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    Binary artificial algae algorithm for multidimensional knapsack problems

    241564_241564.pdf (1.536Mb)
    Access Status
    Open access
    Authors
    Zhang, X.
    Wu, Changzhi
    Li, J.
    Wang, X.
    Yang, Z.
    Lee, J.
    Jung, K.
    Date
    2016
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Zhang, X. and Wu, C. and Li, J. and Wang, X. and Yang, Z. and Lee, J. and Jung, K. 2016. Binary artificial algae algorithm for multidimensional knapsack problems. Applied Soft Computing. 43: pp. 583-595.
    Source Title
    Applied Soft Computing
    DOI
    10.1016/j.asoc.2016.02.027
    ISSN
    1568-4946
    School
    Department of Construction Management
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/LP130100451
    URI
    http://hdl.handle.net/20.500.11937/32127
    Collection
    • Curtin Research Publications
    Abstract

    The multidimensional knapsack problem (MKP) is a well-known NP-hard optimization problem. Various meta-heuristic methods are dedicated to solve this problem in literature. Recently a new meta-heuristic algorithm, called artificial algae algorithm (AAA), was presented, which has been successfully applied to solve various continuous optimization problems. However, due to its continuous nature, AAA cannot settle the discrete problem straightforwardly such as MKP. In view of this, this paper proposes a binary artificial algae algorithm (BAAA) to efficiently solve MKP. This algorithm is composed of discrete process, repair operators and elite local search. In discrete process, two logistic functions with different coefficients of curve are studied to achieve good discrete process results. Repair operators are performed to make the solution feasible and increase the efficiency. Finally, elite local search is introduced to improve the quality of solutions. To demonstrate the efficiency of our proposed algorithm, simulations and evaluations are carried out with total of 94 benchmark problems and compared with other bio-inspired state-of-the-art algorithms in the recent years including MBPSO, BPSOTVAC, CBPSOTVAC, GADS, bAFSA, and IbAFSA. The results show the superiority of BAAA to many compared existing algorithms.

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