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Distributed Fixed-Time algorithms for Time-Varying Constrained optimization Problems

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IEEE Transactions on Artificial Intelligence

IEEE Transactions on Artificial Intelligence 2025年

作者： He, Xing Li, Yue Zhang, Meng Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, the distributed form of the zeroing neural network for solving time-varying optimal problems is put forward. Compared with traditional centralized algorithms, distributed algorithms possess better privacy and scalability. This paper initially proposes a centralized time-varying optimisation algorithm with fixed-time convergence and certain robustness, which is based on the integration-enhanced zeroing neural network. Subsequently, the algorithm is enhanced, and two distributed algorithms are designed separately. Both of these two algorithms have a fixed convergence time and certain robustness. Additionally, this paper utilizes the penalty function approach to handle time-varying optimization problems with inequality constraints, thereby making the algorithm more widely applicable. The effectiveness of the algorithm is verified through several numerical examples, and the applicability of the algorithm is demonstrated by solving the package-level state-of-charge balancing problem. © 2020 IEEE.

关键词： optimization algorithms

Many-Objective Evolutionary algorithms for optimization of Vehicle-Road Cooperation Systems Based on Intelligent Wireless Sensor Networks

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IEEE Internet of Things Journal 2025年

作者： Liu, Tingting Zhang, Lei Ding, Xuefeng China Three Gorges University School of Economics and Management Yichang443005 China China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443005 China China Three Gorges University College of Computer and Information Technology Yichang443005 China

The vehicle road cooperation system (VRCS) is set to be a critical component of intelligent transportation systems. The rapid and precise acquisition of substantial multi-source traffic data is pivotal, with intelligent wireless sensor networks (IWSNs) emerging as a promising tool for developing VRCS. Nonetheless, the complexity of communication environment and the massive transmission of information have made VRCS optimization an increasingly difficult task, involving the intricate processes of coverage and routing optimization within IWSNs. This challenge essentially transforms into a very complicated many-objective optimization problem, where multiple conflicting objectives and resource constraints need to be tackled simultaneously. Traditional optimization methods struggle to offer satisfactory solutions. Our study introduces a many-objective evolutionary algorithm that concurrently manages coverage and routing optimization in IWSNs. Initially, we present a three-dimensional sensing model to delineate the perception range of an individual sensor. Subsequently, we propose a comprehensive four-objective optimization model, encompassing coverage, connectivity, energy consumption, and deployment cost, to accurately depict the performance of IWSNs. Especially this integrated model includes a swift connectivity evaluation method and a specialized routing forwarding strategy. Furthermore, we suggest a node sleeping strategy to minimize energy consumption further. To find a solution to the resultant many-objective optimization problem, which refers to numerous objectives and constraints, we developed a swarm optimization algorithm based on rapid fitness evaluation strategy and differential co-evolution, aimed at boosting convergence speed and accuracy of the proposed approach. Comparative experiments indicate that our model and algorithm outperform other algorithms in terms of efficiency and effectiveness. © 2025 IEEE.

关键词： optimization algorithms

Feature Explosion: a generic optimization strategy for outlier detection algorithms

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arXiv 2025年

作者： Li, Qi School of Information Science and Technology Beijing Forestry University Beijing100083 China

Outlier detection tasks aim at discovering potential issues or opportunities and are widely used in cybersecurity, financial security, industrial inspection, etc. To date, thousands of outlier detection algorithms have been proposed. Clearly, in real-world scenarios, such a large number of algorithms is unnecessary. In other words, a large number of outlier detection algorithms are redundant. We believe the root cause of this redundancy lies in the current highly customized (i.e., non-generic) optimization strategies. Specifically, when researchers seek to improve the performance of existing outlier detection algorithms, they have to design separate optimized versions tailored to the principles of each algorithm, leading to an ever-growing number of outlier detection algorithms. To address this issue, in this paper, we introduce the ‘explosion’ from physics into the outlier detection task and propose a ‘generic’ optimization strategy based on ‘feature explosion’, called OSD (optimization Strategy for outlier Detection algorithms). In the future, when improving the performance of existing outlier detection algorithms, it will be sufficient to invoke the OSD plugin without the need to design customized optimized versions for them. We compared the performances of 14 outlier detection algorithms on 24 datasets before and after invoking the OSD plugin. The experimental results show that the performances of all outlier detection algorithms are improved on almost all datasets. In terms of average accuracy, OSD make these outlier detection algorithms improve by 15% (AUC), 63.7% (AP). Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

Runtime Analysis of Evolutionary algorithms for Multiparty Multiobjective optimization

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arXiv 2025年

作者： Sun, Yuetong Xu, Peilan Luo, Wenjian School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Shenzhen518055 China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Guangdong Shenzhen518000 China

In scenarios where multiple decision-makers operate within a common decision space, each focusing on their own multi-objective optimization problem (e.g., bargaining games), the problem can be modeled as a multiparty multi-objective optimization problem (MPMOP). While numerous evolutionary algorithms have been proposed to solve MPMOPs, most results remain empirical. This paper presents the first theoretical analysis of the expected runtime of evolutionary algorithms on bi-party multi-objective optimization problems (BP-MOPs). Our findings demonstrate that employing traditional multi-objective optimization algorithms to solve MPMOPs is both time-consuming and inefficient, as the resulting population contains many solutions that fail to achieve consensus among decision-makers. An alternative approach involves decision-makers individually solving their respective optimization problems and seeking consensus only in the final stage. While feasible for pseudo-Boolean optimization problems, this method may fail to guarantee approximate performance for one party in NP-hard problems. Finally, We propose coevolutionary multi-party multiobjective optimizers (CoEMPMO) for pseudo-Boolean optimization and shortest path problems within a multi-party multi-objective context, which maintains a common solution set among all parties through coevolution. Theoretical and experimental results demonstrate that the proposed CoEMPMOrandom outperforms previous algorithms in terms of the expected lower bound on runtime for pseudo-Boolean optimization problems. Additionally, CoEMPMOSPcons achieves better efficiency and precision in solving shortest path problems compared to existing algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

Efficient Gradient Tracking algorithms for Distributed optimization Problems with Inexact Communication

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arXiv 2025年

作者： Zhao, Shengchao Liu, Yongchao School of Mathematics China University of Mining and Technology Xuzhou China School of Mathematical Sciences Dalian University of Technology Dalian China

Distributed optimization problems usually face inexact communication issues induced by communication quantization, differential privacy protection, or channels noise. Most existing algorithms need two-timescale setting of the stepsize of gradient descent and the parameter of noise suppression to ensure the convergence to the optimal solution. In this paper, we propose two single-timescale algorithms, VRA-DGT and VRA–DSGT, for distributed deterministic and stochastic optimization problems with inexact communication respectively. VRA-DGT integrates the Variance-Reduced Aggregation (VRA) mechanism with the distributed gradient tracking framework, which achieves a convergence rate of O (k−1) in the mean-square sense when the objective function is strongly convex and smooth. For distributed stochastic optimization problem, VRA–DSGT, where a hybrid variance reduction technique has been introduced in VRA-DGT, maintains the convergence rate of O (k−1) for strongly convex and smooth objective function. Simulated experiments on logistic regression problem with real-world data verify the effectiveness of the proposed algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

Evaluation and Efficiency Comparison of Evolutionary algorithms for Service Placement optimization in Fog Architectures

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arXiv 2025年

作者： Guerrero, Carlos Lera, Isaac Juiz, Carlos Computer Science Department University of Balearic Islands Crta. Valldemossa km 7.5 PalmaE07121 Spain

This study compares three evolutionary algorithms for the problem of fog service placement: weighted sum genetic algorithm (WSGA), non-dominated sorting genetic algorithm II (NSGA-II), and multiobjective evolutionary algorithm based on decomposition (MOEA/D). A model for the problem domain (fog architecture and fog applications) and for the optimization (objective functions and solutions) is presented. Our main concerns are related to optimize the network latency, the service spread and the use of the resources. The algorithms are evaluated with a random Barabasi-Albert network topology with 100 devices and with two experiment sizes of 100 and 200 application services. The results showed that NSGA-II obtained the highest optimizations of the objectives and the highest diversity of the solution space. On the contrary, MOEA/D was better to reduce the execution times. The WSGA algorithm did not show any benefit with regard to the other two algorithms. © 2025, CC BY-NC-SA.

关键词： optimization algorithms

Bayesian optimization for Simultaneous Selection of Machine Learning algorithms and Hyperparameters on Shared Latent Space

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arXiv 2025年

作者： Ishikawa, Kazuki Ozaki, Ryota Kanzaki, Yohei Takeuchi, Ichiro Karasuyama, Masayuki Nagoya Institute of Technology Japan Nagoya University Japan RIKEN Japan

Selecting the optimal combination of a machine learning (ML) algorithm and its hyper-parameters is crucial for the development of high-performance ML systems. However, since the combination of ML algorithms and hyper-parameters is enormous, the exhaustive validation requires a significant amount of time. Many existing studies use Bayesian optimization (BO) for accelerating the search. On the other hand, a significant difficulty is that, in general, there exists a different hyper-parameter space for each one of candidate ML algorithms. BO-based approaches typically build a surrogate model independently for each hyperparameter space, by which sufficient observations are required for all candidate ML algorithms. In this study, our proposed method embeds different hyper-parameter spaces into a shared latent space, in which a surrogate multi-task model for BO is estimated. This approach can share information of observations from different ML algorithms by which efficient optimization is expected with a smaller number of total observations. We further propose the pre-training of the latent space embedding with an adversarial regularization, and a ranking model for selecting an effective pre-trained embedding for a given target dataset. Our empirical study demonstrates effectiveness of the proposed method through datasets from OpenML. © 2025, CC BY.

关键词： optimization algorithms

One-Point Residual Feedback algorithms for Distributed Online Convex and Non-convex optimization

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arXiv 2025年

作者： Wang, Yaowen Mo, Lipo Zuo, Min Zheng, Yuanshi School of Mathematics and Statistics Beijing Technology and Business University Beijing100048 China Institute of Systems Science Beijing Wuzi University Beijing101126 China School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing100048 China Center for Complex Systems School of Mechano-electronic Engineering Xidian University Xi’an710071 China

This paper mainly addresses the distributed online optimization problem where the local objective functions are assumed to be convex or non-convex. First, the distributed algorithms are proposed for the convex and non-convex situations, where the one-point residual feedback technology is introduced to estimate gradient of local objective functions. Then the regret bounds of the proposed algorithms are derived respectively under the assumption that the local objective functions are Lipschitz or smooth, which implies that the regrets are sublinear. Finally, we give two numerical examples of distributed convex optimization and distributed resources allocation problem to illustrate the effectiveness of the proposed algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

Power of (L0,L1)-Smoothness in Stochastic Convex optimization: First- and Zero-Order algorithms

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arXiv 2025年

作者： Lobanov, Aleksandr Gasnikov, Alexander MIPT Skoltech HSE Russia Innopolis MIPT ISP RAS Russia

This paper is devoted to the study of stochastic optimization problems under the generalized smoothness assumption. By considering the unbiased gradient oracle in Stochastic Gradient Descent, we provide strategies to achieve the desired accuracy with linear rate. Moreover, in the case of the strong growth condition for smoothness (L0 = 0), we obtain in the convex setup the iteration complexity: N = O (L1Rlog 1Ε + L1cRΕ2) for Clipped Stochastic Gradient Descent and N = O(L1Rlog 1Ε) for Normalized Stochastic Gradient Descent. Furthermore, we generalize the convergence results to the case with a biased gradient oracle, and show that the power of (L0,L1)-smoothness extends to zero-order algorithms. Finally, we validate our theoretical results with a numerical experiment, which has aroused some interest in the machine learning community. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms