An adaptive memetic algorithm for the architecture optimisation problem

Nasser R. Sabar, Aldeida Aleti

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    6 Citations (Scopus)

    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 compo- nent deployment problem to evaluates the effectiveness of the proposed algorithm with and without the adaptive local search algorithm.

    Original languageEnglish
    Title of host publicationArtificial Life and Computational Intelligence
    Subtitle of host publicationThird Australasian Conference, ACALCI 2017, Geelong, VIC, Australia, January 31 - February 2, 2017, Proceedings
    EditorsMarkus Wagner, Xiaodong Li, Tim Hendtlass
    Place of PublicationCham, Switzerland
    PublisherSpringer
    Pages254-265
    Number of pages12
    ISBN (Electronic)9783319516912
    ISBN (Print)9783319516905
    DOIs
    Publication statusPublished - 2017
    EventAustralasian Conference on Artificial Life and Computational Intelligence (ACALCI) 2017 - Deakin University, Waterfront Campus, Geelong, Australia
    Duration: 31 Jan 20172 Feb 2017
    Conference number: 3rd
    http://www.acalci.net/2017/
    https://link.springer.com/book/10.1007/978-3-319-51691-2 (Springer Proceedings)

    Publication series

    NameLecture Notes in Artificial Intelligence
    PublisherSpringer
    Volume10142
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    ConferenceAustralasian Conference on Artificial Life and Computational Intelligence (ACALCI) 2017
    Abbreviated titleACALCI 2017
    CountryAustralia
    CityGeelong
    Period31/01/172/02/17
    OtherACACLI 2017 is co-located with the Australasian Computer Science Week (ACSW 2017), which will be held at Deakin University's Waterfront Campus, Geelong, which is about 70 kilometers west of Mebourne.

    3rd Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017
    Internet address

    Keywords

    • Adaptive memetic algorithm
    • Architecture optimisation
    • Component deployment

    Cite this

    Sabar, N. R., & Aleti, A. (2017). An adaptive memetic algorithm for the architecture optimisation problem. In M. Wagner, X. Li, & T. Hendtlass (Eds.), Artificial Life and Computational Intelligence: Third Australasian Conference, ACALCI 2017, Geelong, VIC, Australia, January 31 - February 2, 2017, Proceedings (pp. 254-265). (Lecture Notes in Artificial Intelligence; Vol. 10142). Springer. https://doi.org/10.1007/978-3-319-51691-2_22