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Fuzzified Hybrid Metaheuristics for QoS-Aware Service Composition

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Data Science and Emerging Technologies (DaSET 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 191))

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Abstract

Service composition has been a centric challenge in various distributed computational paradigms arising from the necessity of composing services to deliver more complicated computing tasks. In many cases, service composition was the process of selecting the optimal set of services from a repository of available services, which led to a multi-objective combinatorial optimization problem falling in the category of NP-hard. As a result, seeking optimal solutions in a minimum time budget has been a continuous quest in the research agenda. Metaheuristics and hybrid metaheuristics have been the primary avenues to address this problem. Nonetheless, metaheuristics suffer from randomicity, slow or premature convergence and stochastic behaviour. A plethora of hybridization methods leveraging operator modification is evident in existing literature. This paper proposes a fuzzy hybrid metaheuristics using fuzzy linguistics search operator to accelerate convergence and minimize the stochastic behaviours of metaheuristics to achieve a stable search mechanism.

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Acknowledgements

The main author wants to thank Professor Witold Pedrycz for providing precise comments and the Centre for Research and Consultancy, UNITAR International University.

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Authors and Affiliations

  1. School of Information Technology, UNITAR International University, Petaling Jaya, Malaysia

    Hadi Naghavipour, Hadi Naghavipour, Farhad Nadi & Ali Aitizaz

Authors
  1. Hadi Naghavipour
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  2. Farhad Nadi
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  3. Ali Aitizaz
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Corresponding author

Correspondence to Hadi Naghavipour .

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Editors and Affiliations

  1. UNITAR Graduate School, UNITAR International University, Petaling Jaya, Malaysia

    Yap Bee Wah

  2. Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, UK

    Dhiya Al-Jumeily OBE

  3. University of Tennessee, Knoxville, TN, USA

    Michael W. Berry

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Naghavipour, H., Nadi, F., Aitizaz, A. (2024). Fuzzified Hybrid Metaheuristics for QoS-Aware Service Composition. In: Bee Wah, Y., Al-Jumeily OBE, D., Berry, M.W. (eds) Data Science and Emerging Technologies. DaSET 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 191. Springer, Singapore. https://doi.org/10.1007/978-981-97-0293-0_8

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