All Outputs (100)

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.

Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles (2020)
Journal Article
He, Q., Meng, X., Qu, R., & Xi, R. (2020). Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles. Mathematics, 8(8), Article 1311. https://doi.org/10.3390/math8081311

Connected and Autonomous Vehicle (CAV)-related initiatives have become some of the fastest expanding in recent years, and have started to affect the daily lives of people. More and more companies and research organizations have announced their initia... Read More about Machine Learning-Based Detection for Cyber Security Attacks on 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.

Fuzzy Superpixels based Semi-supervised Similarity-constrained CNN for PolSAR Image Classification (2020)
Journal Article
Guo, Y., Sun, Z., Qu, R., Jiao, L., Liu, F., & Zhang, X. (2020). Fuzzy Superpixels based Semi-supervised Similarity-constrained CNN for PolSAR Image Classification. Remote Sensing, 12(10), Article 1694. https://doi.org/10.3390/rs12101694

Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classifica... Read More about Fuzzy Superpixels based Semi-supervised Similarity-constrained CNN for PolSAR Image Classification.

A Multiobjective Computation Offloading Algorithm for Mobile Edge Computing (2020)
Journal Article
Song, F., Xing, H., Luo, S., Zhan, D., Dai, P., & Qu, R. (2020). A Multiobjective Computation Offloading Algorithm for Mobile Edge Computing. IEEE Internet of Things Journal, 7(9), 8780 -8799. https://doi.org/10.1109/jiot.2020.2996762

In mobile edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption... Read More about A Multiobjective Computation Offloading Algorithm for Mobile Edge Computing.

A Unified Framework of Graph-based Evolutionary Multitasking Hyper-heuristic (2020)
Journal Article
Hao, X., Qu, R., & Liu, J. (2021). A Unified Framework of Graph-based Evolutionary Multitasking Hyper-heuristic. IEEE Transactions on Evolutionary Computation, 25(1), 35-47. https://doi.org/10.1109/tevc.2020.2991717

In recent research, hyper-heuristics have attracted increasing attention among researchers in various fields. The most appealing feature of hyper-heuristics is that they aim to provide more generalized solutions to optimization problems by searching... Read More about A Unified Framework of Graph-based Evolutionary Multitasking Hyper-heuristic.

The General Combinatorial Optimization Problem: Towards Automated Algorithm Design (2020)
Journal Article
Qu, R., Kendall, G., & Pillay, N. (2020). The General Combinatorial Optimization Problem: Towards Automated Algorithm Design. IEEE Computational Intelligence Magazine, 15(2), 14-23. https://doi.org/10.1109/mci.2020.2976182

This paper defines a new combinatorial optimisation problem, namely General Combinatorial Optimisation Problem (GCOP), whose decision variables are a set of parametric algorithmic components, i.e. algorithm design decisions. The solutions of GCOP, i.... Read More about The General Combinatorial Optimization Problem: Towards Automated Algorithm Design.

A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices (2019)
Journal Article
Cui, T., Bai, R., Ding, S., Parkes, A. J., Qu, R., He, F., & Li, J. (2020). A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices. Soft Computing, 24, 2809–2831. https://doi.org/10.1007/s00500-019-04517-y

© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Portfolio optimization is one of the most important problems in the finance field. The traditional Markowitz mean-variance model is often unrealistic since it relies on the perfect market... Read More about A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices.

Information Retrieval for Evidence-Based Policy Making Applied to Lifelong Learning (2019)
Conference Proceeding
Clos, J., Qu, R., & Atkin, J. (2019). Information Retrieval for Evidence-Based Policy Making Applied to Lifelong Learning. In Artificial Intelligence XXXVI (487-493). https://doi.org/10.1007/978-3-030-34885-4_41

© 2019, Springer Nature Switzerland AG. Policy making involves an extensive research phase during which existing policies which are similar to the one under development need to be retrieved and analysed. This phase is time-consuming for the following... Read More about Information Retrieval for Evidence-Based Policy Making Applied to Lifelong Learning.

A Survey of Deep Learning-Based Object Detection (2019)
Journal Article
Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., & Qu, R. (2019). A Survey of Deep Learning-Based Object Detection. IEEE Access, 7, 128837-128868. https://doi.org/10.1109/access.2019.2939201

A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes (2019)
Journal Article
Chen, B., Qu, R., Bai, R., & Laesanklang, W. (2020). A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes. RAIRO: Operations Research, 54(5), 1467-1494. https://doi.org/10.1051/ro/2019080

Based on a real-life container transport problem, a model of Open Periodic Vehicle Routing Problem with Time Windows (OPVRPTW) is proposed in this paper. In a wide planning horizon, which is divided into a number of shifts, a fixed number of trucks a... Read More about A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes.

Accelerated genetic algorithm based on search-space decomposition for change detection in remote sensing images (2019)
Journal Article
Mu, C., Li, C., Liu, Y., Qu, R., & Jiao, L. (2019). Accelerated genetic algorithm based on search-space decomposition for change detection in remote sensing images. Applied Soft Computing, 84, Article 105727. https://doi.org/10.1016/j.asoc.2019.105727

Detecting change areas among two or more remote sensing images is a key technique in remote sensing. It usually consists of generating and analyzing a difference image thus to produce a change map. Analyzing the difference image to obtain the change... Read More about Accelerated genetic algorithm based on search-space decomposition for change detection in remote sensing images.

Controlling understaffing with conditional Value-at-Risk constraint for an integrated nurse scheduling problem under patient demand uncertainty (2019)
Journal Article
He, F., Chaussalet, T., & Qu, R. (2019). Controlling understaffing with conditional Value-at-Risk constraint for an integrated nurse scheduling problem under patient demand uncertainty. Operations Research Perspectives, 6, Article 100119. https://doi.org/10.1016/j.orp.2019.100119

Nursing workforce management is a challenging decision-making task in hospitals. The decisions are made across different timescales and levels from strategic long-term staffing budget to mid-term scheduling. These decisions are interconnected and imp... Read More about Controlling understaffing with conditional Value-at-Risk constraint for an integrated nurse scheduling problem under patient demand uncertainty.

Modelling the home health care nurse scheduling problem for patients with long-term conditions in the UK (2019)
Conference Proceeding
He, F., Chaussalet, T., & Qu, R. (2019). Modelling the home health care nurse scheduling problem for patients with long-term conditions in the UK

In this work, using a Behavioural Operational Research (BOR) perspective, we develop a model for the Home Health Care Nurse Scheduling Problem (HHCNSP) with application to renal patients taking Peritoneal Dialysis (PD) at their own homes as treatment... Read More about Modelling the home health care nurse scheduling problem for patients with long-term conditions in the UK.

Multi-objective ant colony optimization algorithm based on decomposition for community detection in complex networks (2019)
Journal Article
Mu, C., Zhang, J., Liu, Y., Qu, R., & Huang, T. (2019). Multi-objective ant colony optimization algorithm based on decomposition for community detection in complex networks. Soft Computing, 23, 12683-12709. https://doi.org/10.1007/s00500-019-03820-y

Community detection aims to identify topological structures and discover patterns in complex networks, which presents an important problem of great significance. The problem can be modeled as an NP hard combinatorial optimization problem, to which mu... Read More about Multi-objective ant colony optimization algorithm based on decomposition for community detection in complex networks.

Hyper-heuristics: theory and applications (2018)
Book
Pillay, N., & Qu, R. (2018). Hyper-heuristics: theory and applications. Cham, Switzerland: Springer Nature. doi:10.1007/978-3-319-96514-7

This introduction to the field of hyper-heuristics presents the required foundations and tools and illustrates some of their applications. The authors organized the 13 chapters into three parts. The first, hyper-heuristic fundamentals and theory, pro... Read More about Hyper-heuristics: theory and applications.

Iteration-related various learning particle swarm optimization for quay crane scheduling problem (2018)
Conference Proceeding
Yu, M., Cong, X. C., Niu, B., & Qu, R. (2018). Iteration-related various learning particle swarm optimization for quay crane scheduling problem. In Bio-inspired computing: theories and applications: 13th International Conference, BIC-TA 2018, Beijing, China, November 2–4, 2018, Proceedings, Part II (201-212). https://doi.org/10.1007/978-981-13-2829-9_19

Quay crane scheduling is critical in reducing operation costs at container terminals. Designing a schedule to handling containers in an efficient order can be difficult. For this problem which is proved NP-hard, heuristic algorithms are effective to... Read More about Iteration-related various learning particle swarm optimization for quay crane scheduling problem.

Modified mixed-dimension chaotic particle swarm optimization for liner route planning with empty container repositioning (2018)
Conference Proceeding
Yu, M., Chen, Z., Chen, L., Qu, R., & Niu, B. (2018). Modified mixed-dimension chaotic particle swarm optimization for liner route planning with empty container repositioning. In Bio-inspired Computing: Theories and Applications (296-307). https://doi.org/10.1007/978-981-13-2829-9_27

Empty container repositioning has become one of the important issues in ocean shipping industry. Researchers often solve these problems using linear programming or simulation. For large-scale problems, heuristic algorithms showed to be preferable due... Read More about Modified mixed-dimension chaotic particle swarm optimization for liner route planning with empty container repositioning.

A hyper-heuristic with two guidance indicators for bi-objective mixed-shift vehicle routing problem with time windows (2018)
Journal Article
Chen, B., Qu, R., Bai, R., & Laesanklang, W. (2018). A hyper-heuristic with two guidance indicators for bi-objective mixed-shift vehicle routing problem with time windows. Applied Intelligence, 48(12), 4937–4959. https://doi.org/10.1007/s10489-018-1250-y

In this paper, a Mixed-Shift Vehicle Routing Problem is proposed based on a real-life container transportation problem. In a long planning horizon of multiple shifts, transport tasks are completed satisfying the time constraints. Due to the different... Read More about A hyper-heuristic with two guidance indicators for bi-objective mixed-shift vehicle routing problem with time windows.