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  • Diversified late acceptance search

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    Namazi167637.pdf (464.9Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Namazi, M
    Sanderson, C
    Newton, MAH
    Polash, MMA
    Sattar, A
    Griffith University Author(s)
    Sattar, Abdul
    Year published
    2018
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    Abstract
    The well-known Late Acceptance Hill Climbing (LAHC) search aims to overcome the main downside of traditional Hill Climbing (HC) search, which is often quickly trapped in a local optimum due to strictly accepting only non-worsening moves within each iteration. In contrast, LAHC also accepts worsening moves, by keeping a circular array of fitness values of previously visited solutions and comparing the fitness values of candidate solutions against the least recent element in the array. While this straightforward strategy has proven effective, there are nevertheless situations where LAHC can unfortunately behave in a similar ...
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    The well-known Late Acceptance Hill Climbing (LAHC) search aims to overcome the main downside of traditional Hill Climbing (HC) search, which is often quickly trapped in a local optimum due to strictly accepting only non-worsening moves within each iteration. In contrast, LAHC also accepts worsening moves, by keeping a circular array of fitness values of previously visited solutions and comparing the fitness values of candidate solutions against the least recent element in the array. While this straightforward strategy has proven effective, there are nevertheless situations where LAHC can unfortunately behave in a similar manner to HC. For example, when a new local optimum is found, often the same fitness value is stored many times in the array. To address this shortcoming, we propose new acceptance and replacement strategies to take into account worsening, improving, and sideways movement scenarios with the aim to improve the diversity of values in the array. Compared to LAHC, the proposed Diversified Late Acceptance Search approach is shown to lead to better quality solutions that are obtained with a lower number of iterations on benchmark Travelling Salesman Problems and Quadratic Assignment Problems.
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    Conference Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume
    11320 LNAI
    DOI
    https://doi.org/10.1007/978-3-030-03991-2_29
    Copyright Statement
    © 2018 Springer International Publishing AG. This is an electronic version of an article published in Lecture Notes In Computer Science (LNCS), volume 11320, AI 2018: AI 2018: Advances in Artificial Intelligence pp 299-311. Lecture Notes In Computer Science (LNCS) is available online at: http://link.springer.com// with the open URL of your article.
    Subject
    Other information and computing sciences not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/383321
    Collection
    • Conference outputs

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