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On the effects of seeding strategies: A case for search-based multi-objective service composition

conference contribution
posted on 19.09.2019, 14:05 by Tao Chen, Miqing Li, Xin Yao
Service composition aims to search a composition plan of candidate services that produces the optimal results with respect to multiple and possibly conflicting Quality-of-Service (QoS) attributes, e.g., latency, throughput and cost. This leads to a multi-objective optimization problem for which evolutionary algorithm is a promising solution. In this paper, we investigate different ways of injecting knowledge about the problem into the Multi-Objective Evolutionary Algorithm (MOEA) by seeding. Specifically, we propose four alternative seeding strategies to strengthen the quality of the initial population for the MOEA to start working with. By using the real-world WS-DREAM dataset, we conduced experimental evaluations based on 9 different workflows of service composition problems and several metrics. The results confirm the effectiveness and efficiency of those seeding strategies. We also observed that, unlike the discoveries for other problem domains, the implication of the number of seeds on the service composition problems is minimal, for which we investigated and discussed the possible reasons.

Funding

DAASE Programme Grant from the EPSRC (Grant No. EP/J017515/1)

History

School

  • Science

Department

  • Computer Science

Published in

Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18)

Pages

1419 - 1426

Source

Genetic and Evolutionary Computation Conference

Publisher

Association for Computing Machinery (ACM)

Version

VoR (Version of Record)

Rights holder

© owner/author(s)

Publication date

2018-07-02

Copyright date

2018

ISBN

9781450356183

Language

en

Editor(s)

Hernan Aguirre

Location

Kyoto, Japan

Event dates

15th July 2018 - 19th July 2018

Depositor

Dr Tao Chen

Exports