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    A Multiobjective Genetic Algorithm Based on a Discrete Selection Procedure

    230976_230976.pdf (4.459Mb)
    Access Status
    Open access
    Authors
    Long, Q.
    Wu, Changzhi
    Wang, X.
    Jiang, L.
    Li, J.
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Long, Q. and Wu, C. and Wang, X. and Jiang, L. and Li, J. 2015. A Multiobjective Genetic Algorithm Based on a Discrete Selection Procedure. Mathematical Problems in Engineering. Article ID 349781, 17 pages.
    Source Title
    Mathematical Problems in Engineering
    DOI
    10.1155/2015/349781
    ISSN
    1024-123X
    School
    Department of Construction Management
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/LP140100873
    Remarks

    This open access article is distributed under the Creative Commons license http://creativecommons.org/licenses/by/3.0/

    URI
    http://hdl.handle.net/20.500.11937/23625
    Collection
    • Curtin Research Publications
    Abstract

    © 2015 Qiang Long et al. Multiobjective genetic algorithm (MOGA) is a direct search method for multiobjective optimization problems. It is based on the process of the genetic algorithm; the population-based property of the genetic algorithm is well applied in MOGAs. Comparing with the traditional multiobjective algorithm whose aim is to find a single Pareto solution, the MOGA intends to identify numbers of Pareto solutions. During the process of solving multiobjective optimization problems using genetic algorithm, one needs to consider the elitism and diversity of solutions. But, normally, there are some trade-offs between the elitism and diversity. For some multiobjective problems, elitism and diversity are conflicting with each other. Therefore, solutions obtained by applying MOGAs have to be balanced with respect to elitism and diversity. In this paper, we propose metrics to numerically measure the elitism and diversity of solutions, and the optimum order method is applied to identify these solutions with better elitism and diversity metrics. We test the proposed method by some well-known benchmarks and compare its numerical performance with other MOGAs; the result shows that the proposed method is efficient and robust.

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