Skip to main content

Research Repository

Advanced Search

Outputs (94)

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.

Sequential Rule Mining for Automated Design of Meta-heuristics (2023)
Conference Proceeding
Meng, W., & Qu, R. (2023). Sequential Rule Mining for Automated Design of Meta-heuristics. In GECCO’23 Companion: Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (1727-1735). https://doi.org/10.1145/3583133.3596303

With a recently defined AutoGCOP framework, the design of local search algorithms can be defined as the composition of the basic elementary algorithmic components. These compositions into the best algorithms thus retain useful knowledge of effective... Read More about Sequential Rule Mining for Automated Design of Meta-heuristics.

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.

An Improved Ant Colony Approach for the Competitive Traveling Salesmen Problem (2022)
Conference Proceeding
Du, X., Bai, R., Cui, T., Qu, R., & Li, J. (2022). An Improved Ant Colony Approach for the Competitive Traveling Salesmen Problem. In 2022 IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/cec55065.2022.9870414

A competitive traveling salesmen problem is a variant of traveling salesman problem in that multiple agents compete with each other in visiting a number of cities. The agent who is the first one to visit a city will receive a reward. Each agent aims... Read More about An Improved Ant Colony Approach for the Competitive Traveling Salesmen Problem.

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.

Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification (2021)
Journal Article
Guo, Y., Jiao, L., Qu, R., Sun, Z., Wang, S., Wang, S., & Liu, F. (2022). Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5217818. https://doi.org/10.1109/TGRS.2021.3128908

The increasing applications of polarimetric synthetic aperture radar (PolSAR) image classification demand for effective superpixels’ algorithms. Fuzzy superpixels’ algorithms reduce the misclassification rate by dividing pixels into superpixels, whic... Read More about Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification.

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.

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.

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 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.

Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization (2018)
Journal Article
Xu, Y., Ding, O., Qu, R., & Li, K. (2018). Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization. Applied Soft Computing, 68, 268-282. https://doi.org/10.1016/j.asoc.2018.03.053

In Wireless Sensor Networks (WSN), maintaining a high coverage and extending the network lifetime are two conflicting crucial issues considered by real world service providers. In this paper, we consider the coverage optimization problem in WSN with t... Read More about Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization.

Variable-depth adaptive large meighbourhood search algorithm for Open Periodic Vehicle Routing Problem with time windows (2017)
Conference Proceeding
Chen, B., Qu, R., & Ishibuchi, H. (2017). Variable-depth adaptive large meighbourhood search algorithm for Open Periodic Vehicle Routing Problem with time windows.

The Open Periodic Vehicle Routing Problem with Time Windows (OPVRPTW) is a practical transportation routing and scheduling problem arising from real-world scenarios. It shares some common features with some classic VRP variants. The problem has a tig... Read More about Variable-depth adaptive large meighbourhood search algorithm for Open Periodic Vehicle Routing Problem with time windows.

Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems (2017)
Conference Proceeding
Mu, C., Cheng, H., Feng, W., Liu, Y., & Qu, R. (2017). Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems.

Recommender system (RS) plays an important role in helping users find the information they are interested in and providing accurate personality recommendation. It has been found that among all the users, there are some user groups called “core users”... Read More about Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems.

Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm (2017)
Conference Proceeding
Mu, C., Li, C., Liu, Y., Sun, M., Jiao, L., & Qu, R. (2017). Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm. In 2017 IEEE Congress on Evolutionary Computation (CEC) - Proceedings (1150-1157). https://doi.org/10.1109/CEC.2017.7969436

This paper proposes a change detection algorithm in synthetic aperture radar (SAR) images based on the salient image guidance and an accelerated genetic algorithm (S-aGA). The difference image is first generated by logarithm ratio operator based on t... Read More about Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm.

A hybrid EDA for load balancing in multicast with network coding (2017)
Journal Article
Xing, H., Li, S., cui, Y., Yan, L., Pan, W., & Qu, R. (2017). A hybrid EDA for load balancing in multicast with network coding. Applied Soft Computing, 59, https://doi.org/10.1016/j.asoc.2017.06.003

Load balancing is one of the most important issues in the practical deployment of multicast with network coding. However, this issue has received little research attention. This paper studies how traffic load of network coding based multicast (NCM) i... Read More about A hybrid EDA for load balancing in multicast with network coding.

An improved MOEA/D algorithm for multi-objective multicast routing with network coding (2017)
Journal Article
Xing, H., Wang, Z., Li, T., Li, H., & Qu, R. (2017). An improved MOEA/D algorithm for multi-objective multicast routing with network coding. Applied Soft Computing, 59, https://doi.org/10.1016/j.asoc.2017.05.033

Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very... Read More about An improved MOEA/D algorithm for multi-objective multicast routing with network coding.

A survey on cyber security of CAV (2017)
Conference Proceeding
He, Q., Meng, X., & Qu, R. (2017). A survey on cyber security of CAV.

With the ever fast developments of technologies in science and engineering, it is believed that CAV (connected and autonomous vehicles) will come into our daily life soon. CAV could be used in many different aspects in our lives such as public transp... Read More about A survey on cyber security of CAV.

Mean-VaR portfolio optimization: a nonparametric approach (2017)
Journal Article
Lwin, K. T., Qu, R., & MacCarthy, B. L. (2017). Mean-VaR portfolio optimization: a nonparametric approach. European Journal of Operational Research, 260(2), https://doi.org/10.1016/j.ejor.2017.01.005

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz's mean-variance model, in which the vari... Read More about Mean-VaR portfolio optimization: a nonparametric approach.

A dynamic truck dispatching problem in marine container terminal (2016)
Conference Proceeding
Chen, J., Bai, R., Dong, H., Qu, R., & Kendall, G. (2016). A dynamic truck dispatching problem in marine container terminal.

In this paper, a dynamic truck dispatching problem of a marine container terminal is described and discussed. In this problem, a few containers, encoded as work instructions, need to be transferred between yard blocks and vessels by a fleet of trucks... Read More about A dynamic truck dispatching problem in marine container terminal.

An application programming interface with increased performance for optimisation problems data (2016)
Journal Article
Pinheiro, R. L., Landa-Silva, D., Qu, R., Constantino, A. A., & Yanaga, E. (in press). An application programming interface with increased performance for optimisation problems data. Journal of Management Analytics, 3(4), https://doi.org/10.1080/23270012.2016.1233514

An optimisation problem can have many forms and variants. It may consider different objectives, constraints, and variables. For that reason, providing a general application programming interface (API) to handle the problem data efficiently in all sce... Read More about An application programming interface with increased performance for optimisation problems data.

Solving the randomly generated university examination timetabling problem through Domain Transformation Approach (DTA) (2016)
Book Chapter
Nor Abdul Rahim, S. K., Bargiela, A., & Qu, R. (2016). Solving the randomly generated university examination timetabling problem through Domain Transformation Approach (DTA). In Proceedings of the International Conference on Computing, Mathematics and Statistics (iCMS 2015): bridging research endeavo. Springer Singapore. https://doi.org/10.1007/978-981-10-2772-7_8

Amongst the wide-ranging areas of the timetabling problems, educational timetabling was reported as one of the most studied and researched areas in the timetabling literature. In this paper, our focus is the university examination timetabling. Despit... Read More about Solving the randomly generated university examination timetabling problem through Domain Transformation Approach (DTA).

A quantum inspired evolutionary algorithm for dynamic multicast routing with network coding (2016)
Conference Proceeding
Xing, H., Xu, L., Qu, R., & Qu, Z. (2016). A quantum inspired evolutionary algorithm for dynamic multicast routing with network coding.

This paper studies and models the multicast routing problem with network coding in dynamic network environment, where computational and bandwidth resources are to be jointly optimized. A quantum inspired evolutionary algorithm (QEA) is developed to a... Read More about A quantum inspired evolutionary algorithm for dynamic multicast routing with network coding.

A PBIL for load balancing in network coding based multicasting (2016)
Journal Article
Xing, H., Xu, Y., Qu, R., & Xu, L. (2016). A PBIL for load balancing in network coding based multicasting. Lecture Notes in Artificial Intelligence, 9787, 34-44. https://doi.org/10.1007/978-3-319-42108-7_3

One of the most important issues in multicast is how to achieve a balanced traffic load within a communications network. This paper formulates a load balancing optimization problem in the context of multicast with network coding and proposes a modifi... Read More about A PBIL for load balancing in network coding based multicasting.

Hybridising local search with Branch-and-Bound for constrained portfolio selection problems (2016)
Conference Proceeding
He, F., & Qu, R. (2016). Hybridising local search with Branch-and-Bound for constrained portfolio selection problems.

In this paper, we investigate a constrained portfolio selection problem with cardinality constraint, minimum size and position constraints, and non-convex transaction cost. A hybrid method named Local Search Branch-and-Bound (LS-B&B) which integrates... Read More about Hybridising local search with Branch-and-Bound for constrained portfolio selection problems.

Towards an efficient API for optimisation problems data (2016)
Conference Proceeding
Pinheiro, R. L., Landa-Silva, D., Qu, R., Yanaga, E., & Constantino, A. A. (2016). Towards an efficient API for optimisation problems data. In . S. Hammoudi, . L. Maciaszek, . M. M. Missikoff, O. Camp, & . J. Cordeiro (Eds.), Proceedings of the 18th International Conference on Enterprise Information Systems . Volume 2: ICEIS (89-98). https://doi.org/10.5220/0005915800890098

The literature presents many application programming interfaces (APIs) and frameworks that provide state of the art algorithms and techniques for solving optimisation problems. The same cannot be said about APIs and frameworks focused on the problem... Read More about Towards an efficient API for optimisation problems data.

A variable neighbourhood search algorithm with compound neighbourhoods for VRPTW (2016)
Conference Proceeding
Chen, B., Qu, R., Bai, R., & Ishibuchi, H. (2016). A variable neighbourhood search algorithm with compound neighbourhoods for VRPTW.

The Vehicle Routing Problem with Time Windows (VRPTW) consists of constructing least cost routes from a depot to a set of geographically scattered service points and back to the depot, satisfying service time interval and capacity constraints. A Var... Read More about A variable neighbourhood search algorithm with compound neighbourhoods for VRPTW.

Constrained portfolio optimisation: the state-of-the-art Markowitz models (2016)
Conference Proceeding
Jin, Y., Qu, R., & Atkin, J. (2016). Constrained portfolio optimisation: the state-of-the-art Markowitz models.

This paper studies the state-of-art constrained portfolio optimisation models, using exact solver to identify the optimal solutions or lower bound for the benchmark instances at the OR-library with extended constraints. The effects of pre-assignment,... Read More about Constrained portfolio optimisation: the state-of-the-art Markowitz models.

Good Laboratory Practice for optimization research (2015)
Journal Article
Kendall, G., Bai, R., Blazewicz, J., De Causmaecker, P., Gendreau, M., John, R., …Yee, A. (2016). Good Laboratory Practice for optimization research. Journal of the Operational Research Society, 67(4), 676-689. https://doi.org/10.1057/jors.2015.77

Good Laboratory Practice has been a part of non-clinical research for over 40 years yet. Optimization Research, despite having many papers discussing standards being published over the same period of time, has yet to embrace standards that underpin i... Read More about Good Laboratory Practice for optimization research.

A Modified Ant Colony Optimization Algorithm for Network Coding Resource Minimization (2015)
Journal Article
Wang, Z., Xing, H., Li, T., Yang, Y., Qu, R., & Pan, Y. (2016). A Modified Ant Colony Optimization Algorithm for Network Coding Resource Minimization. IEEE Transactions on Evolutionary Computation, 20(3), 325-342. https://doi.org/10.1109/TEVC.2015.2457437

The paper presents a modified ant colony optimization approach for the network coding resource minimization problem. It is featured with several attractive mechanisms specially devised for solving the network coding resource minimization problem: 1)... Read More about A Modified Ant Colony Optimization Algorithm for Network Coding Resource Minimization.

A compromise based fuzzy goal programming approach with satisfaction function for multi-objective portfolio optimisation (2015)
Conference Proceeding
He, F., Qu, R., & John, R. (2015). A compromise based fuzzy goal programming approach with satisfaction function for multi-objective portfolio optimisation.

In this paper we investigate a multi-objective portfolio selection model with three criteria: risk, return and liquidity for investors. Non-probabilistic uncertainty factors in the market, such as imprecision and vagueness of investors’ preference an... Read More about A compromise based fuzzy goal programming approach with satisfaction function for multi-objective portfolio optimisation.

Price and service competition with maintenance service bundling (2015)
Journal Article
Wang, Y., Sun, L., Qu, R., & Li, G. (2015). Price and service competition with maintenance service bundling. Journal of Systems Science and Systems Engineering, 24(2), https://doi.org/10.1007/s11518-015-5267-z

In many equipment manufacturing industries, firms compete with each other not only on products price, but also on maintenance service. More and more traditional products oriented firms are offering their customers products bundled with maintenance se... Read More about Price and service competition with maintenance service bundling.

A greedy heuristic for workforce scheduling and routing with time-dependent activities constraints (2015)
Conference Proceeding
Castillo-Salazar, J. A., Landa-Silva, D., & Qu, R. (2015). A greedy heuristic for workforce scheduling and routing with time-dependent activities constraints.

We present a greedy heuristic (GHI) designed to tackle five time-dependent activities constraints (synchronisation, overlap, minimum difference, maximum difference and minimum-maximum difference) on workforce scheduling and routing problems. These ty... Read More about A greedy heuristic for workforce scheduling and routing with time-dependent activities constraints.

Hybridising heuristics within an estimation distribution algorithm for examination timetabling (2014)
Journal Article
Qu, R., Pham, D. N. T., Bai, R., & Kendall, G. (2015). Hybridising heuristics within an estimation distribution algorithm for examination timetabling. Applied Intelligence, 42(4), 679-693. https://doi.org/10.1007/s10489-014-0615-0

This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of accep... Read More about Hybridising heuristics within an estimation distribution algorithm for examination timetabling.

Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling (2014)
Journal Article
Li, J., Bai, R., Shen, Y., & Qu, R. (in press). Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling. European Journal of Operational Research, 242(3), https://doi.org/10.1016/j.ejor.2014.11.002

This paper presents a state transition based formal framework for a new search method, called Evolutionary Ruin and Stochastic Recreate, which tries to learn and adapt to the changing environments during the search process. It improves the performanc... Read More about Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling.

A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization (2014)
Journal Article
Lwin, K., Qu, R., & Kendall, G. (2014). A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization. Applied Soft Computing, 24, https://doi.org/10.1016/j.asoc.2014.08.026

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. In this paper, we studied the extended Markowitz's mean-variance p... Read More about A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization.

On minimizing coding operations in network coding based multicast: an evolutionary algorithm (2014)
Journal Article
Xing, H., Qu, R., Bai, L., & Ji, Y. (2014). On minimizing coding operations in network coding based multicast: an evolutionary algorithm. Applied Intelligence, 41(3), https://doi.org/10.1007/s10489-014-0559-4

In telecommunications networks, to enable a valid data transmission based on network coding, any intermediate node within a given network is allowed, if necessary, to perform coding operations. The more coding operations needed, the more coding resou... Read More about On minimizing coding operations in network coding based multicast: an evolutionary algorithm.

A two-stage stochastic mixed-integer program modelling and hybrid solution approach to portfolio selection problems (2014)
Journal Article
He, F., & Qu, R. (2014). A two-stage stochastic mixed-integer program modelling and hybrid solution approach to portfolio selection problems. Information Sciences, 289, https://doi.org/10.1016/j.ins.2014.08.028

In this paper, we investigate a multi-period portfolio selection problem with a comprehensive set of real-world trading constraints as well as market random uncertainty in terms of asset prices. We formulate the problem into a two-stage stochastic mi... Read More about A two-stage stochastic mixed-integer program modelling and hybrid solution approach to portfolio selection problems.

Workforce scheduling and routing problems: literature survey and computational study (2014)
Journal Article
Castillo-Salazar, J. A., Landa-Silva, D., & Qu, R. (2016). Workforce scheduling and routing problems: literature survey and computational study. Annals of Operations Research, 239(1), 39-67. https://doi.org/10.1007/s10479-014-1687-2

In the context of workforce scheduling, there are many scenarios in which personnel must carry out tasks at different locations hence requiring some form of transportation. Examples of these type of scenarios include nurses visiting patients at home,... Read More about Workforce scheduling and routing problems: literature survey and computational study.

Adaptive selection of heuristics for improving exam timetables (2014)
Journal Article
Burke, E., Qu, R., & Soghier, A. (2014). Adaptive selection of heuristics for improving exam timetables. Annals of Operations Research, 218(1), https://doi.org/10.1007/s10479-012-1140-3

This paper presents a hyper-heuristic approach which hybridises low-level heuristic moves to improve timetables. Exams which cause a soft-constraint violation in the timetable are ordered and rescheduled to produce a better timetable. It is observed... Read More about Adaptive selection of heuristics for improving exam timetables.

A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems (2014)
Journal Article
Sabar, N. R., Ayob, M., Kendall, G., & Qu, R. (2015). A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems. IEEE Transactions on Cybernetics, 45(2), 217-228. https://doi.org/10.1109/TCYB.2014.2323936

Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework... Read More about A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems.

The automatic design of hyper-heuristic framework with gene expression programming for combinatorial optimization problems (2014)
Journal Article
Sabar, N., Ayob, M., Kendall, G., & Qu, R. (2014). The automatic design of hyper-heuristic framework with gene expression programming for combinatorial optimization problems. IEEE Transactions on Evolutionary Computation, https://doi.org/10.1109/TEVC.2014.2319051

Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high level heuristic (heuristic selecti... Read More about The automatic design of hyper-heuristic framework with gene expression programming for combinatorial optimization problems.

Network flow models for intraday personnel scheduling problems (2014)
Journal Article
Brucker, P., & Qu, R. (2014). Network flow models for intraday personnel scheduling problems. Annals of Operations Research, 218(1), https://doi.org/10.1007/s10479-012-1234-y

Personnel scheduling problems can be decomposed into two stages. In the first stage for each employee the working days have to be fixed. In the second stage for each day of the planning period an intraday scheduling problem has to be solved. It consi... Read More about Network flow models for intraday personnel scheduling problems.

Computational study for workforce scheduling and routing problems (2014)
Conference Proceeding
Castillo-Salazar, J. A., Landa-Silva, D., & Qu, R. (2014). Computational study for workforce scheduling and routing problems.

We present a computational study on 112 instances of the Workforce Scheduling and Routing Problem (WSRP). This problem has applications in many service provider industries where employees visit customers to perform activities. Given their similarity,... Read More about Computational study for workforce scheduling and routing problems.

Hyper-heuristics: a survey of the state of the art (2013)
Journal Article
Burke, E., Gendreau, M., Hyde, M., Kendall, G., Ocha, G., Özcan, E., & Qu, R. (2013). Hyper-heuristics: a survey of the state of the art. Journal of the Operational Research Society, 64, https://doi.org/10.1057/jors.2013.71

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more g... Read More about Hyper-heuristics: a survey of the state of the art.

Grammatical evolution hyper-heuristic for combinatorial optimization problems (2013)
Journal Article
Sabar, N., Ayob, M., Kendall, G., & Qu, R. (2013). Grammatical evolution hyper-heuristic for combinatorial optimization problems. IEEE Transactions on Evolutionary Computation, 17(6), https://doi.org/10.1109/TEVC.2013.2281527

Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective and generic solution method by utilizing grammatic... Read More about Grammatical evolution hyper-heuristic for combinatorial optimization problems.

A path-oriented encoding evolutionary algorithm for network coding resource minimization (2013)
Journal Article
Xing, H., Qu, R., Kendall, G., & Bai, R. (2014). A path-oriented encoding evolutionary algorithm for network coding resource minimization. Journal of the Operational Research Society, 65(8), https://doi.org/10.1057/jors.2013.79

Network coding is an emerging telecommunication technique, where any intermediate node is allowed to recombine incoming data if necessary. This technique helps to increase the throughput, however, very likely at the cost of huge amount of computation... Read More about A path-oriented encoding evolutionary algorithm for network coding resource minimization.

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems (2013)
Journal Article
Xu, Y., Qu, R., & Li, R. (2013). A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems. Annals of Operations Research, 260(1), https://doi.org/10.1007/s10479-013-1322-7

This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing based strategies and a genetic local search, aiming at a more flexib... Read More about A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems.

A nondominated sorting genetic algorithm for bi-objective network coding based multicast routing problems (2013)
Journal Article
Xing, H., & Qu, R. (2013). A nondominated sorting genetic algorithm for bi-objective network coding based multicast routing problems. Information Sciences, 233, https://doi.org/10.1016/j.ins.2013.01.014

Network coding is a new communication technique that generalizes routing, where, instead of simply forwarding the packets they receive, intermediate nodes are allowed to recombine (code) together some of the data packets received from different incom... Read More about A nondominated sorting genetic algorithm for bi-objective network coding based multicast routing problems.

A harmony search algorithm for nurse rostering problems (2013)
Journal Article
Hadwan, M., Ayob, M., Kendall, G., & Qu, R. (2013). A harmony search algorithm for nurse rostering problems. Information Sciences, 233, https://doi.org/10.1016/j.ins.2012.12.025

Harmony search algorithm (HSA) is a relatively new nature-inspired algorithm. It evolves solutions in the problem search space by mimicking the musical improvisation process in seeking agreeable harmony measured by aesthetic standards. The nurse rost... Read More about A harmony search algorithm for nurse rostering problems.

Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems (2013)
Journal Article
Qu, R., Xu, Y., Castro-Gutierrez, J., & Landa-Silva, D. (2013). Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems. Journal of Heuristics, 19(2), https://doi.org/10.1007/s10732-012-9198-2

This paper presents the first investigation on applying a particle swarm optimization (PSO) algorithm to both the Steiner tree problem and the delay constrained multicast routing problem. Steiner tree problems, being the underlining models of many ap... Read More about Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems.

Domain transformation approach to deterministic optimization of examination timetables (2013)
Journal Article
Abdul Rahim, S., Bargiela, A., & Qu, R. (2013). Domain transformation approach to deterministic optimization of examination timetables. Artificial Intelligence Research, 2(1), https://doi.org/10.5430/air.v2n1p122

In this paper we introduce a new optimization method for the examinations scheduling problem. Rather than attempting direct optimization of assignments of exams to specific time-slots, we perform permutations of slots and reassignments of exams upon... Read More about Domain transformation approach to deterministic optimization of examination timetables.

A time predefined variable depth search for nurse rostering (2013)
Journal Article
Burke, E., Curtois, T., Qu, R., & Vanden Berghe, G. (2013). A time predefined variable depth search for nurse rostering. INFORMS Journal on Computing, 25(3), https://doi.org/10.1287/ijoc.1120.0510

This paper presents a variable depth search for the nurse rostering problem. The algorithm works by chaining together single neighbourhood swaps into more effective compound moves. It achieves this by using heuristics to decide whether to continue ex... Read More about A time predefined variable depth search for nurse rostering.

Tabu assisted guided local search approaches for freight service network design (2011)
Journal Article
Bai, R., Kendall, G., Qu, R., & Atkin, J. A. (2012). Tabu assisted guided local search approaches for freight service network design. Information Sciences, 189, https://doi.org/10.1016/j.ins.2011.11.028

The service network design problem (SNDP) is a core problem in freight transportation. It involves the determination of the most cost-effective transportation network and the character- istics of the corresponding services, subject to various constra... Read More about Tabu assisted guided local search approaches for freight service network design.

A survey of search methodologies and automated system development for examination timetabling (2008)
Journal Article
Qu, R., Burke, E., McCollum, B., Merlot, L., & Lee, S. (2008). A survey of search methodologies and automated system development for examination timetabling. Journal of Scheduling, 12(1), https://doi.org/10.1007/s10951-008-0077-5

Examination timetabling is one of the most important administrative activities that takes place in all academic institutions. In this paper, we present a critical discussion of the research on exam timetabling which has taken place in the last decade... Read More about A survey of search methodologies and automated system development for examination timetabling.

Hybridizations within a graph based hyper-heuristic framework for university timetabling problems (2008)
Journal Article
Qu, R., & Burke, E. (2008). Hybridizations within a graph based hyper-heuristic framework for university timetabling problems. Journal of the Operational Research Society, 60(9), https://doi.org/10.1057/jors.2008.102

A significant body of recent literature has explored various research directions in hyper-heuristics (which can be thought as heuristics to choose heuristics). In this paper, we extend our previous work to construct a unified graph-based hyper-heuris... Read More about Hybridizations within a graph based hyper-heuristic framework for university timetabling problems.

A graph-based hyper heuristic for timetabling problems (2007)
Journal Article
Burke, E., MacCloumn, B., Meisels, A., Petrovic, S., & Qu, R. (2007). A graph-based hyper heuristic for timetabling problems. European Journal of Operational Research, 176(1), https://doi.org/10.1016/j.ejor.2005.08.012

This paper presents an investigation of a simple generic hyper-heuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within the hyperheuristic framework, a Tabu Search approach is employed to... Read More about A graph-based hyper heuristic for timetabling problems.

Case Based Heuristic Selection for Timetabling Problems (2006)
Journal Article
Burke, E., Petrovic, S., & Qu, R. (2006). Case Based Heuristic Selection for Timetabling Problems. Journal of Scheduling, 9(2),

This paper presents a case-based heuristic selection approach for automated university course and exam timetabling. The method described in this paper is motivated by the goal of developing timetabling systems that are fundamentally more general than... Read More about Case Based Heuristic Selection for Timetabling Problems.

Multiple-retrieval case-based reasoning for course timetabling problems (2006)
Journal Article
Burke, E., MacCarthy, B. L., Petrovic, S., & Qu, R. (2006). Multiple-retrieval case-based reasoning for course timetabling problems. Journal of the Operational Research Society, 57(2),

The structured representation of cases by attribute graphs in a Case-Based Reasoning (CBR) system for course timetabling has been the subject of previous research by the authors. In that system, the case base is organised as a decision tree and the r... Read More about Multiple-retrieval case-based reasoning for course timetabling problems.

Hybrid Variable Neighborhood HyperHeuristicsfor Exam Timetabling Problems (2005)
Presentation / Conference
Qu, R., & Burke, E. (2005, August). Hybrid Variable Neighborhood HyperHeuristicsfor Exam Timetabling Problems. Paper presented at The Sixth Metaheuristics International Conference 2005, Vienna, Austria

This paper presents our work on analysing the high level search within a graph based hyperheuristic. The graph based hyperheuristic solves the problem at a higher level by searching through permutations of graph heuristics rather than the actual solu... Read More about Hybrid Variable Neighborhood HyperHeuristicsfor Exam Timetabling Problems.

A Decomposition, Construction and Post-Processing Approach for Nurse Rostering (2005)
Conference Proceeding
Brucker, P., Qu, R., Burke, E., & Post, G. (2005). A Decomposition, Construction and Post-Processing Approach for Nurse Rostering.

This paper presents our work on decomposing a specific nurse rostering problem by cyclically assigning blocks of shifts, which are designed considering both hard and soft constraints, to groups of nurses. The rest of the shifts are then assigned to t... Read More about A Decomposition, Construction and Post-Processing Approach for Nurse Rostering.

Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems (2005)
Book Chapter
Burke, E., Dror, M., Petrovic, S., & Qu, R. (2005). Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems. In B. Golden, S. Raghavan, & E. Wasil (Eds.), The Next Wave in Computing, Optimization, and Decision Technologies. Springer

This paper is concerned with the hybridization of two graph coloring heuristics (Saturation Degree and Largest Degree), and their application within a hyperheuristic for exam timetabling problems. Hyper-heuristics can be seen as algorithms which inte... Read More about Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems.

Analysing similarity in exam timetabling (2004)
Conference Proceeding
Burke, E., Eckersley, A., McCollum, B., Petrovic, S., & Qu, R. (2004). Analysing similarity in exam timetabling.

In this paper we carry out an investigation of some of the major features of exam timetabling problems with a view to developing a similarity measure. This similarity measure will be used within a case-based reasoning (CBR) system to match a new prob... Read More about Analysing similarity in exam timetabling.

Similarity Measures for Exam Timetabling Problems (2003)
Conference Proceeding
Burke, E., Eckersley, A., McCollum, B., Petrovic, S., & Qu, R. (2003). Similarity Measures for Exam Timetabling Problems.

A large number of heuristic algorithms have been developed over the years which have been aimed at solving examination timetabling problems. However, many of these algorithms have been developed specifically to solve one particular problem instance o... Read More about Similarity Measures for Exam Timetabling Problems.

Case-Based Reasoning as a Heuristic Selector in a Hyper-Heuristic for Course Timetabling Problems (2002)
Book Chapter
Petrovic, S., & Qu, R. (2002). Case-Based Reasoning as a Heuristic Selector in a Hyper-Heuristic for Course Timetabling Problems. In Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies. IOS Press

This paper studies Knowledge Discovery (KD) using Tabu Search and Hill Climbing within Case-Based Reasoning (CBR) as a hyper-heuristic method for course timetabling problems. The aim of the hyper-heuristic is to choose the best heuristic(s) for given... Read More about Case-Based Reasoning as a Heuristic Selector in a Hyper-Heuristic for Course Timetabling Problems.

Knowledge discovery in hyper-heuristic using case-based reasoning on course timetabling (2002)
Conference Proceeding
Burke, E., MacCarthy, B. L., Petrovic, S., & Qu, R. (2002). Knowledge discovery in hyper-heuristic using case-based reasoning on course timetabling.

This paper presents a new hyper-heuristic method using Case-Based Reasoning (CBR) for solving course timetabling problems. The term Hyper-heuristics has recently been employed to refer to 'heuristics that choose heuristics' rather than heuristics tha... Read More about Knowledge discovery in hyper-heuristic using case-based reasoning on course timetabling.

Case-based reasoning in course timetabling: an attribute graph approach (2001)
Conference Proceeding
Burke, E., MacCarthy, B. L., Petrovic, S., & Qu, R. (2001). Case-based reasoning in course timetabling: an attribute graph approach.

An earlier Case-based Reasoning (CBR) approach developed by the authors for educational course timetabling problems employed structured cases to represent the complex relationships between courses. Previous solved cases represented by attribute g... Read More about Case-based reasoning in course timetabling: an attribute graph approach.

Structured cases in case-based reasoning: re-using and adapting cases for time-tabling problems (2000)
Journal Article
Burke, E., MacCarthy, B. L., Petrovic, S., & Qu, R. (2000). Structured cases in case-based reasoning: re-using and adapting cases for time-tabling problems. Knowledge-Based Systems, 13(2-3),

In this paper, we present a case-based reasoning (CBR) approach solving educational time-tabling problems. Following the basic idea behind CBR, the solutions of previously solved problems are employed to aid finding the solutions for new problems. A... Read More about Structured cases in case-based reasoning: re-using and adapting cases for time-tabling problems.

Investigating a Hybrid Metaheuristic For Job Shop Rescheduling
Conference Proceeding
Abdullah, S., Aickelin, U., Burke, E., Din, A., & Qu, R. Investigating a Hybrid Metaheuristic For Job Shop Rescheduling.

Previous research has shown that artificial immune systems can be used to produce robust schedules in a manufacturing environment. The main goal is to develop building blocks (antibodies) of partial schedules that can be used to construct backup solu... Read More about Investigating a Hybrid Metaheuristic For Job Shop Rescheduling.