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    A genetic algorithm for unconstrained multi-objective optimization

    Access Status
    Fulltext not available
    Authors
    Long, Q.
    Wu, Changzhi
    Huang, T.
    Wang, Xiangyu
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Long, Q. and Wu, C. and Huang, T. and Wang, X. 2015. A genetic algorithm for unconstrained multi-objective optimization. Swarm and Evolutionary Computation. 22: pp. 1-14.
    Source Title
    Swarm and Evolutionary Computation
    DOI
    10.1016/j.swevo.2015.01.002
    ISSN
    2210-6502
    School
    Department of Construction Management
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/LP140100873
    URI
    http://hdl.handle.net/20.500.11937/4334
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-objective genetic algorithm (MOGA) is a direct method for multi-objective optimization problems. Compared to the traditional multi-objective optimization method whose aim is to find a single Pareto solution, MOGA tends to find a representation of the whole Pareto frontier. During the process of solving multi-objective optimization problems using genetic algorithm, one needs to synthetically consider the fitness, diversity and elitism of solutions. In this paper, more specifically, the optimal sequence method is altered to evaluate the fitness; cell-based density and Pareto-based ranking are combined to achieve diversity; and the elitism of solutions is maintained by greedy selection. To compare the proposed method with others, a numerical performance evaluation system is developed. We test the proposed method by some well known multi-objective benchmarks and compare its results with other MOGASs; the result show that the proposed method is robust and efficient.

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