Skip to main content
SpringerLink
Log in

Solving service selection problem based on a novel multi-objective artificial bees colony algorithm

  • Published:
Journal of Shanghai Jiaotong University (Science) Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. LIN C, CHEN Y, HUANG J W, et al. A survey on models and solutions of multi-objective optimization for QoS in service computing [J]. Chinese Journal of Computers, 2015, 38(10): 1907–1923 (in Chinese).

    Google Scholar 

  2. SHENG Q Z, QIAO X Q, VASILAKOS A V, et al. Web services composition: A decade’s overview [J]. Information Sciences, 2014, 280(1): 218–238.

    Article  Google Scholar 

  3. ALRIFAI M, RISSE T, NEJDL W. A hybrid approach for efficient web service composition with end-to-end QoS constraints [J]. ACM Transactions on the Web, 2012, 6(2): 7:1-7:31.

    Google Scholar 

  4. TRUMMER I, FALTINGS B, BINDER W. Multiobjective quality-driven service selection — A fully polynomial time approximation scheme [J]. IEEE Transactions on Software Engineering, 2014, 40(2): 167–191.

    Article  Google Scholar 

  5. VINEK E, BERAN P P, SCHIKUTA E. A dynamic multi-objective optimization framework for selecting distributed deployments in a heterogeneous environment [C]//International Conference on Computational Science. Amsterdam, the Netherlands: Elsevier, 2011: 166–175.

    Google Scholar 

  6. ZHANG C S, YIN H, ZHANG B. A novel ant colony optimization algorithm for large scale QoS-based service selection problem [J]. Discrete Dynamics in Nature and Society, 2013, 2013:1–9.

    Google Scholar 

  7. WANG X Z, XU X F, SHENG Q Z, et al. Novel artificial bee colony algorithms for QoS-aware service selection [J]. IEEE Transactions on Services Computing, 2016, PP(99): 1–14.

    Chapter  Google Scholar 

  8. CAO J X, SUN X S, ZHENG X, et al. Efficient multiobjective services selection algorithm based on particle swarm optimization [C]//IEEE Asia-Pacific Services Computing Conference (APSCC). New York: IEEE, 2010: 603–608.

    Google Scholar 

  9. WANG J L, HOU Y B. Optimal web service selection based on multi-objective genetic algorithm [C]//The International Symposium on Computational Intelligence and Design. New York: IEEE, 2008: 553–556.

    Google Scholar 

  10. HUANG L P, ZHANG B, YUAN X, et al. A novel bi-ant colony optimization algorithm for solving multiobjective service selection problem [J]. Journal of Intelligent & Fuzzy Systems, 2016, 31(2): 873–884.

    Article  Google Scholar 

  11. AKBARI R, HEDAYATZADEH R, ZIARATI K, et al. A multi-objective artificial bee colony algorithm [J]. Swarm and Evolutionary Computation, 2012, 2: 39–52.

    Article  Google Scholar 

  12. XIANG Y, ZHOU Y R. A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization [J]. Applied Soft Computing, 2015, 35: 766–785.

    Article  Google Scholar 

  13. KIRAN M S. The continuous artificial bee colony algorithm for binary optimization [J]. Applied Soft Computing, 2015, 33: 15–23.

    Article  Google Scholar 

  14. MA L B, HU K Y, ZHU Y L, et al. Cooperative artificial bee colony algorithm for multi-objective RFID network planning [J]. Journal of Network and Computer Applications, 2014, 42: 143–162.

    Article  Google Scholar 

  15. FAN X Q, FANGX W, JIANG C J. Research on Web service selection based on cooperative evolution [J]. Expert Systems with Applications, 2011, 38(8): 9736–9743.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Software College, Northeastern University, Shenyang, 110819, China

    Liping Huang  (黄利萍)

  2. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Liping Huang  (黄利萍), Bin Zhang  (张 斌), Changsheng Zhang  (张长胜) & Yan Gao  (高 岩)

  3. Institute of Information Science & Engineering, Shenyang Ligong University, Shenyang, 110159, China

    Xun Yuan  (苑 勋)

Authors
  1. Liping Huang  (黄利萍)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Zhang  (张 斌)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xun Yuan  (苑 勋)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Changsheng Zhang  (张长胜)
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yan Gao  (高 岩)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liping Huang  (黄利萍).

Additional information

Foundation item: the National Natural Science Foundation of China (Nos. 61202085, 61300019), the Ningxia Hui Autonomous Region Natural Science Foundation (No. NZ13265), and the Special Fund for Fundamental Research of Central Universities of Northeastern University (Nos. N120804001, N120204003)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, L., Zhang, B., Yuan, X. et al. Solving service selection problem based on a novel multi-objective artificial bees colony algorithm. J. Shanghai Jiaotong Univ. (Sci.) 22, 474–480 (2017). https://doi.org/10.1007/s12204-017-1860-2

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12204-017-1860-2

Key words

CLC number

Search

Navigation