Solving 0 –1 knapsack problem by an improved binary monarch butterfly algorithm

  • Ghalya Basheer University of Mosul
  • Lamyaa Mohammed University of Mosul
  • Zakariya Algamal University of Mosul
DOI: https://doi.org/10.19139/soic-2310-5070-2965
Keywords: 0-1 knapsack problem, monarch butterfly optimization algorithm, transfer function, time-varying parameter

Abstract

The binary monarch butterfly optimization algorithm (BMBOA) is a meta-heuristic algorithm that has been applied widely in combinational optimization problems. Binary knapsack problem has received considerable attention in the combinational optimization. In this paper, a new time-varying transfer function is proposed to improve the exploration and exploitation capability of the BMBOA with the best solution and short computing time. Based on small, medium, and high-dimensional sizes of the knapsack problem, the computational results reveal that the proposed time-varying transfer functions obtain the best results not only by finding the best possible solutions but also by yielding short computational times. Compared to the standard transfer functions, the efficiency of the proposed time-varying transfer functions is superior, especially in the high-dimensional sizes.
Published
2025-11-19
How to Cite
Basheer, G., Mohammed, L., & Algamal, Z. (2025). Solving 0 –1 knapsack problem by an improved binary monarch butterfly algorithm. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2965
Issue
Online First
Section
Research Articles
Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).