IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v29y2018i1d10.1007_s10845-015-1087-8.html
   My bibliography  Save this article

Multi-objective modified differential evolution algorithm with archive-base mutation for solving multi-objective $$p$$ p -xylene oxidation process

Author

Listed:
  • Qinqin Fan

    (East China University of Science and Technology)

  • Xuefeng Yan

    (East China University of Science and Technology)

Abstract

Maximizing the diversity of the obtained objective vectors and increasing the convergence speed to the true Pareto front are two important issues in the design of multi-objective evolutionary algorithms (MOEAs). To solve complex multi-objective optimization problems (MOPs), a multi-objective modified differential evolution algorithm with archive-base mutation (MOMDE-AM) is proposed. In MOMDE-AM, with the purpose of reducing the loss of population evolution information, a modified mutation strategy with archive is introduced, which could utilize several useful inferior solutions and provide promising direction information toward the true Pareto front. The performance of MOMDE-AM is compared with five other MOEAs on five bi-objective and five tri-objective optimization problems. The simulation and statistical analysis results indicate that the overall performance of MOMDE-AM is better than those of the compared algorithms on these test functions. Finally, MOMDE-AM is used to optimize ten operation conditions of the $$p$$ p -xylene oxidation reaction process; the results show that MOMDE-AM is an effective and efficient optimization tool for solving actual MOPs.

Suggested Citation

  • Qinqin Fan & Xuefeng Yan, 2018. "Multi-objective modified differential evolution algorithm with archive-base mutation for solving multi-objective $$p$$ p -xylene oxidation process," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 35-49, January.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:1:d:10.1007_s10845-015-1087-8
    DOI: 10.1007/s10845-015-1087-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-015-1087-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-015-1087-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ali, Musrrat. & Siarry, Patrick & Pant, Millie., 2012. "An efficient Differential Evolution based algorithm for solving multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 217(2), pages 404-416.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alejandro Alvarado-Iniesta & Luis Gonzalo Guillen-Anaya & Luis Alberto Rodríguez-Picón & Raul Ñeco-Caberta, 2020. "Multi-objective optimization of an engine mount design by means of memetic genetic programming and a local exploration approach," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 19-32, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lunardi, Willian T. & Birgin, Ernesto G. & Ronconi, Débora P. & Voos, Holger, 2021. "Metaheuristics for the online printing shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 293(2), pages 419-441.
    2. Mahalec, Vladimir & Chen, Yingwu & Liu, Xiaolu & He, Renjie & Sun, Kai, 2015. "Reconfiguration of satellite orbit for cooperative observation using variable-size multi-objective differential evolutionAuthor-Name: Chen, Yingguo," European Journal of Operational Research, Elsevier, vol. 242(1), pages 10-20.
    3. Yu, Yang & Tang, Jiafu & Gong, Jun & Yin, Yong & Kaku, Ikou, 2014. "Mathematical analysis and solutions for multi-objective line-cell conversion problem," European Journal of Operational Research, Elsevier, vol. 236(2), pages 774-786.
    4. Liu, Ruochen & Li, Jianxia & fan, Jing & Mu, Caihong & Jiao, Licheng, 2017. "A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1028-1051.
    5. Zhalechian, M. & Torabi, S. Ali & Mohammadi, M., 2018. "Hub-and-spoke network design under operational and disruption risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 20-43.
    6. Wei Wang & Jingjie Chen & Qi Liu & Zhaoxia Guo, 2018. "Green Project Planning with Realistic Multi-Objective Consideration in Developing Sustainable Port," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    7. Om Prakash Verma & Toufiq Haji Mohammed & Shubham Mangal & Gaurav Manik, 2018. "Optimization of steam economy and consumption of heptad’s effect evaporator system in Kraft recovery process," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 111-130, February.
    8. Fan, Qinqin & Yan, Xuefeng & Zhang, Yilian, 2018. "Auto-selection mechanism of differential evolution algorithm variants and its application," European Journal of Operational Research, Elsevier, vol. 270(2), pages 636-653.
    9. Muhsen, Dhiaa Halboot & Ghazali, Abu Bakar & Khatib, Tamer & Abed, Issa Ahmed & Natsheh, Emad M., 2016. "Sizing of a standalone photovoltaic water pumping system using a multi-objective evolutionary algorithm," Energy, Elsevier, vol. 109(C), pages 961-973.
    10. Wang, Dujuan & Yin, Yunqiang & Cheng, T.C.E., 2018. "Parallel-machine rescheduling with job unavailability and rejection," Omega, Elsevier, vol. 81(C), pages 246-260.
    11. Ma, Xuemin & Yang, Jingming & Sun, Hao & Hu, Ziyu & Wei, Lixin, 2021. "Feature information prediction algorithm for dynamic multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 295(3), pages 965-981.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:29:y:2018:i:1:d:10.1007_s10845-015-1087-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.