All Outputs (105)

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. Springer Nature. https://doi.org/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)
Presentation / Conference Contribution
Yu, M., Cong, X. C., Niu, B., & Qu, R. (2018, September). Iteration-related various learning particle swarm optimization for quay crane scheduling problem. Presented at The 13th International Conference on Bio-inspired Computing: Theories and Applications, Beijing, China

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)
Presentation / Conference Contribution
Yu, M., Chen, Z., Chen, L., Qu, R., & Niu, B. (2018, November). Modified mixed-dimension chaotic particle swarm optimization for liner route planning with empty container repositioning. Presented at The 13th International Conference on Bio-inspired Computing: Theories and Applications, Beijing, China

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)
Presentation / Conference Contribution
Chen, B., Qu, R., & Ishibuchi, H. Variable-depth adaptive large meighbourhood search algorithm for Open Periodic Vehicle Routing Problem with time windows. Presented at The 19th International Conference on Harbor, Maritime & Multimodal Logistics Modelling and Simulation

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)
Presentation / Conference Contribution
Mu, C., Cheng, H., Feng, W., Liu, Y., & Qu, R. Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems. Presented at 2017 IEEE Congress on Evolutionary Computation (CEC 2017)

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)
Presentation / Conference Contribution
Mu, C., Li, C., Liu, Y., Sun, M., Jiao, L., & Qu, R. (2017, June). Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm. Presented at 2017 IEEE Congress on Evolutionary Computation (CEC 2017), Donostia, Spain

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)
Presentation / Conference Contribution
He, Q., Meng, X., & Qu, R. A survey on cyber security of CAV. Presented at 2017 Forum on Cooperative Positioning and Service (CPGPS17)

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)
Presentation / Conference Contribution
Chen, J., Bai, R., Dong, H., Qu, R., & Kendall, G. A dynamic truck dispatching problem in marine container terminal. Presented at 2016 IEEE Symposium on Computational Intelligence in Scheduling and Network Design (IEEE SSCI 2016)

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)
Presentation / Conference Contribution
Xing, H., Xu, L., Qu, R., & Qu, Z. A quantum inspired evolutionary algorithm for dynamic multicast routing with network coding. Presented at 16th International Symposium on Communications and Information Technologies (ISCIT 2016)

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)
Presentation / Conference Contribution
He, F., & Qu, R. Hybridising local search with Branch-and-Bound for constrained portfolio selection problems. Presented at 30th EUROPEAN Conference on Modelling and Simulation

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)
Presentation / Conference Contribution
Pinheiro, R. L., Landa-Silva, D., Qu, R., Yanaga, E., & Constantino, A. A. (2016, April). Towards an efficient API for optimisation problems data. Presented at ICEIS 2016 - Proceedings of the 18th International Conference on Enterprise Information Systems, Rome, Italy

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.