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SCGJO: A hybrid golden jackal optimization with a sine cosine algorithm for tackling multilevel thresholding image segmentation

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Abstract

Multilevel thresholding is a fundamental, substantial and constructive technique that has been widely recognized and concerned in recent years. However, the computational complexity rises as the threshold level raises. The golden jackal optimization (GJO) imitates discovering prey, tracking and encircling prey, and trapping prey by employing a collaborative foraging mechanism. To eliminate the GJO’s drawbacks, such as premature convergence, inferior computation accuracy and sluggish convergence rate, this paper proposes a hybrid golden jackal optimization with a sine cosine algorithm (SCGJO) based on Kapur’s entropy to tackle the multilevel thresholding image segmentation, the intention is to actualize the accurate threshold values and the maximal fitness values. The SCGJO not only has fantastic adaptability and reliability to promote the complementary benefits and boost the convergence accuracy but also integrates exploration and exploitation to mitigate search stagnation and arrive at the ideal value. The experimental results demonstrate that the SCGJO is superior to the other algorithms and has a quicker convergence rate, higher computation accuracy, greater segmentation quality and stronger stability. In addition, the SCGJO is a steady and trustworthy approach for tackling image segmentation.

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Acknowledgments

This work was partially funded by the Start-up Fee for Scientific Research of High-level Talents in 2022 under Grant No. 00701092336, and partly supported by the University Synergy Innovation Program of Anhui Province under Grant No. GXXT-2021-026, Smart Agriculture and Forestry and Smart Equipment Scientific Research and Innovation Team (Anhui Undergrowth Crop Intelligent Equipment Engineering Research Center) under Grant No. 2022AH010091, Scientific Research Project of University in Anhui Province under Grant Nos. 2022AH040241 and 2022AH051674.

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  1. School of Electrical and Optoelectronic Engineering, West Anhui University, Lu’an, 237012, China

    Jinzhong Zhang, Gang Zhang, Min Kong & Tan Zhang

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  1. Jinzhong Zhang
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  2. Gang Zhang
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Correspondence to Gang Zhang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Jinzhong Zhang: Conceptualization, Methodology, Software, Data curation, Formal analysis, Writing – original draft, Funding acquisition. Gang Zhang: Conceptualization, Methodology, Resources, Project administration, Funding acquisition. Min Kong: Conceptualization, Methodology, Writing – review & editing, Investigation. Tan Zhang: Validation, Writing – review & editing.

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Zhang, J., Zhang, G., Kong, M. et al. SCGJO: A hybrid golden jackal optimization with a sine cosine algorithm for tackling multilevel thresholding image segmentation. Multimed Tools Appl 83, 7681–7719 (2024). https://doi.org/10.1007/s11042-023-15812-0

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