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An Adaptive Memetic Algorithm for the Architecture Optimisation Problem

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Artificial Life and Computational Intelligence (ACALCI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10142))

Abstract

Architecture design is one of the most important steps in software development, since design decisions affect the quality of the final system (e.g. reliability and performance). Due to the ever-growing complexity and size of software systems, deciding on the best design is a computationally intensive and complex task. This issue has been tackled by using optimisation method, such as local search and genetic algorithms. Genetic algorithms work well in rugged fitness landscapes, whereas local search methods are successful when the search space is smooth. The strengths of these two algorithms have been combined to create memetic algorithms, which have shown to be more efficient than genetic algorithms and local search on their own. A major point of concern with memetic algorithms is the likelihood of loosing the exploration capacity because of the ‘exploitative’ nature of local search. To address this issue, this work uses an adaptive scheme to control the local search application. The utilised scheme takes into account the diversity of the current population. Based on the diversity indicator, it decides whether to call local search or not. Experiments were conducted on the component deployment problem to evaluates the effectiveness of the proposed algorithm with and without the adaptive local search algorithm.

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Acknowledgements

This research was supported under Australian Research Council’s Discovery Projects funding scheme, project number DE 140100017.

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Authors and Affiliations

  1. Queensland University of Technology, Brisbane, Australia

    Nasser R. Sabar

  2. Faculty of Information Technology, Monash University, Melbourne, Australia

    Aldeida Aleti

Authors
  1. Nasser R. Sabar
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  2. Aldeida Aleti
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Corresponding author

Correspondence to Aldeida Aleti .

Editor information

Editors and Affiliations

  1. The University of Adelaide, Adelaide, South Australia, Australia

    Markus Wagner

  2. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  3. Swinburne University, Melbourne, Victoria, Australia

    Tim Hendtlass

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Sabar, N.R., Aleti, A. (2017). An Adaptive Memetic Algorithm for the Architecture Optimisation Problem. In: Wagner, M., Li, X., Hendtlass, T. (eds) Artificial Life and Computational Intelligence. ACALCI 2017. Lecture Notes in Computer Science(), vol 10142. Springer, Cham. https://doi.org/10.1007/978-3-319-51691-2_22

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  • DOI: https://doi.org/10.1007/978-3-319-51691-2_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51690-5

  • Online ISBN: 978-3-319-51691-2

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