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International Conference on Intelligent Computing

ICIC 2018: Intelligent Computing Theories and Application pp 792–805Cite as

Magnetotactic Bacteria Constrained Optimization Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10954))

Abstract

Many problems encountered in the field of science and engineering can be ultimately attributed to constrained optimization problems (COPs). During the past decades, solving COPs with evolutionary algorithms have received considerable attentions among researchers. A novel approach to deal with numerical constrained optimization problems, which incorporates a Magnetotactic Bacteria Optimization Algorithm (MBOA) and an adaptive constraint-handling technique, named COMBOA, is presented in this paper. COMBOA mainly consists of an improved MBOA and archiving-based adaptive tradeoff model (ArATM) used as the constraint-handling technique. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. It is compared with several state-of-the-art algorithms on 13 well-known benchmark functions. The experiment results show that the COMBOA is effective in solving constrained optimization problems. It shows better or competitive performance compared with other state-of-the-art algorithms referred to in this paper in terms of the quality of the solutions.

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Author information

Authors and Affiliations

  1. LiaoCheng University, Liaocheng, Shandong, China

    Lili Liu

Authors
  1. Lili Liu
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Correspondence to Lili Liu .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Phalguni Gupta

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Liu, L. (2018). Magnetotactic Bacteria Constrained Optimization Algorithm. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_80

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  • DOI: https://doi.org/10.1007/978-3-319-95930-6_80

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95929-0

  • Online ISBN: 978-3-319-95930-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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