. Nevertheless, although NMR shows its powerful performance in robust face recognition, its intrinsic regression/classification mechanism is still unclear, which restricts its capability. Furthermore, since NMR works in a sample-dependent scheme, it requires remodelling for each given image sample and leads to a failure in learning the intrinsic and structural information from the given image samples. Leveraging the superiority and drawbacks of the NMR framework, in this paper, we propose P robabilistic N uclear-norm M atrix R egression (PNMR). We form the idea of PNMR with theoretical deduction using Bayesian inference to clearly show its probability interpretation, where we present a unified as well as a delicated formulation for optimization. PNMR can be proven to achieve the joint learning of the NMR-style formulation regularized by the $L_{2,1}$ -norm, making it adaptive to arbitrary given image samples. To fully consider the intrinsic relationships of the observed samples, we propose the P robabilistic N uclear-norm M atrix R egression regularized by R andom G raph (PNMR-RG) on the basis of PNMR. Extensive experiments on several image datasets were performed and comparisons were made with 10 state-of-the-art methods to demonstrate the feasibility and promising performance of both PNMR and PNMR-RG.
{"title":"Probabilistic Nuclear-Norm Matrix Regression Regularized by Random Graph Theory","authors":"Jianhang Zhou;Shuyi Li;Shaoning Zeng;Bob Zhang","doi":"10.1109/TETCI.2024.3372406","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372406","url":null,"abstract":"The structural information is critical in image analysis as one of the most popular topics in the computational intelligence area. To capture the structural information of the given images, the nuclear-norm matrix regression (NMR) framework provides a natural way by successfully formulating the two-dimensional image error matrix into the image analysis. Nevertheless, although NMR shows its powerful performance in robust face recognition, its intrinsic regression/classification mechanism is still unclear, which restricts its capability. Furthermore, since NMR works in a sample-dependent scheme, it requires remodelling for each given image sample and leads to a failure in learning the intrinsic and structural information from the given image samples. Leveraging the superiority and drawbacks of the NMR framework, in this paper, we propose \u0000<bold>P</b>\u0000robabilistic \u0000<bold>N</b>\u0000uclear-norm \u0000<bold>M</b>\u0000atrix \u0000<bold>R</b>\u0000egression (PNMR). We form the idea of PNMR with theoretical deduction using Bayesian inference to clearly show its probability interpretation, where we present a unified as well as a delicated formulation for optimization. PNMR can be proven to achieve the joint learning of the NMR-style formulation regularized by the \u0000<inline-formula><tex-math>$L_{2,1}$</tex-math></inline-formula>\u0000-norm, making it adaptive to arbitrary given image samples. To fully consider the intrinsic relationships of the observed samples, we propose the \u0000<bold>P</b>\u0000robabilistic \u0000<bold>N</b>\u0000uclear-norm \u0000<bold>M</b>\u0000atrix \u0000<bold>R</b>\u0000egression regularized by \u0000<bold>R</b>\u0000andom \u0000<bold>G</b>\u0000raph (PNMR-RG) on the basis of PNMR. Extensive experiments on several image datasets were performed and comparisons were made with 10 state-of-the-art methods to demonstrate the feasibility and promising performance of both PNMR and PNMR-RG.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965839","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 : 2024-03-19DOI: 10.1109/TETCI.2024.3372410
Zeyi Liu;Tao Wen;Yong Deng;Hamido Fujita
The evaluation of expert importance degree for solving group decision-making problems (GDM) is meaningful, especially for social network GDM cases. Conventionally, the importance of experts in existing GDM models is assumed to be isolated. Nevertheless, in real-life scenarios, the internal components of expert systems should be mutually influential. In this study, a novel game-theoretic expert importance evaluation model guided by cooperation effects is proposed. First, the framework of non-additive fuzzy measure values is utilized to obtain the initial opinions of all experts. An interaction indicator is then exploited to represent peer interaction effort (PIE). With the log-sigmoid transition technique, individual social cooperation networks (ISCNs) are then constructed. With the advanced aggregation operator, the global social cooperation network (GSCN) of the corresponding expert collection can be generated. Eventually, a modified gravity model is designed to evaluate the degree of importance for the experts. Several experiments are conducted to demonstrate the effectiveness of the proposed method. The results show that the influence of cooperation effects can reasonably be considered in the expert importance evaluation procedure, which is beneficial to real-life scenarios. Additional comparisons and related discussions are also provided.
{"title":"Game-Theoretic Expert Importance Evaluation Model Guided by Cooperation Effects for Social Network Group Decision Making","authors":"Zeyi Liu;Tao Wen;Yong Deng;Hamido Fujita","doi":"10.1109/TETCI.2024.3372410","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372410","url":null,"abstract":"The evaluation of expert importance degree for solving group decision-making problems (GDM) is meaningful, especially for social network GDM cases. Conventionally, the importance of experts in existing GDM models is assumed to be isolated. Nevertheless, in real-life scenarios, the internal components of expert systems should be mutually influential. In this study, a novel game-theoretic expert importance evaluation model guided by cooperation effects is proposed. First, the framework of non-additive fuzzy measure values is utilized to obtain the initial opinions of all experts. An interaction indicator is then exploited to represent peer interaction effort (PIE). With the log-sigmoid transition technique, individual social cooperation networks (ISCNs) are then constructed. With the advanced aggregation operator, the global social cooperation network (GSCN) of the corresponding expert collection can be generated. Eventually, a modified gravity model is designed to evaluate the degree of importance for the experts. Several experiments are conducted to demonstrate the effectiveness of the proposed method. The results show that the influence of cooperation effects can reasonably be considered in the expert importance evaluation procedure, which is beneficial to real-life scenarios. Additional comparisons and related discussions are also provided.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965828","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}
A vast amount of textual and structural information is required for knowledge graph construction and its downstream tasks. However, most of the current knowledge graphs are incomplete due to the difficulty of knowledge acquisition and integration. Knowledge Graph Completion (KGC) is used to predict missing connections. In previous studies, textual information and graph structural information are utilized independently, without an effective method for fusing these two types of information. In this paper, we propose a graph structure enhanced pre-training language model for knowledge graph completion. Firstly, we design a graph sampling algorithm and a Graph2Seq module for constructing sub-graphs and their corresponding contexts to support large-scale knowledge graph learning and parallel training. It is also the basis for fusing textual data and graph structure. Next, two pre-training tasks based on masked modeling are designed for capturing accurate entity-level and relation-level information. Furthermore, this paper proposes a novel asymmetric Encoder-Decoder architecture to restore masked components, where the encoder is a Pre-trained Language Model (PLM) and the decoder is a multi-relational Graph Neural Network (GNN). The purpose of the architecture is to integrate textual information effectively with graph structural information. Finally, the model is fine-tuned for KGC tasks on two widely used public datasets. The experiments show that the model achieves excellent performance and outperforms baselines in most metrics, which demonstrate the effectiveness of our approach by fusing the structure and semantic information to knowledge graph.
{"title":"Graph Structure Enhanced Pre-Training Language Model for Knowledge Graph Completion","authors":"Huashi Zhu;Dexuan Xu;Yu Huang;Zhi Jin;Weiping Ding;Jiahui Tong;Guoshuang Chong","doi":"10.1109/TETCI.2024.3372442","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372442","url":null,"abstract":"A vast amount of textual and structural information is required for knowledge graph construction and its downstream tasks. However, most of the current knowledge graphs are incomplete due to the difficulty of knowledge acquisition and integration. Knowledge Graph Completion (KGC) is used to predict missing connections. In previous studies, textual information and graph structural information are utilized independently, without an effective method for fusing these two types of information. In this paper, we propose a graph structure enhanced pre-training language model for knowledge graph completion. Firstly, we design a graph sampling algorithm and a Graph2Seq module for constructing sub-graphs and their corresponding contexts to support large-scale knowledge graph learning and parallel training. It is also the basis for fusing textual data and graph structure. Next, two pre-training tasks based on masked modeling are designed for capturing accurate entity-level and relation-level information. Furthermore, this paper proposes a novel asymmetric Encoder-Decoder architecture to restore masked components, where the encoder is a Pre-trained Language Model (PLM) and the decoder is a multi-relational Graph Neural Network (GNN). The purpose of the architecture is to integrate textual information effectively with graph structural information. Finally, the model is fine-tuned for KGC tasks on two widely used public datasets. The experiments show that the model achieves excellent performance and outperforms baselines in most metrics, which demonstrate the effectiveness of our approach by fusing the structure and semantic information to knowledge graph.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965824","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}
The last four decades have witnessed the birth and growth of neurodynamic optimization with numerous recurrent neural networks developed for solving various constrained optimization problems. Numerous results on neurodynamic optimization are reported in the literature,. In view of the diverse nature of the publications, this survey provides an updated overview of neurodynamic optimization to summarize the state-of-the-art results in terms of model structure, convergence property, and solvability scopes. It starts with an introduction and preliminaries, followed by categorizing many representative neural network models for constrained optimization, such as linear and quadratic programming, smooth and nonsmooth nonlinear programming, minimax optimization, distributed optimization, generalized-convex optimization, and global and mixed-integer optimization. In addition, it also delineates some perspective research topics for further investigations.
{"title":"A Survey of Neurodynamic Optimization","authors":"Youshen Xia;Qingshan Liu;Jun Wang;Andrzej Cichocki","doi":"10.1109/TETCI.2024.3369667","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369667","url":null,"abstract":"The last four decades have witnessed the birth and growth of neurodynamic optimization with numerous recurrent neural networks developed for solving various constrained optimization problems. Numerous results on neurodynamic optimization are reported in the literature,. In view of the diverse nature of the publications, this survey provides an updated overview of neurodynamic optimization to summarize the state-of-the-art results in terms of model structure, convergence property, and solvability scopes. It starts with an introduction and preliminaries, followed by categorizing many representative neural network models for constrained optimization, such as linear and quadratic programming, smooth and nonsmooth nonlinear programming, minimax optimization, distributed optimization, generalized-convex optimization, and global and mixed-integer optimization. In addition, it also delineates some perspective research topics for further investigations.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965823","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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