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Simultaneous Source Localization and Formation via a Distributed Sign gradient-free algorithm

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2024年第1期11卷 319-327页

作者： Du, Yong Chen, Fei Xiang, Linying Guo, Ge Chen, Guanrong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Tiangong Univ Sch Artificial Intelligence Tianjin 300387 Peoples R China Northeastern Univ Qinhuangdao Campus Qinhuangdao 066004 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

This article develops a distributed sign gradient-free algorithm for simultaneous source localization and formation of a multirobot system. A distinguished feature of the algorithm is that it takes into account robots' measure noise as well as ternary communication, which significantly reduces the communication cost of the overall network. Considering the presence of noise, the algorithm is designed to be gradient-free. In addition, an inherent connection is established between the selection of control parameters and the convergence property of the sign gradient-free algorithm, as well as signal strength, noisy intensity, formation radius, and the number of informed robots.

关键词： Distributed source localization formation control gradient-free algorithm multiagent system signum function

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Thermo-Economic Optimization of Spiral Plate HX by Means of gradient and gradient-free algorithm

Thermo-Economic Optimization of Spiral Plate HX by Means of ...

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International Conference on Advancement in Materials, Manufacturing and Energy Engineering (ICAMME)

作者： Krishna, Musunuru Hari Dasore, Abhishek Rajak, Upendra Konijeti, Ramakrishna Verma, Tikendra Nath SRK Inst Technol Dept Mech Engn Vijayawada 521108 India RGM Coll Engn & Technol Sch Mech Engn Nandyal 518501 India Koneru Lakshmaiah Educ Fdn Dept Mech Engn Guntur 522502 Andhra Pradesh India Maulana Azad Natl Inst Technol Dept Mech Engn Bhopal 462003 India

ISBN: (纸本)9789811683411;9789811683404

Spiral plate heat exchangers (SPHX) play prominent role in process industries with the fact that rotational motion of the fluid in the channel eliminates the occurrence of fouling. In the present study, thermo-economic optimization of SPHX was performed considering the standard Hall equation as the objective function. For a specified heat duty design, parameters, namely pressure drop in hot and cold fluids, outer spiral diameter, plate length and plate width, are optimized with the objective of minimized cost, using gradient (fmincon) and gradient-free (genetic algorithm (GA)) optimization algorithms. The outcomes were compared to the data attained from experimental works found in the literature. It was found that GA outperforms fmincon in terms of cost minimization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

关键词： gradient algorithm gradient-free algorithm Maximization Spiral plate heat exchanger Thermo-economics

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Distributed Nonconvex Optimization via Bounded gradient-free Inputs

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第4期55卷 2560-2568页

作者： Du, Yong Chen, Fei Xiang, Linying Feng, Gang Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Tiangong Univ Sch Artificial Intelligence Tianjin 300387 Peoples R China City Univ Hong Kong Dept Biomed Engn Hong Kong Peoples R China

This article investigates the problem of distributed optimization over multiagent networks with the global objective being the sum of a set of possibly nonconvex functions. Based on recent developments in distributed average tracking as well as distributed extremum seeking, a distributed bounded gradient-free optimization algorithm is proposed. It is shown that the proposed scheme is able to solve nonconvex optimization problems with arbitrary prescribed accuracy. The relationship between the optimization error and control parameters is established with the error bound's explicit dependence on the bounds of agents' control inputs, which clearly demonstrates a tradeoff between the optimization error and input bound. An illustrative example is included to validate the effectiveness of proposed scheme.

关键词： Optimization Linear programming Heuristic algorithms Network topology Graph theory Costs Upper bound Multi-agent systems Lower bound Location awareness Bounded input extremum seeking gradient-free algorithm multiagent systems

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gradient-free distributed online optimization in networks

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CONTROL THEORY AND TECHNOLOGY 2025年第2期23卷 207-220页

作者： Liu, Yuhang Zhao, Wenxiao Zhang, Nan Lv, Dongdong Zhang, Shuai State Grid Informat & Telecommun Grp Co Ltd Acad Informat & Commun Res Beijing 102211 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

In this paper, we consider the distributed online optimization problem on a time-varying network, where each agent on the network has its own time-varying objective function and the goal is to minimize the overall loss accumulated. Moreover, we focus on distributed algorithms which do not use gradient information and projection operators to improve the applicability and computational efficiency. By introducing the deterministic differences and the randomized differences to substitute the gradient information of the objective functions and removing the projection operator in the traditional algorithms, we design two kinds of gradient-free distributed online optimization algorithms without projection step, which can economize considerable computational resources as well as has less limitations on the applicability. We prove that both of two algorithms achieves consensus of the estimates and regrets of Olog(T)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O\left( \log (T)\right) $$\end{document} for local strongly convex objective, respectively. Finally, a simulation example is provided to verify the theoretical results.

关键词： Distributed optimization Online convex optimization gradient-free algorithm Projection-free algorithm

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gradient-free algorithms for distributed online convex optimization

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ASIAN JOURNAL OF CONTROL 2023年第4期25卷 2451-2468页

作者： Liu, Yuhang Zhao, Wenxiao Dong, Daoyi Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Univ New South Wales Sch Engn & Informat Technol Canberra ACT Australia Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China

In this paper, we consider the distributed bandit convex optimization of time-varying objective functions over a network. By introducing perturbations into the objective functions, we design a deterministic difference and a randomized difference to replace the gradient information of the objective functions and propose two classes of gradient-free distributed algorithms. We prove that both the two classes of algorithms achieve regrets of O(T-3/4) for convex objective functions and O(T-2/3) for strongly convex objective functions, with respect to the time index T and consensus of the estimates established as well. Simulation examples are given justifying the theoretical results.

关键词： distributed algorithm gradient-free algorithm multiagent system online convex optimization

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Distributed Online Optimization With gradient-free Design 38

Distributed Online Optimization With Gradient-free Design

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38th Chinese Control Conference (CCC)

作者： Wang, Lingfei Wang, Yinghui Hong, Yiguang Chinese Acad Sci Univ Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China

ISBN: (纸本)9789881563972

This paper studies an online optimization problem, where the cost function at every time stage is summation of a group of local cost functions assigned to to a single agent/node in a multi-agent network. We propose a distributed algorithm by combining the gradient descent method and consensus design. Then we prove that the regret of our online optimization algorithm as well as the accumulative disagreement for the multi-agent network is sublinear under a properly chosen stepsize.

关键词： Distributed online optimization gradient-free algorithm Regret

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Almost sure convergence of randomised-difference descent algorithm for stochastic convex optimisation

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IET CONTROL THEORY AND APPLICATIONS 2021年第17期15卷 2183-2194页

作者： Geng, Xiaoxue Huang, Gao Zhao, Wenxiao Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

Stochastic gradient descent algorithm is a classical and useful method for stochastic optimisation. While stochastic gradient descent has been theoretically investigated for decades and successfully applied in machine learning such as training of deep neural networks, it essentially relies on obtaining the unbiased estimates of gradients/subgradients of the objective functions. In this paper, by constructing the randomised differences of the objective function, a gradient-free algorithm, named the stochastic randomised-difference descent algorithm, is proposed for stochastic convex optimisation. Under the strongly convex assumption of the objective function, it is proved that the estimates generated from stochastic randomised-difference descent converge to the optimal value with probability one, and the convergence rates of both the mean square error of estimates and the regret functions are established. Finally, some numerical examples are prsented.

关键词： Optimisation techniques Optimisation Interpolation and function approximation (numerical analysis) gradient-free algorithm stochastic convex optimisation convex programming stochastic programming gradient methods Numerical analysis stochastic gradient descent algorithm convergence mean square error mean square error methods randomised-difference descent algorithm regret functions randomised-difference descent converge

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Indirect methods for improving parameter estimation of PET kinetic models

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MEDICAL PHYSICS 2019年第4期46卷 1777-1784页

作者： Huang, Hsuan-Ming Liu, Chih-Chieh Lin, Chieh Natl Taiwan Univ Coll Med Inst Med Device & Imaging 1Sect 1Jen Ai Rd Taipei 100 Zhongzheng Dist Taiwan Univ Calif Davis Dept Biomed Engn Davis CA 95616 USA Chang Gung Mem Hosp Dept Nucl Med 5 Fu Shin St Kwei Shan Taoyuan County Taiwan

PurposeParametric images obtained from kinetic modeling of dynamic positron emission tomography (PET) data provide a new way of visualizing quantitative parameters of the tracer kinetics. However, due to the high noise level in pixel-wise image-driven time-activity curves, parametric images often suffer from poor quality and accuracy. In this study, we propose an indirect parameter estimation framework which aims to improve the quality and quantitative accuracy of parametric images. MethodsThree different approaches related to noise reduction and advanced curve fitting algorithm are used in the proposed framework. First, dynamic PET images are denoised using a kernel-based denoising method and the highly constrained backprojection technique. Second, gradient-free curve fitting algorithms are exploited to improve the accuracy and precision of parameter estimates. Third, a kernel-based post-filtering method is applied to parametric images to further improve the quality of parametric images. Computer simulations were performed to evaluate the performance of the proposed framework. Results and conclusionsThe simulation results showed that when compared to the Gaussian filtering, the proposed denoising method could provide better PET image quality, and consequentially improve the quality and quantitative accuracy of parametric images. In addition, gradient-free optimization algorithms (i.e., pattern search) can result in better parametric images than the gradient-based curve fitting algorithm (i.e., trust-region-reflective). Finally, our results showed that the proposed kernel-based post-filtering method could further improve the precision of parameter estimates while maintaining the accuracy of parameter estimates.

关键词： gradient-free algorithm image denoising kernel kinetic model

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A novel policy gradient algorithm with PSO-based parameter exploration for continuous control

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 90卷

作者： Liu, Tundong Li, Liduan Shao, Guifang Wu, Xiaomin Huang, Meng Xiamen Univ Dept Automat Xiamen 361005 Fujian Peoples R China Xiamen Key Lab Big Data Intelligent Anal & Decis Xiamen 361005 Fujian Peoples R China

Continuous control has attracted enormous attention due to its essential role in real-world applications. However, it is considerably difficult to be addressed through explicitly modeling in practice. As promising approaches, model-free policy gradient (PG) based methods in reinforcement learning (RL), however, suffer from slow convergence and complex computation owing to the high variance of gradient estimating and sophisticated backpropagation. Therefore, in this paper, a gradient-free policy gradient algorithm with PSO-based parameter exploration (PG-PSOPE) is proposed for continuous control tasks. To reduce variance and improve convergence rate, the PSO is combined with PG to provide a novel way for training policy network in RL. Experimental results of simulated physical control tasks verify the effectiveness of the proposed algorithm. Besides, the PG-PSOPE is superior in both convergence speed and final performance to the typical on-policy PG and the off-policy deep RL method. Furthermore, the PG-PSOPE exhibits the simplicity and high effectiveness by comparison of training time under different tasks, and its running time is reduced by 58 times compared with other gradient-based methods for the best case.

关键词： Continuous control Model-free reinforcement learning Convergence improvement gradient-free algorithm Policy gradient Parameter exploration

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A review of closed-loop reservoir management

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Petroleum Science 2015年第1期12卷 114-128页

作者： Hou, Jian Zhou, Kang Zhang, Xian-Song Kang, Xiao-Dong Xie, Hai State Key Laboratory of Heavy Oil Processing China University of Petroleum Qingdao 266580 Shandong China School of Petroleum Engineering China University of Petroleum Qingdao 266580 Shandong China CNOOC Research Institute Beijing 100027 China ZTE Corporation Shenzhen 518055 Guangdong China

The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management: automatic history matching and reservoir production optimization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir management;summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms;analyzes the characteristics and application conditions of these optimization methods;and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization. © 2015, The Author(s).

关键词： Artificial intelligence algorithm Automatic history matching Closed-loop reservoir management gradient-based algorithm gradient-free algorithm Reservoir production optimization

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