xinghuanlai Associate Professor

Supervisor of Doctorate Candidates

Supervisor of Master's Candidates

  

  • Education Level: PhD graduate

  • Professional Title: Associate Professor

  • Alma Mater: 英国诺丁汉大学

  • Supervisor of Doctorate Candidates

  • Supervisor of Master's Candidates

  • School/Department: 计算机与人工智能学院

  • Discipline:Communications and Information Systems
    Computer Science and Technology
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    Recommended Ph.D.Supervisor Recommended MA Supervisor
    Language: 中文

    Paper Publications

    A path-oriented encoding evolutionary algorithm for network coding resource minimization

    Impact Factor:0.953

    DOI number:10.1057/jors.2013.79

    Affiliation of Author(s):Univ Nottingham, Sch Comp Sci,

    Teaching and Research Group:Room C79,Jubilee Campus,Wollaton Rd, Nottingham NG

    Journal:Journal of the Operational Research Society

    Key Words:evolutionary computation,multicast routing,network coding

    Abstract: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 computational overhead, due to the packet recombination performed (ie coding operations). Hence, it is of practical importance to reduce coding operations while retaining the benefits that network coding brings to us. In this paper, we propose a novel evolutionary algorithm (EA) to minimize the amount of coding operations involved. Different from the state-of-the-art EAs which all use binary encodings for the problem, our EA is based on path-oriented encoding. In this new encoding scheme, each chromosome is represented by a union of paths originating from the source and terminating at one of the receivers. Employing path-oriented encoding leads to a search space where all solutions are feasible, which fundamentally facilitates more efficient search of EAs. Based on the new encoding, we develop three basic operators, that is, initialization, crossover and mutation. In addition, we design a local search operator to improve the solution quality and hence the performance of our EA. The simulation results demonstrate that our EA significantly outperforms the state-of-the-art algorithms in terms of global exploration and computational time.

    Note:该论文被JORS期刊选为最具代表性的十篇优秀文章之一,该报道网址为 http://www.palgrave-journals.com/jors/free_articles.html

    Co-author:Huanlai Xing*,Rong Qu,Graham Kendall,Ruibin Bai

    Document Code:10.1057/jors.2013.79

    Volume:65

    Issue:8

    Page Number:1261-1277

    ISSN No.:0160-5682

    Translation or Not:no

    Date of Publication:2014-08-01

    Included Journals:SCI、SSCI

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