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A multi-objective particle swarm optimization for the submission decision process

Author

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  • Aderemi Oluyinka Adewumi

    (University of KwaZulu-Natal)

  • Peter Ayokunle Popoola

    (University of KwaZulu-Natal)

Abstract

The recently introduced Submission Decision Process problem entails deciding, out of N-1! possible journal submission schedules, which one will, if followed, give an author the maximum expected number of citations while minimizing the expected number of submissions required on one hand, or the expected time spent in review on the other hand. The unnecessarily high computational burden in the existing algorithm used for addressing this problem was observed, and propose a new discrete Multi-Objective Particle Swarm Optimization algorithm which cuts down computational time by a huge factor is proposed. An improvement in the computation of the various objectives is also suggested which further reduces computational burden, and the problem is extended beyond the usual bi-objective optimization to a 3-objective optimization which is solved with the proposed algorithm.

Suggested Citation

  • Aderemi Oluyinka Adewumi & Peter Ayokunle Popoola, 2018. "A multi-objective particle swarm optimization for the submission decision 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 98-110, February.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:1:d:10.1007_s13198-016-0487-2
    DOI: 10.1007/s13198-016-0487-2
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    References listed on IDEAS

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    1. Santiago Salinas & Stephan B Munch, 2015. "Where Should I Send It? Optimizing the Submission Decision Process," PLOS ONE, Public Library of Science, vol. 10(1), pages 1-11, January.
    2. Stegehuis, Clara & Litvak, Nelly & Waltman, Ludo, 2015. "Predicting the long-term citation impact of recent publications," Journal of Informetrics, Elsevier, vol. 9(3), pages 642-657.
    3. David I Stern, 2014. "High-Ranked Social Science Journal Articles Can Be Identified from Early Citation Information," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-11, November.
    4. Johan Bollen & Herbert Van de Sompel & Aric Hagberg & Ryan Chute, 2009. "A Principal Component Analysis of 39 Scientific Impact Measures," PLOS ONE, Public Library of Science, vol. 4(6), pages 1-11, June.
    5. Éric Archambault & Vincent Larivière, 2009. "History of the journal impact factor: Contingencies and consequences," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(3), pages 635-649, June.
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    Cited by:

    1. Zeynep Didem Unutmaz Durmuşoğlu & Alptekin Durmuşoğlu, 2021. "A TOPSIS model for understanding the authors choice of journal selection," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 521-543, January.

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