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Multisource and Hidden Source-Based Knowledge Transfer for Solving Dynamic Multiobjective Optimization Problems
Wei Song;Zhi Liu;Jian Yu;Xiaoyan Sun;Yaochu Jin;Khin Wee Lai
IEEE Transactions on Evolutionary Computation
Year: 2025 | Early Access Article |
Recently, transfer learning-based dynamic multiobjective optimization algorithms (TL-DMOAs) have been shown to be very promising in solving dynamic multiobjective optimization problems (DMOPs). However, it is difficult for them to model knowledge capable of delineating the Pareto optimal solutions (POSs) found in each historical environment, because the POSs’ distribution cannot be adequately refl...Show More
Dual Sparse Variational Autoencoder-driven Interactive Estimation of Distribution Algorithm with User Generated Contents
2024 International Conference on New Trends in Computational Intelligence (NTCI)
Year: 2024 | Conference Paper |
Personalized search and recommendation tasks in a big data environment have attracted wide attention from researchers while also presenting significant challenges.This paper proposed a dual sparse variational autoencoder-driven interactive estimation of distribution algorithm with user generated contents, which considers digging deeper into user generated contents and combining with intelligent ev...Show More
Multiform Differential Evolution With Elite-Guided Knowledge Transfer for Coal Mine Integrated Energy Systems Constrained Dispatch
Canyun Dai;Xiaoyan Sun;Hejuan Hu;Wei Song;Yong Zhang;Dunwei Gong
IEEE Transactions on Evolutionary Computation
Year: 2024 | Early Access Article |
The dispatch optimization of coal mine integrated energy system is challenging due to high dimensionality, strong coupling constraints, and multi-objective. Existing constrained multi-objective evolutionary algorithms struggle with locating multiple small and irregular feasible regions when solving the dispatch problem. To address this issue, we here develop a multiform evolutionary algorithm fram...Show More
Low-Carbon Economic Operation Optimization of Mine Integrated Energy System Considering Energy Supply Uncertainty
Xiaokang Wu;Yong Zhang;Xiaotian Fei;Hejuan Hu;Xiaoyan Sun;Dunwei Gong;Xianfang Song
Mine integrated energy system (MIES) can promote the utilization of derived energy and achieve multi-energy complementation and ecological protection. Now it gradually becomes an important focus for scientific carbon reduction and carbon neutrality. To reduce the impact of uncertain prediction differences on the system during the process of using mine derived energy, a low-carbon economic operatio...Show More
Operating Cost and Peak Load Driven Day-Ahead Multivariate Load Forecasting For Integrated Coal Mine Energy Systems
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Day-ahead multivariate load forecasting is the foundation of establishing scheduling strategies for integrated coal mine energy systems. However, conventional evaluation metrics cannot reliably indicate whether the forecasting model can reduce the system’s operating costs, and peak load plays a significant role in the day-ahead load forecasting errors, which is crucial for the system’s stable and ...Show More
Dynamic Multi-Task Interactive Evolutionary Optimization Algorithm with Search Space Alignment
2024 IEEE Congress on Evolutionary Computation (CEC)
Year: 2024 | Conference Paper |
The interactive evolutionary multi-task optimization approach assisted by surrogate models has proven successful in enhancing individualized recommendation performance. However, in light of the dynamically changing user preferences, it becomes imperative to further develop more powerful knowledge sharing strategy to improve the interactive multi-task optimization efficiency. A probability model-as...Show More
A Multitask Multiobjective Operation Optimization Method for Coal Mine Integrated Energy System
The operation optimization problem of coal mine integrated energy system (CMIES) is characterized by multiobjective, strong constraints, large scale, and mixed variables. It is difficult for existing multiobjective evolutionary algorithms to obtain a set of nondominated solutions with good convergence and uniform distribution, primarily due to the absence of suitable constraint-handling techniques...Show More
Dispatch of a Coal Mine-Integrated Energy System: Optimization Model with Interval Variables and Lower Carbon Emission
Hejuan Hu;Xiaoyan Sun;Bo Zeng;Dunwei Gong;Yong Zhang;Patrick Nyonganyi;Henerica Tazvinga
Tsinghua Science and Technology
Cited by: Papers (2)
In the coal mining process, a large amount of Coal Mine-Associated energy (CMAE), such as coal mine methane and underground wastewater, is produced. Research on the modeling and optimization dispatching of a Coal Mine-Integrated Energy System (CMIES) with CMAE effectively saves energy and reduces carbon pollution. CMAE has great uncertainties owing to the affections of the hydrogeology conditions ...Show More
Linear Regression-Based Autonomous Intelligent Optimization for Constrained Multiobjective Problems
Yan Wang;Xiaoyan Sun;Yong Zhang;Dunwei Gong;Hejuan Hu;Mingcheng Zuo
It is very challenging to autonomously generate algorithms suitable for constrained multiobjective optimization problems due to the diverse performance of existing algorithms. In this article, we propose a linear regression (LR)-based autonomous intelligent optimization method. It first extracts typical features of a constrained multiobjective optimization problem by focused sampling to form a fea...Show More
An Interval Multi-Objective Evolutionary Generation Algorithm for Product Design Change Plans in Uncertain Environments
Rui-zhao Zheng;Yong Zhang;Xiao-yan Sun;Dun-wei Gong;Xiao-zhi Gao
IEEE Transactions on Evolutionary Computation
Year: 2024 | Early Access Article |
Design change is an important issue in complex product development projects. In a complex product with numerous parts (also known as components), the change of one key part may spread to other parts associated with it, generating a chain reaction throughout the entire project. Therefore, it is necessary to select a suitable change plan involving only fewer crucial parts in order to enhance the pro...Show More
Multi-granularity Autonomous Intelligent Method for Operation Optimization of Integrated Coal Mine Energy Systems
2023 International Joint Conference on Neural Networks (IJCNN)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
An intelligent optimization algorithm is only valid for solving some problems, which difficultly solves all operation optimization problems of integrated coal mine energy systems with different characteristics. An optimization paradigm usually consists of multiple operators/strategies, each of which is suitable for solving different problems. It is difficult for an operator/strategy to ensure that...Show More
Source Free Semi-Supervised Transfer Learning for Diagnosis of Mental Disorders on fMRI Scans
Yao Hu;Zhi-An Huang;Rui Liu;Xiaoming Xue;Xiaoyan Sun;Linqi Song;Kay Chen Tan
The high prevalence of mental disorders gradually poses a huge pressure on the public healthcare services. Deep learning-based computer-aided diagnosis (CAD) has emerged to relieve the tension in healthcare institutions by detecting abnormal neuroimaging-derived phenotypes. However, training deep learning models relies on sufficient annotated datasets, which can be costly and laborious. Semi-super...Show More
Communication Efficient Federated Learning With Heterogeneous Structured Client Models
Federated learning (FL) has recently attracted much attention due to its superior performance in privacy protection when processing data from different terminals. However, homogeneous deep learning models are pervasively adopted without considering the difference between distinct data in various clients, resulting in low learning performance and high communication costs. This paper thus proposes a...Show More
Objective-Constraint Mutual-Guided Surrogate-Based Particle Swarm Optimization for Expensive Constrained Multimodal Problems
Yong Zhang;Xin-Fang Ji;Xiao-Zhi Gao;Dun-Wei Gong;Xiao-Yan Sun
Expensive constraint multimodal optimization problems (ECMMOPs) have such characteristics as expensive objectives and constraints, and multiple optimal modalities simultaneously, which pose severe challenges to evolutionary optimization methods. This article studies an objective-constraint mutual-guided surrogate-assisted particle swarm optimization algorithm for the kind of problem, aiming to dis...Show More
Multiparticipant Federated Feature Selection Algorithm With Particle Swarm Optimization for Imbalanced Data Under Privacy Protection
In the real world, data describing the same learning task may be distributed in different institutions (called participants), and these participants cannot share their own data due to the need of privacy protection. How to select an optimal feature subset on the basis of ensuring the data privacy of each participant is an important and challenging topic. At the same time, data on part participants...Show More
Multisurrogate-Assisted Multitasking Particle Swarm Optimization for Expensive Multimodal Problems
IEEE Transactions on Cybernetics
Cited by: Papers (36)
Many real-world applications can be formulated as expensive multimodal optimization problems (EMMOPs). When surrogate-assisted evolutionary algorithms (SAEAs) are employed to tackle these problems, they not only face the problem of selecting surrogate models but also need to tackle the problem of discovering and updating multiple modalities. Different optimization problems and different stages of ...Show More
A Multitask Bee Colony Band Selection Algorithm With Variable-Size Clustering for Hyperspectral Images
Band selection (BS) is a widely used dimensionality reduction technique for hyperspectral images. However, most of existing evolutionary algorithms focus on searching a globally optimal band subset under a fixed size, and their obtained band subsets may still contain a large number of redundant bands. In order to simultaneously obtain multiple optimal band subsets with different sizes, this articl...Show More
Multisource Heterogeneous User-Generated Contents-Driven Interactive Estimation of Distribution Algorithms for Personalized Search
Personalized search is essentially a complex qualitative optimization problem, and interactive evolutionary algorithms (EAs) have been extended from EAs to adapt to solving it. However, the multisource user-generated contents (UGCs) in the personalized services have not been concerned on in the adaptation. Accordingly, we here present an enhanced restricted Boltzmann machine (RBM)-driven interacti...Show More
Clustering-Guided Particle Swarm Feature Selection Algorithm for High-Dimensional Imbalanced Data With Missing Values
Feature selection (FS) in data with class imbalance or missing values has received much attention from researchers due to their universality in real-world applications. However, for data with both the two characteristics above, there is still a lack of the corresponding FS algorithm. Due to the complex coupling relationship between missing data and class imbalance, the need for better FS method be...Show More
Attack-Defense Game based Security Analysis for Supply Chain System Subject to Change Design
Yun-Long Zhang;Shan-Shan Gao;Yu Wang;Xiaoyan Sun;Qing-Kui Li
2021 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Supply chain system (SCS) is a network control system integrated with a series of facilities that procure raw materials, process them into intermediate goods and the final products, which are distributively delivered to customers. It is an important task to guarantee the stability when the system is suffering external attacks and interference signals, which leads to the system status change accord...Show More
Multi-source product change influence assessment based on similar node clustering
Mingqun Liu;Yong Zhang;Ruizhao Zheng;Faguang Wang;Xiaoyan Sun
2021 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Accurately assessing and identifying essential nodes in complex product networks is vital to reduce the impact of design changes. Most of existing node influence evaluation methods are for the case of single-source design changes, so that their applications have certain limitations. In view of this, this paper studies a change influence evaluation method for multisource design changes. First, a de...Show More
Model Construction for Complex Product Design Change with Improved Dendritic Neural Network
Zimin Li;Xiaoyan Sun;Xun Chen;Yong Zhang;Qingkui Li;Chen Peng
2021 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Design changes are widespread during the produce development due to many uncertain factors, e.g., the impact of the epidemic on the supply chain, changes in customer demands, and upgrades demand by the produce production itself. It is very important for companies to predict product’s parameter changes and clarify the mechanism of change propagation for saving costs and improving efficiency. Howeve...Show More
A Reduced Mixed Representation Based Multi-Objective Evolutionary Algorithm for Large-Scale Overlapping Community Detection
Yongkang Luo;Kening Zhang;Haipeng Yang;Feng Liu;Shuai Luo;Lei Zhang;Xiaoyan Sun
2021 IEEE Congress on Evolutionary Computation (CEC)
Year: 2021 | Conference Paper |
Cited by: Papers (5)
In recent years, the application of multi-objective evolutionary algorithms (MOEAs) to overlapping community detection in complex networks has been a hot research topic. However, the existing MOEAs for detecting overlapping communities show poor scalability to large-scale networks due to the fact that the encoding length of individuals is usually equal to the number of all nodes in the network. To...Show More
Dual-Surrogate-Assisted Cooperative Particle Swarm Optimization for Expensive Multimodal Problems
Various real-world applications can be classified as expensive multimodal optimization problems. When surrogate-assisted evolutionary algorithms (SAEAs) are employed to tackle these problems, they not only face a contradiction between the precision of surrogate models and the cost of individual evaluations but also have the difficulty that surrogate models and problem modalities are hard to match....Show More
Enhanced Interactive Estimation of Distribution Algorithms with Attention Mechanism and Restricted Boltzmann Machine
2020 IEEE Congress on Evolutionary Computation (CEC)
Year: 2020 | Conference Paper |
Interactive Estimation of Distribution Algorithm (IEDA), by integrating users interactions with Estimation of Distribution Algorithm, is powerful for efficient personalized search when the probability model and fitness function are well designed. We here propose an improved IEDA by using attention mechanism strengthened Restricted Boltzmann Machine (RBM). An attention mechanism assisted RBM model ...Show More
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