Performance Prediction Model Based on Multi-Task Learning and Co-Evolutionary Strategy for Ground Source Heat Pump System

Zhuang, Zhitao; Chen, Wei; Li, Bing; Xu, Peng; Guo, Yifei; Ren, Jiachang

doi:10.1109/access.2019.2936508

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Island-EMT [167] Examination timetabling problem EMHH [78] Graph coloring problem EMHH [78] Minimum inter-cluster routing cost clustered tree problem (InterCluMRCT) CC-MFEA [65] Clustered shortest path tree problem (CluSTP) None [62], None [64], CC-MFEA [65], N-MFEA [68], N-MFEA [70] Real-world problem Machine learning Time series prediction problem MFGP [61] Performance prediction problem None [168] Gene regulatory network (GRN) reconstruction MMMA-FCM [169] Community detection MUMI [73] Chaotic time series prediction problem HD-MFEA neuroevolution [145] Training deep neural networks (DNN) problem AMTO [170], None [171] Fuzzy cognitive map (FCM) learning MMMA-FCM [169] Symbolic regression problem (SRP) MFGP [61] Multi-classification problem mXOF [138], EMC-GEP [172] Binary classification problem MFGP [59] Automatic hyperparameter tuning of machine learning models TEMO-MPS [109] Fuzzy system optimization problem MTGFS [72] Association mining problem MFEA [76] Classification problem DMSPSO [89], PSO-EMT [173], MMT-ELM [174] Table 3. Cont.…”

Section: Domain Problem Algorithmsmentioning

confidence: 99%

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Wang

et al. 2021

Mathematics

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Traditional evolution algorithms tend to start the search from scratch. However, real-world problems seldom exist in isolation and humans effectively manage and execute multiple tasks at the same time. Inspired by this concept, the paradigm of multi-task evolutionary computation (MTEC) has recently emerged as an effective means of facilitating implicit or explicit knowledge transfer across optimization tasks, thereby potentially accelerating convergence and improving the quality of solutions for multi-task optimization problems. An increasing number of works have thus been proposed since 2016. The authors collect the abundant specialized literature related to this novel optimization paradigm that was published in the past five years. The quantity of papers, the nationality of authors, and the important professional publications are analyzed by a statistical method. As a survey on state-of-the-art of research on this topic, this review article covers basic concepts, theoretical foundation, basic implementation approaches of MTEC, related extension issues of MTEC, and typical application fields in science and engineering. In particular, several approaches of chromosome encoding and decoding, intro-population reproduction, inter-population reproduction, and evaluation and selection are reviewed when developing an effective MTEC algorithm. A number of open challenges to date, along with promising directions that can be undertaken to help move it forward in the future, are also discussed according to the current state. The principal purpose is to provide a comprehensive review and examination of MTEC for researchers in this community, as well as promote more practitioners working in the related fields to be involved in this fascinating territory.

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Section: Domain Problem Algorithmsmentioning

confidence: 99%

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Wang

et al. 2021

Mathematics

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Trend Prediction and Operation Alarm Model Based on PCA-Based MTL and AM for the Operating Parameters of a Water Pumping Station

Shao,

Mei,

Liu

et al. 2024

Sensors

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In order to effectively predict the changing trend of operating parameters in the pump unit and carry out fault diagnosis and alarm processes, a trend prediction model is proposed in this paper based on PCA-based multi-task learning (MTL) and an attention mechanism (AM). The multi-task learning method based on PCA was used to process the operating data of the pump unit to make full use of the historical data to extract the key common features reflecting the operating state of the pump unit. The attention mechanism (AM) is introduced to dynamically allocate the weight coefficient of common feature mapping for highlighting the key common features and improving the prediction accuracy of the model when predicting the trend of data change for new working conditions. The model is tested with the actual operating data of a pumping station unit, and the calculation results of different models are compared and analyzed. The results show that the introduction of multi-task learning and attention mechanisms can improve the stability and accuracy of the trend prediction model compared with traditional single-task learning and static common feature mapping weights. According to the threshold analysis of the monitoring statistical parameters of the model, a multi-stage alarm model of pump unit operation condition monitoring can be established, which provides a theoretical basis for optimizing operation and maintenance management strategy in the process of pump station management.

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Performance Prediction Model Based on Multi-Task Learning and Co-Evolutionary Strategy for Ground Source Heat Pump System

Cited by 2 publications

References 24 publications

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Trend Prediction and Operation Alarm Model Based on PCA-Based MTL and AM for the Operating Parameters of a Water Pumping Station

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