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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-Branch Semantic Enhancement Network Joint With Iterative Self-Matching Training Strategy for Semi-Supervised Semantic Segmentation
Feng Xiao;Ruyu Liu;Xu Cheng;Haoyu Zhang;Jianhua Zhang;Yaochu Jin
IEEE Transactions on Emerging Topics in Computational Intelligence
Year: 2025 | Early Access Article |
With the rapid development of deep learning, supervised training methods have become increasingly sophisticated. There has been a growing trend towards semi-supervised and weakly supervised learning methods. This shift in focus is partly due to the challenges in obtaining large amounts of labeled data. The key to semi-supervised semantic segmentation is how to efficiently use a large amount of unl...Show More
Locomotion Joint Angle and Moment Estimation With Soft Wearable Sensors for Personalized Exosuit Control
Luying Feng;Lianghong Gui;Wenzhu Xu;Xiang Wang;Canjun Yang;Yaochu Jin;Wei Yang
Recent advancements in flexible sensing and machine learning have positioned soft sensors as promising alternatives to traditional methods for human posture detection. However, most research has centered on calibration, with limited progress in practical applications due to the challenges posed by diverse users and complex scenarios such as human-robot interaction. To address these challenges, thi...Show More
Efficient Large-Scale Expensive Optimization via Surrogate-assisted Sub-problem Selection
IEEE Transactions on Evolutionary Computation
Year: 2025 | Early Access Article |
Traditional large-scale evolutionary algorithms are limited in their ability to solve certain real-world applications with high-dimensional, black-box, and computationally expensive objectives due to their need for numerous objective evaluations. Surrogate-assisted evolutionary algorithms (SAEAs) have shown effective for expensive black-box optimization by relying on inexpensive surrogate models. ...Show More
An Indicator-Based Evolutionary Algorithm for Large-Scale Constrained Multi-objective Optimization
Xiaoyu Zhong;Xiangjuan Yao;Kangjia Qiao;Dunwei Gong;Yaochu Jin
IEEE Transactions on Evolutionary Computation
Year: 2025 | Early Access Article |
Most existing constrained multi-objective evolutionary algorithms (CMOEAs) experience a dramatic performance degradation when solving large-scale constrained multi-objective optimization problems (LSCMOPs), since they converge very slowly and easily get trapped in local optima due to the loss of diversity. To enhance the efficiency of tackling LSCMOPs, this paper proposes an indicator-based evolut...Show More
Surrogate-Assisted Multiobjective Gene Selection for Cell Classification from Large-Scale Single-Cell RNA Sequencing Data
IEEE Transactions on Evolutionary Computation
Year: 2025 | Early Access Article |
Accurate cell classification is crucial but expensive for large-scale single-cell RNA sequencing (scRNA-seq) analysis. Gene selection (GS) emerges as a pivotal technique in identifying gene subsets of scRNA-seq for classification accuracy improvement and gene scale reduction. Nevertheless, the rising scale of scRNA-seq data presents challenges to existing GS methods regarding performance and compu...Show More
Data Stream Driven Dynamic Multiobjective Optimization Using Surrogate Transfer
IEEE Transactions on Emerging Topics in Computational Intelligence
Year: 2025 | Early Access Article |
Most existing dynamic multiobjective evolutionary algorithms assume that the real objective function evaluations are easily acquired during the optimization process. However, in some situations, the optimization algorithm cannot invoke the objective functions and is exclusively driven by the solutions within the continuously incoming data stream. Although these solutions come with their own object...Show More
ParetoLens: A Visual Analytics Framework for Exploring Solution Sets of Multi-Objective Evolutionary Algorithms [Application Notes]
In the domain of multi-objective optimization, evolutionary algorithms are distinguished by their capability to generate a diverse population of solutions that navigate the trade-offs inherent among competing objectives. This has catalyzed the ascension of evolutionary multi-objective optimization (EMO) as a prevalent approach. Despite the effectiveness of the EMO paradigm, the analysis of resulta...Show More
CVDLLM: Automated Cardiovascular Disease Diagnosis with Large-Language-Model-Assisted Graph Attentive Feature Interaction
IEEE Transactions on Artificial Intelligence
Year: 2025 | Early Access Article |
Electrocardiogram (ECG) measurements are essential for detecting and treating cardiovascular disease (CVD). However, manual evaluation of ECGs is prone to errors due to morphological variations. Although machine learning methods have shown promise in diagnosing diseases, automatic CVD diagnosis based on ECGs is still suffering from low diagnosis accuracy due to the limited usage of time-series inf...Show More
DeFedTL: A Decentralized Federated Transfer Learning Method for Fault Diagnosis
Danya Xu;Yi Liu;Guanghui Wen;Yaochu Jin;Tianyou Chai;Tao Yang
Deep learning has become increasingly important in fault diagnosis, but it relies on a large amount of high-quality labeled data. Collecting data from distributed machines can expand the dataset, but it usually leads to privacy concerns. Moreover, since the operating conditions are complex in real-world applications, the collected training data and the test data often have different distributions....Show More
Distributed Optimization With Asynchronous Computation and Event-Triggered Communication
The implementation of distributed optimization, depending on the application, imposes escalating demands on communication and computational synchronization, with the general desire for the robust performance in the face of computationally slow agents and the avoidance of unnecessary communication. In this article, we propose a distributed algorithm with asynchronous computation and event-triggered...Show More
Preference Prediction-Based Evolutionary Multiobjective Optimization for Gasoline Blending Scheduling
Gasoline blending scheduling is challenging, involving multiple conflicting objectives and a large decision space with many mixed integers. Due to these difficulties, one promising solution is to use preference-based multiobjective evolutionary algorithms (PBMOEAs). However, in practical applications, suitable preferences of decision makers are often difficult to generalize and summarize from thei...Show More
Global and Cluster Structural Balance via a Priority Strategy Based Memetic Algorithm
Structural balance in signed networks aims to search for the structure with the least imbalance of relationships. However, most existing studies focus on global structural balance, and little work has considered local structural balance. In this study, an optimization model is proposed based on the weak definition of structural balance theory. The model incorporates both the global imbalance and m...Show More
Large-Scale Continuous-Time Crude Oil Scheduling: A Variable-Length Evolutionary Optimization Approach
Evolutionary algorithms (EAs) have significantly contributed to addressing large-scale crude oil scheduling problems (COSPs) that exceed the capabilities of mathematical programming. However, current research using EAs to resolve COSPs is limited to discrete-time models that assume uniform operation durations. While this simplification transforms COSPs into fixed-length optimization problems suita...Show More
Surrogate-Assisted Neighborhood Search With Only a Few Weight Vectors for Expensive Large-Scale Multiobjective Binary Optimization
IEEE Transactions on Evolutionary Computation
Year: 2024 | Early Access Article |
Large-scale multiobjective binary optimization problems often occur in real-world applications, where the function evaluation can only be performed through computationally expensive simulations, which renders standard exact and heuristic methods ineffective. Aggregation-based surrogate-assisted multiobjective evolutionary algorithms have been developed and shown to be promising for solving such pr...Show More
Dynamic Multi-Objective Home Appliance Scheduling Using Transfer Learning Based Evolutionary Algorithm
2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS)
Year: 2024 | Conference Paper |
Household Energy Management Systems (HEMS) have garnered significant public attention for their ability to optimize the operation of home appliances. Despite extensive research on optimal home appliance scheduling, these scheduling problems are inherently dynamic multi-objective optimization issues that can be effectively addressed using dynamic multi-objective evolutionary algorithms. This paper ...Show More
Force Calibration of Soft-Sensing Unit for Flexible Exoskeleton
Luying Feng;Lianghong Gui;Xiang Wang;Hui Lyu;Canjun Yang;Yaochu Jin;Wei Yang
2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Soft sensors are increasingly used in wearable de-vices as well as soft robotics. This paper introduces a novel, lightweight soft-sensing unit and a new data collection method, aimed at enabling the implementation of soft sensors in robotics. These advancements are designed to replace traditional sen-sors, providing precise data capture and comfortable wearing experience. Through machine learning,...Show More
A PSO-Assisted Reinforcement Learning Algorithm for Job Shop Scheduling
2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS)
Year: 2024 | Conference Paper |
The Job Shop Scheduling Problem (JSSP) is a critical and complex optimization challenge in manufacturing. Recently, Reinforcement Learning (RL)-based methods have garnered significant attention due to their promising performance in improving scheduling efficiency. However, traditional RL algorithms often suffer from inefficiencies in exploring the vast solution space, resulting in suboptimal polic...Show More
Model Management Agent for Expensive Constrained Multi-Objective Optimization
2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS)
Year: 2024 | Conference Paper |
Surrogate-assisted evolutionary algorithms (SAEAs) have been extensively studied for their effectiveness in solving expensive optimization problems in various domains. A critical component of the SAEA framework is model management, which is used to query new solutions and update the surro-gate model. This paper introduces a novel model management agent based on reinforcement learning. The develope...Show More
A Multi-objective Bayesian Optimization Algorithm with Online Knowledge Transfer
2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS)
Year: 2024 | Conference Paper |
Since transfer learning can learn effective knowledge from the source task to help solve the target task, it offers a powerful tool for assisting multi-objective evolutionary Bayesian optimization in solving expensive multi-objective problems in the presence of data paucity using. Most existing optimization algorithms use genetic operators or construct Gaussian process models to achieve knowledge ...Show More
Data Representation with Robust Hypergraph Coding
2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS)
Year: 2024 | Conference Paper |
This paper introduces a novel matrix factorization framework, Data Representation with Robust Hypergraph Coding (MFRHL), designed to enhance unsupervised learning techniques in data mining and machine learning. Traditional matrix factorization methods often fail to capture complex semantic information in datasets. We leverage hypergraph-based Laplacian Eigenmaps to construct a discriminative basis...Show More
A Personalized Federated Framework for Document-Level Biomedical Relation Extraction
2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS)
Year: 2024 | Conference Paper |
Document-Ievel relation extraction (RE), a critical task in natural language processing, involves classifying relationships between entities mentioned in documents. Traditional document-level RE methods often rely on centralized datasets, which necessitates the collection of documents from various sources and their storage on a single machine. However, this brings up issues regarding privacy prote...Show More
Large Language Models (LLMs) such as OpenAI’s ChatGPT have achieved surprisingly huge progresses in the field of Natural Language Processing (NLP). This paper aims to present an immersive introduction to LLMs from the perspective of generative models. The main components of the training process of LLMs are explained, and an example of LLMs for AI-generated contents is given. This short paper is a ...Show More
Federated Loss Exploration for Improved Convergence on Non-IID Data
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Federated learning (FL) has emerged as a ground-breaking paradigm in machine learning (ML), offering privacy-preserving collaborative model training across diverse datasets. Despite its promise, FL faces significant hurdles in non-identically and independently distributed (non-IID) data scenarios, where most existing methods often struggle with data heterogeneity and lack robustness in performance...Show More
FOG: A Unified Framework for Federated Combinatorial Optimization on Graphs
2024 IEEE Congress on Evolutionary Computation (CEC)
Year: 2024 | Conference Paper |
With the revolutionary advancements in deep learning technologies, neural combinatorial optimization (NCO) emerges as a promising field for solving complex combinatorial optimization problems (COPs) in real-world scenarios. By lever-aging the learning ability of deep neural networks, NCO trains network models to learn the mapping from problem instances to optimal solutions, offering advantages in ...Show More
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