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Research on reducing pollutant, improving efficiency and enhancing running safety for 1000 MW coal-fired boiler based on data-driven evolutionary optimization and online retrieval method

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APPLIED ENERGY 2025年 377卷

作者： Xu, Wentao Poh, Kimleng Song, Siheng Huang, Yaji Southeast Univ Minist Educ Key Lab Energy Thermal Convers & Proc Measurement Nanjing 210096 Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management S-117576 Singapore Singapore Dalian Power Supply Co State Grid Liaoning Elect Power Co Ltd Dalian 116001 Peoples R China

This article adopts data-driven evolutionary optimization and online retrieval method to generate the boiler online combustion decisions and improve the boiler working performance. Improved sparrow search algorithm- based least squares support vector machine (ISSA-LSSVM) is utilized to develop the boiler's static mathematical model with self-adaptive capability under steady-load operating condition at first. And then improved sparrow search algorithm and long short-term memory neural networks (ISSA-LSTM) are combined to construct the dynamical combustion model for the boiler with self-adaptive capability under variable-load running condition. Whereafter, improved strength pareto evolutionary algorithm-II (ISPEA-II), future dynamic time-steps prediction models (FDTSP) and Bollinger Band-based safety assessment technique (BBSAT) are applied to obtain a number of combustion decisions owning better working state, higher economy and lower pollutant discharge offline. At last, safety assessment, mutation operation and the determination principle of the unique similarity case are introduced to the online retrieval method to generate the boiler combustion decisions in time. To illustrate the usability of proposed online optimization approach, several different on-line combustion optimization methods are applied in a practical online optimization process. The results indicated that based on proposed optimization method, the boiler thermal efficiency was improved by 0.210% and the NOx emission was reduced by 32.132 mg/m3 and the Bollinger Band, reflecting the fluctuation characteristic of wall temperature, was reduced from 32.685 to 10.249, simultaneously. Consequently, proposed on-line combustion optimization method of boiler is effective and it can realize the on-line combustion optimization of boiler.

关键词： 1000 MW coal-fired boiler Combustion decision data-driven evolutionary optimization Bollinger band-based safety assessment Online combustion optimization

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Fine-Grained Trajectory Reconstruction by Microscopic Traffic Simulation With Dynamic data-driven evolutionary optimization

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第2期26卷 1930-1950页

作者： Naing, Htet Cai, Wentong Yu, Jinqiang Zhong, Jinghui Yu, Liang Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore Ctr Climate Res Singapore Coll Comp & Data Sci Singapore 537054 Singapore City Brain Lab AlibabaCloud Hangzhou 311121 Peoples R China South China Univ Technol Jinghui Zhong is Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

Vehicle trajectory data are essential in smart mobility applications, yet often incomplete, necessitating systematic reconstruction for effective use. Existing methods often overlook traffic rules and vehicle interactions in their reconstruction process, a research gap that becomes critical for fine-grained reconstruction of incomplete and irregular microscopic traffic data. To address this limitation, this paper introduces a novel fine-grained trajectory reconstruction (FTR) framework, particularly for urban signalized intersections, considering both traffic rules and vehicle interactions through a microscopic traffic simulation (MTS) model. This is motivated by challenging missing patterns in real-world data from Alibaba City Brain Lab and limitations in existing reconstruction approaches. To this end, the FTR problem is first formulated as an MTS-based optimization problem. Then, to solve this problem effectively under a limited computing budget, an advanced dynamic data-driven evolutionary optimization technique, D3GA++, is proposed. Through the validation involving two real-world datasets, D3GA++ has demonstrated superior performance under various missing data scenarios consistently surpassing baselines such as brute-force random search and standard evolutionary algorithm in terms of reconstruction accuracy. Our work can have crucial implications for traffic management, urban planning, and autonomous vehicle technology development.

关键词： Trajectory reconstruction microscopic traffic simulation data-driven evolutionary optimization data-driven evolutionary optimization surrogate-assisted evolutionary optimization surrogate-assisted evolutionary optimization

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A Robust Offline data-driven evolutionary optimization Algorithm for Solving Expensive Soft Pneumatic Actuator Design

A Robust Offline Data-Driven Evolutionary Optimization Algor...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Chen, Hao Wang, Zhenhua Sun, Kai Cui, Weicheng Li, Weikun Zhejiang Univ Hangzhou Peoples R China Westlake Univ Hangzhou Peoples R China

ISBN: (纸本)9798400701207

data-driven evolutionary algorithms (DDEAs) using surrogate models have been successfully applied in solving expensive optimization problems. However, many real-world engineering optimization problems can only build surrogate models with limited offline data. Inspired by the K-Fold cross validation and the Bootstrap aggregating, an offline DDEA based on elite model strategy (DDEA-EMS) is developed in this work. In DDEA-EMS, the K-Fold generalized mean square error (KGMSE) is employed to select the surrogate model with best prediction accuracy. KGMSE can evaluate the prediction accuracy of surrogates with different hyper-parameters or types. The Bootstrap aggregating method is employed to further improve the robustness of the elite model selected by KGMSE. The DDEA-EMS is successfully applied to the expensive structural optimization of a soft pneumatic actuator.

关键词： data-driven evolutionary optimization Structural optimization

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A data-driven evolutionary Transfer optimization for Expensive Problems in Dynamic Environments

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2024年第5期28卷 1396-1411页

作者： Li, Ke Chen, Renzhi Yao, Xin Univ Elect Sci & Technol China Coll Comp Sci & Engn Chengdu 611731 Peoples R China Univ Exeter Dept Comp Sci Exeter EX44 QF England Natl Innovat Inst Def Technol Coll Comp Sci & Engn Beijing 100091 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

Many real-world problems are computationally costly and the objective functions evolve over time. data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach to tackle expensive black-box optimization problems in a static environment whereas it has rarely been studied under dynamic environments. This article proposes a simple yet effective transfer learning framework to empower data-driven evolutionary optimization to solve expensive dynamic optimization problems. Specifically, a hierarchical multioutput Gaussian process is proposed to capture the correlation among data collected from different time steps with a linearly increased number of hyperparameters. Furthermore, an adaptive source task selection along with a bespoke warm staring initialization mechanisms are proposed to better leverage the knowledge extracted from previous optimization processes. By doing so, the data-driven evolutionary optimization can jump start the optimization in the new environment with a very limited computational budget. Experiments on synthetic benchmark test problems and a real-world case study demonstrate the effectiveness of our proposed algorithm in comparison with nine state-of-the-art peer algorithms.

关键词： optimization Linear programming Computational modeling Task analysis Iron Closed box Uncertainty data-driven evolutionary optimization dynamic optimization kernel methods multiouput Gaussian processes (GPs) transfer optimization

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Multiscale-Network Structure Inversion of Fractured Media Based on a Hierarchical-Parameterization and data-driven evolutionary-optimization Method

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SPE JOURNAL 2020年第5期25卷 2729-2748页

作者： Ma, Xiaopeng Zhang, Kai Yao, Chuanjin Zhang, Liming Wang, Jian Yang, Yongfei Yao, Jun Univ Petr East China Shandong Peoples R China

Efficient identification and characterization of fracture networks are crucial for the exploitation of fractured media such as naturally fractured reservoirs. Using the information obtained from borehole logs, core images, and outcrops, fracture geometries can be roughly estimated. However, this estimation always has uncertainty, which can be decreased using inverse modeling. Following the Bayes framework, a common practice for inverse modeling is to sample from the posterior distribution of uncertain parameters, given the observational data. However, a challenge for fractured reservoirs is that the fractures often occur on different scales, and these fractures form an irregular network structure that is difficult to model and predict. In this work, a multiscale-parameterization method is developed to model the fracture network. Based on this parameterization method, we present a novel history-matching approach using a data-driven evolutionary algorithm to explore the Bayesian posterior space and decrease the uncertainties of the model parameters. Empirical studies on hypothetical and outcrop-based cases demonstrate that the proposed method can model and estimate the complex multiscale-fracture network on a limited computational budget.

关键词： fracture media multiscale and hierarchical parameterization Inverse modeling data-driven evolutionary optimization

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A federated data-driven evolutionary algorithm for expensive multi-/many-objective optimization

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COMPLEX & INTELLIGENT SYSTEMS 2021年第6期7卷 3093-3109页

作者： Xu, Jinjin Jin, Yaochu Du, Wenli East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Processess Minist Educ Shanghai 200237 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

data-driven optimization has found many successful applications in the real world and received increased attention in the field of evolutionary optimization. Most existing algorithms assume that the data used for optimization are always available on a central server for construction of surrogates. This assumption, however, may fail to hold when the data must be collected in a distributed way and are subject to privacy restrictions. This paper aims to propose a federated data-driven evolutionary multi-/many-objective optimization algorithm. To this end, we leverage federated learning for surrogate construction so that multiple clients collaboratively train a radial-basis-function-network as the global surrogate. Then a new federated acquisition function is proposed for the central server to approximate the objective values using the global surrogate and estimate the uncertainty level of the approximated objective values based on the local models. The performance of the proposed algorithm is verified on a series of multi-/many-objective benchmark problems by comparing it with two state-of-the-art surrogate-assisted multi-objective evolutionary algorithms.

关键词： Federated learning Multi- many-objective optimization Bayesian optimization Surrogate data-driven evolutionary optimization

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Solving Expensive optimization Problems in Dynamic Environments With Meta-Learning

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第12期54卷 7430-7442页

作者： Zhang, Huan Ding, Jinliang Feng, Liang Tan, Kay Chen Li, Ke Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Hong Kong Polytech Univ Dept Data Sci & Artificial Intelligence Hong Kong Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England

Dynamic environments pose great challenges for expensive optimization problems, as the objective functions of these problems change over time and thus require remarkable computational resources to track the optimal solutions. Although data-driven evolutionary optimization and Bayesian optimization (BO) approaches have shown promise in solving expensive optimization problems in static environments, the attempts to develop such approaches in dynamic environments remain rarely explored. In this article, we propose a simple yet effective meta-learning-based optimization framework for solving the expensive dynamic optimization problems. This framework is flexible, allowing any off-the-shelf continuously differentiable surrogate model to be used in a plug-in manner, either in data-driven evolutionary optimization or BO approaches. In particular, the framework consists of two unique components: 1) the meta-learning component, in which a gradient-based meta-learning approach is adopted to learn experience (effective model parameters) across different dynamics along the optimization process and 2) the adaptation component, where the learned experience (model parameters) is used as the initial parameters for fast adaptation in the dynamic environment based on few shot samples. By doing so, the optimization process is able to quickly initiate the search in a new environment within a strictly restricted computational budget. Experiments demonstrate the effectiveness of the proposed algorithm framework compared to several state-of-the-art algorithms on common benchmark test problems under different dynamic characteristics.

关键词： optimization Metalearning Heuristic algorithms Task analysis Computational modeling Adaptation models Iron Bayesian optimization (BO) data-driven evolutionary optimization expensive dynamic optimization few-shot learning meta-learning

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An Uncertainty Measure for Prediction of Non-Gaussian Process Surrogates

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evolutionary COMPUTATION 2023年第1期31卷 53-71页

作者： Hu, Caie Zeng, Sanyou Li, Changhe China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

Model management is an essential component in data-driven surrogate-assisted evolutionary optimization. In model management, the solutions with a large degree of uncertainty in approximation play an important role. They can strengthen the exploration ability of algorithms and improve the accuracy of surrogates. However, there is no theoretical method to measure the uncertainty of prediction of Non-Gaussian process surrogates. To address this issue, this article proposes a method to measure the uncertainty. In this method, a stationary random field with a known zero mean is used to measure the uncertainty of prediction of Non-Gaussian process surrogates. Based on experimental analyses, this method is able to measure the uncertainty of prediction of Non-Gaussian process surrogates. The method's effectiveness is demonstrated on a set of benchmark problems in single surrogate and ensemble surrogates cases.

关键词： evolutionary computation data-driven evolutionary optimization surrogate model management Non-Gaussian process

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On Nonstationary Gaussian Process Model for Solving data-driven optimization Problems

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第4期53卷 2440-2453页

作者： Hu, Caie Zeng, Sanyou Li, Changhe Zhao, Fei China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Univ Geosci Wuhan 430074 Peoples R China Southwest Elect & Telecommun Technol Res Inst Sci & Technol Blind Signal Proc Lab Chengdu 610041 Peoples R China

In data-driven evolutionary optimization, most existing Gaussian processes (GPs)-assisted evolutionary algorithms (EAs) adopt stationary GPs (SGPs) as surrogate models, which might be insufficient for solving most optimization problems. This article finds that GPs in the optimization problems are nonstationary with great probability. We propose to employ a nonstationary GP (NSGP) surrogate model for data-driven evolutionary optimization, where the mean of the NSGP is allowed to vary with the decision variables, while its residue variance follows an SGP. In this article, the nonstationarity of GPs in the tested functions is theoretically analyzed. In addition, this article constructs an NSGP where the SGP is a degenerate case. Performance comparisons of the NSGP with the SGP and the NSGP-assisted EA (NSGP-MAEA) with the SGP-assisted EA (SGP-MAEA) are carried out on a set of benchmark problems and an antenna design problem. These comparison results demonstrate the competitiveness of the NSGP model.

关键词： optimization Predictive models Gaussian processes Benchmark testing Antennas Geology Correlation data-driven evolutionary optimization evolutionary algorithms (EAs) nonstationary Gaussian process

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A federated data-driven evolutionary algorithm

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KNOWLEDGE-BASED SYSTEMS 2021年 233卷 107532-107532页

作者： Xu, Jinjin Jin, Yaochu Du, Wenli Gu, Sai East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Univ Surrey Dept Chem Proc Engn Guildford GU2 7XH Surrey England

data-driven evolutionary optimization has witnessed great success in solving complex real-world optimization problems. However, existing data-driven optimization algorithms require that all data are centrally stored, which is not always practical and may be vulnerable to privacy leakage and security threats if the data must be collected from different devices. To address the above issue, this paper proposes a federated data-driven evolutionary optimization framework that is able to perform data driven optimization when the data is distributed on multiple devices. On the basis of federated learning, a sorted model aggregation method is developed for aggregating local surrogates based on radial-basis-function networks. In addition, a federated surrogate management strategy is suggested by designing an acquisition function that takes into account the information of both the global and local surrogate models. Empirical studies on a set of widely used benchmark functions in the presence of various data distributions demonstrate the effectiveness of the proposed framework. (c) 2021 Elsevier B.V. All rights reserved.

关键词： data-driven evolutionary optimization Distributed optimization Federated learning RBFN surrogate model

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