All Outputs (93)

Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments (2024)
Journal Article
Chen, X., Qu, R., Dong, J., Dong, H., & Bai, R. (2024). Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments. Applied Soft Computing, 166, Article 112190. https://doi.org/10.1016/j.asoc.2024.112190

Efficient truck dispatching strategies are paramount in container terminal operations. The quality of these strategies heavily relies on accurate and expedient simulations, which provide a crucial platform for training and evaluating dispatching algo... Read More about Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments.

Deep Reinforcement Learning Assisted Genetic Programming Ensemble Hyper-Heuristics for Dynamic Scheduling of Container Port Trucks (2024)
Journal Article
Chen, X., Bai, R., Qu, R., Dong, J., & Jin, Y. (2024). Deep Reinforcement Learning Assisted Genetic Programming Ensemble Hyper-Heuristics for Dynamic Scheduling of Container Port Trucks. IEEE Transactions on Evolutionary Computation, https://doi.org/10.1109/tevc.2024.3381042

Efficient truck dispatching is crucial for optimizing container terminal operations within dynamic and complex scenarios. Despite good progress being made recently with more advanced uncertainty-handling techniques, existing approaches still have gen... Read More about Deep Reinforcement Learning Assisted Genetic Programming Ensemble Hyper-Heuristics for Dynamic Scheduling of Container Port Trucks.

A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem (2024)
Journal Article
Lin, B., Li, J., Cui, T., Jin, H., Bai, R., Qu, R., & Garibaldi, J. (2024). A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem. Expert Systems with Applications, 249, Article 123515. https://doi.org/10.1016/j.eswa.2024.123515

The online bin packing problem is a well-known optimization challenge that finds application in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called FuzzyPatternPack(FPP), which leverages fuzzy inference and pattern... Read More about A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem.

Self-Bidirectional Decoupled Distillation for Time Series Classification (2024)
Journal Article
Xiao, Z., Xing, H., Qu, R., Li, H., Feng, L., Zhao, B., & Yang, J. (2024). Self-Bidirectional Decoupled Distillation for Time Series Classification. IEEE Transactions on Artificial Intelligence, https://doi.org/10.1109/tai.2024.3360180

Over the years, many deep learning algorithms have been developed for time series classification (TSC). A learning model’s performance usually depends on the quality of the semantic information extracted from lower and higher levels within the repres... Read More about Self-Bidirectional Decoupled Distillation for Time Series Classification.

Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk (2024)
Journal Article
Wang, H., Bellotti, A., Qu, R., & Bai, R. (2024). Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk. Risks, 12(2), Article 31. https://doi.org/10.3390/risks12020031

Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot dis... Read More about Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk.

Densely Knowledge-Aware Network for Multivariate Time Series Classification (2024)
Journal Article
Xiao, Z., Xing, H., Qu, R., Feng, L., Luo, S., Dai, P., …Dai, Y. (2024). Densely Knowledge-Aware Network for Multivariate Time Series Classification. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(4), 2192-2204. https://doi.org/10.1109/tsmc.2023.3342640

Multivariate time series classification (MTSC) based on deep learning (DL) has attracted increasingly more research attention. The performance of a DL-based MTSC algorithm is heavily dependent on the quality of the learned representations providing s... Read More about Densely Knowledge-Aware Network for Multivariate Time Series Classification.

CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition (2023)
Journal Article
Xiao, Z., Tong, H., Qu, R., Xing, H., Luo, S., Zhu, Z., …Feng, L. (2023). CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition. IEEE Transactions on Neural Networks and Learning Systems, 1-15. https://doi.org/10.1109/TNNLS.2023.3344294

This article proposes a semi-supervised contrastive capsule transformer method with feature-based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) techniques for wearable human activity recognition (HAR), called... Read More about CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition.

Automated design of local search algorithms: Predicting algorithmic components with LSTM (2023)
Journal Article
Meng, W., & Qu, R. (2024). Automated design of local search algorithms: Predicting algorithmic components with LSTM. Expert Systems with Applications, 237(Part A), Article 121431. https://doi.org/10.1016/j.eswa.2023.121431

With a recently defined AutoGCOP framework, the design of local search algorithms has been defined as the composition of elementary algorithmic components. The effective compositions of the best algorithms thus retain useful knowledge of effective al... Read More about Automated design of local search algorithms: Predicting algorithmic components with LSTM.

Automated design of search algorithms based on reinforcement learning (2023)
Journal Article
Yi, W., & Qu, R. (2023). Automated design of search algorithms based on reinforcement learning. Information Sciences, 649, Article 119639. https://doi.org/10.1016/j.ins.2023.119639

Automated algorithm design has attracted increasing research attention recently in the evolutionary computation community. The main design decisions include selection heuristics and evolution operators in the search algorithms. Most existing studies,... Read More about Automated design of search algorithms based on reinforcement learning.

Deep Contrastive Representation Learning With Self-Distillation (2023)
Journal Article
Xiao, Z., Xing, H., Zhao, B., Qu, R., Luo, S., Dai, P., …Zhu, Z. (2024). Deep Contrastive Representation Learning With Self-Distillation. IEEE Transactions on Emerging Topics in Computational Intelligence, 8(1), 3-15. https://doi.org/10.1109/tetci.2023.3304948

Recently, contrastive learning (CL) is a promising way of learning discriminative representations from time series data. In the representation hierarchy, semantic information extracted from lower levels is the basis of that captured from higher level... Read More about Deep Contrastive Representation Learning With Self-Distillation.

Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning (2023)
Journal Article
Aickelin, U., Khorshidi, H. A., Qu, R., & Charkhgard, H. (2023). Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning. IEEE Transactions on Evolutionary Computation, 27(4), 746-748. https://doi.org/10.1109/tevc.2023.3292528

We are very pleased to introduce this special issue on multiobjective evolutionary optimization for machine learning (MOML). Optimization is at the heart of many machine-learning techniques. However, there is still room to exploit optimization in mac... Read More about Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning.

Models of Representation in Computational Intelligence [Guest Editorial] (2023)
Journal Article
Nobile, M. S., Manzoni, L., Ashlock, D. A., & Qu, R. (2023). Models of Representation in Computational Intelligence [Guest Editorial]. IEEE Computational Intelligence Magazine, 18(1), 20-21. https://doi.org/10.1109/MCI.2022.3223482

Computational Intelligence (CI) provides a set of powerful tools to effectively tackle complex computational tasks: global optimization methods (e.g., evolutionary computation, swarm intelligence), machine learning (e.g., neural networks), fuzzy reas... Read More about Models of Representation in Computational Intelligence [Guest Editorial].

Automated algorithm design using proximal policy optimisation with identified features (2022)
Journal Article
Yi, W., Qu, R., & Jiao, L. (2023). Automated algorithm design using proximal policy optimisation with identified features. Expert Systems with Applications, 216, Article 119461. https://doi.org/10.1016/j.eswa.2022.119461

Automated algorithm design is attracting considerable recent research attention in solving complex combinatorial optimisation problems, due to that most metaheuristics may be particularly effective at certain problems or certain instances of the same... Read More about Automated algorithm design using proximal policy optimisation with identified features.

Cooperative Double-Layer Genetic Programming Hyper-Heuristic for Online Container Terminal Truck Dispatching (2022)
Journal Article
Chen, X., Bai, R., Qu, R., & Dong, H. (2023). Cooperative Double-Layer Genetic Programming Hyper-Heuristic for Online Container Terminal Truck Dispatching. IEEE Transactions on Evolutionary Computation, 27(5), 1220-1234. https://doi.org/10.1109/TEVC.2022.3209985

In a marine container terminal, truck dispatching is a crucial problem that impacts on the operation efficiency of the whole port. Traditionally, this problem is formulated as an offline optimisation problem, whose solutions are, however, impractical... Read More about Cooperative Double-Layer Genetic Programming Hyper-Heuristic for Online Container Terminal Truck Dispatching.

An Efficient Federated Distillation Learning System for Multitask Time Series Classification (2022)
Journal Article
Xing, H., Xiao, Z., Qu, R., Zhu, Z., & Zhao, B. (2022). An Efficient Federated Distillation Learning System for Multitask Time Series Classification. IEEE Transactions on Instrumentation and Measurement, 71, https://doi.org/10.1109/TIM.2022.3201203

This paper proposes an efficient federated distillation learning system (EFDLS) for multi-task time series classification (TSC). EFDLS consists of a central server and multiple mobile users, where different users may run different TSC tasks. EFDLS ha... Read More about An Efficient Federated Distillation Learning System for Multitask Time Series Classification.

Automated Design of Metaheuristics Using Reinforcement Learning within a Novel General Search Framework (2022)
Journal Article
Yi, W., Qu, R., Jiao, L., & Niu, B. (2023). Automated Design of Metaheuristics Using Reinforcement Learning within a Novel General Search Framework. IEEE Transactions on Evolutionary Computation, 27(4), 1072-1084. https://doi.org/10.1109/TEVC.2022.3197298

Metaheuristic algorithms have been investigated intensively to address highly complex combinatorial optimisation problems. However, most metaheuristic algorithms have been designed manually by researchers of different expertise without a consistent f... Read More about Automated Design of Metaheuristics Using Reinforcement Learning within a Novel General Search Framework.

A Collaborative Learning Tracking Network for Remote Sensing Videos (2022)
Journal Article
Li, X., Jiao, L., Zhu, H., Liu, F., Yang, S., Zhang, X., …Qu, R. (2023). A Collaborative Learning Tracking Network for Remote Sensing Videos. IEEE Transactions on Cybernetics, 53(3), 1954-1967. https://doi.org/10.1109/TCYB.2022.3182993

With the increasing accessibility of remote sensing videos, remote sensing tracking is gradually becoming a hot issue. However, accurately detecting and tracking in complex remote sensing scenes is still a challenge. In this article, we propose a col... Read More about A Collaborative Learning Tracking Network for Remote Sensing Videos.

Automated design of search algorithms: Learning on algorithmic components (2021)
Journal Article
Meng, W., & Qu, R. (2021). Automated design of search algorithms: Learning on algorithmic components. Expert Systems with Applications, 185, Article 115493. https://doi.org/10.1016/j.eswa.2021.115493

This paper proposes AutoGCOP, a new general framework for automated design of local search algorithms. In a recently established General Combinatorial Optimisation Problem (GCOP) model, the problem of algorithm design itself is defined as a combinato... Read More about Automated design of search algorithms: Learning on algorithmic components.

Towards a Severity Assessment Method for Potential Cyber Attacks to Connected and Autonomous Vehicles (2020)
Journal Article
He, Q., Meng, X., & Qu, R. (2020). Towards a Severity Assessment Method for Potential Cyber Attacks to Connected and Autonomous Vehicles. Journal of Advanced Transportation, 2020, 1-15. https://doi.org/10.1155/2020/6873273

CAV (connected and autonomous vehicle) is a crucial part of intelligent transportation systems. CAVs utilize both sensors and communication components to make driving decisions. A large number of companies, research organizations, and governments hav... Read More about Towards a Severity Assessment Method for Potential Cyber Attacks to Connected and Autonomous Vehicles.

Assessing hyper-heuristic performance (2020)
Journal Article
Pillay, N., & Qu, R. (2021). Assessing hyper-heuristic performance. Journal of the Operational Research Society, 72(11), 2503-2516. https://doi.org/10.1080/01605682.2020.1796538

Limited attention has been paid to assessing the generality performance of hyper-heuristics. The performance of hyper-heuristics has been predominately assessed in terms of optimality which is not ideal as the aim of hyper-heuristics is not to be com... Read More about Assessing hyper-heuristic performance.