期刊文献+

Solving Service Selection Problem Based on a Novel Multi-Objective Artificial Bees Colony Algorithm 被引量:1

Solving Service Selection Problem Based on a Novel Multi-Objective Artificial Bees Colony Algorithm
原文传递
导出
摘要 Service computing is a new paradigm and has been widely used in many fields. The multi-objective service selection is a basic problem in service computing and it is non-deterministic polynomial(NP)-hard. This paper proposes a novel multi-objective artificial bees colony(n-MOABC) algorithm to solve service selection problem. A composite service instance is a food source in the algorithm. The fitness of a food source is related to the quality of service(QoS) attributes of a composite service instance. The search strategy of the bees are based on dominance. If a food source has not been updated in successive maximum trial(Max Trial) times, it will be abandoned. In experiment phase, a parallel approach is used based on map-reduce framework for n-MOABC algorithm. The performance of the algorithm has been tested on a variety of data sets. The computational results demonstrate the effectiveness of our approach in comparison to a novel bi-ant colony optimization(NBACO)algorithm and co-evolution algorithm. Service computing is a new paradigm and has been widely used in many fields. The multi-objective service selection is a basic problem in service computing and it is non-deterministic polynomial (NP)-hard. This paper proposes a novel multi-objective artificial bees colony (n-MOABC) algorithm to solve service selection problem. A composite service instance is a food source in the algorithm. The fitness of a food source is related to the quality of service (QoS) attributes of a composite service instance. The search strategy of the bees are based on dominance. If a food source has not been updated in successive maximum trial (Max Trial) times, it will be abandoned. In experiment phase, a parallel approach is used based on map-reduce framework for n-MOABC algorithm. The performance of the algorithm has been tested on a variety of data sets. The computational results demonstrate the effectiveness of our approach in comparison to a novel bi-ant colony optimization (NBACO) algorithm and co-evolution algorithm. © 2017, Shanghai Jiaotong University and Springer-Verlag GmbH Germany.
出处 《Journal of Shanghai Jiaotong university(Science)》 EI 2017年第4期474-480,共7页 上海交通大学学报(英文版)
基金 the National Natural Science Foundation of China(Nos.61202085,61300019) the Ningxia Hui Autonomous Region Natural Science Foundation(No.NZ13265) the Special Fund for Fundamental Research of Central Universities of Northeastern University(Nos.N120804001,N120204003)
关键词 novel multi-objective artificial bees colony(n-MOABC) MULTI-OBJECTIVE service selection search strategy Ant colony optimization Evolutionary algorithms Optimization Quality of service
  • 相关文献

参考文献1

二级参考文献79

  • 1Zhang L J, Zhang J, Cai H. Services Computing. Beijing: Springer and Tsinghua University Press, 2007.
  • 2Li Y, Lin C. QoS-aware service composition for workflow- based data-intensive applieations//Proceedings of the 2011 IEEE International Conference on Web Services (ICWS 2011). Washington, USA, 2011:452-459.
  • 3Boyd S, Vandenberghe L. Convex Optimization. Cambridge, UK: Cambridge University Press, 2004.
  • 4Cormen T H, Leiserson C E, Rivest R L, Stein C. Introduction to Algorithms. MIT, USA: MIT Press, 2005.
  • 5Wada H, Champrasert P, Suzuki J, Oha K. Multiobjectrve optimization of SLA-aware service composition//Proceedings of the IEEE Congress on Services. Honolulu, USA, 2008: 368-375.
  • 6Zhou Z, Liu F, Jin H, et al. On arbitrating the power- performance tradeoff in SaaS clouds//Proceedings of the IEEE INFOCOM 2013. Turin, Italy, 2013:872-880.
  • 7Leitner P, Hummer W, Satzger B, et al. Cost-efficient and application SLA-aware client side request scheduling in an infrastructure-as-a-service cloud//Proceedings of the 2012 IEEE 5th International Conference on Cloud Computing (CLOUD 2012). Honolulu, USA, 2012:213-220.
  • 8Kong X, Lin C, Jiang Y, et al. Efficient dynamic task scheduling in virtualized data centers with fuzzy prediction. Journal of Network and Computer Applications, 2011, 34(4) : 1068-1077.
  • 9Bessai K, Youcef S, Oulamara A, et al. Bi-criteria workflow tasks allocation and scheduling in cloud computing environ- ments//Proceedings of the 2012 IEEE 5th International Conference on Cloud Computing (CLOUD 2012). Honolulu, USA, 2012:638-645.
  • 10Wagner F, Klein A, Klopper B, et al. Multi-objective service composition with time- and input-dependent QoS//Proceedings of the 2012 IEEE 19th International Conference on Web Services (ICWS 2012). Honolulu, USA, 2012:234-241.

共引文献37

同被引文献11

引证文献1

二级引证文献16

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部