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International Conference on Advanced Data Mining and Applications

ADMA 2010: Advanced Data Mining and Applications pp 270–281Cite as

Applying Multi-objective Evolutionary Algorithms to QoS-Aware Web Service Composition

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6441))

Abstract

Finding optimal solutions for QoS-aware Web service composition with conflicting objectives and various restrictions on quality matrices is a NP-hard problem. This paper proposes the use of multi-objective evolutionary algorithms (MOEAs for short) for QoS-aware service composition optimisation. More specifically, SPEA2 is introduced to achieve the goal. The algorithm is good at dealing with multi-objective combinational optimisation problems. Experimental results reveal that SPEA2 is able to approach the Pareto-optimal front with well spread distribution. The Pareto front approximations provide different trade-offs, from which the end-users may select the better one based on their preference.

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Author information

Authors and Affiliations

  1. Southwest University, Chongqing, P.R. China

    Li Li, Peng Cheng, Ling Ou & Zili Zhang

Authors
  1. Li Li
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  2. Peng Cheng
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  3. Ling Ou
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  4. Zili Zhang
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Editor information

Editors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, 2007, Sydney, NSW, Australia

    Longbing Cao

  2. College of Computer Science, Chongqing University, 400030, Chongqing, China

    Jiang Zhong  & Yong Feng  & 

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© 2010 Springer-Verlag Berlin Heidelberg

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Li, L., Cheng, P., Ou, L., Zhang, Z. (2010). Applying Multi-objective Evolutionary Algorithms to QoS-Aware Web Service Composition. In: Cao, L., Zhong, J., Feng, Y. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17313-4_27

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  • DOI: https://doi.org/10.1007/978-3-642-17313-4_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17312-7

  • Online ISBN: 978-3-642-17313-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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