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

Zhang, Jinzhong; Zhang, Gang; Kong, Min; Zhang, Tan

doi:10.1007/s11042-023-15812-0

Cited by 10 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All algorithms were run on the experimental platform of Windows 11, 16GB memory, and a 64-bit system, and experiments were conducted using Matlab2022a. This paper selects the whale optimization algorithm (WOA) [45], ant colony optimization algorithm (ACO) [46], ant lion optimization algorithm (ALO) [47], gray wolf optimization algorithm (GWO) [48], golden jackal optimization algorithm (GJO), and the sine-cosine golden jackal optimization algorithm (SCGJO) [39] for comparison.…”

Section: Methodsmentioning

confidence: 99%

“…Tent mapping, also known as the tent map, is a special form of mapping in the field of mathematics, with the characteristic of piecewise linearity. Its functional image is named after its unique tent shape and has a wide range of application values in multiple fields [39]. The core feature of this mapping lies in its piecewise linear property, which allows it to perform mapping operations according to different slopes in different numerical intervals, thus exhibiting a rich and diverse dynamic behavior.…”

Section: Sine-cosine and Cauchy Mutation Of Golden Jackal Optimizatio...mentioning

confidence: 99%

See 1 more Smart Citation

A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

Jiang,

Yue,

Chen

et al. 2024

Biomimetics

View full text Add to dashboard Cite

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine–cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm’s ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Sine-cosine and Cauchy Mutation Of Golden Jackal Optimizatio...mentioning

confidence: 99%

A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

Jiang,

Yue,

Chen

et al. 2024

Biomimetics

View full text Add to dashboard Cite

show abstract

An image segmentation fusion algorithm based on density peak clustering and Markov random field

Feng,

Liu,

Zhang

et al. 2024

Multimed Tools Appl

View full text Add to dashboard Cite

A Review of Enhancing Sine Cosine Algorithm: Common Approaches for Improved Metaheuristic Algorithms

Hamad,

Saleh,

Suandi

et al. 2024

Arch Computat Methods Eng

View full text Add to dashboard Cite

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

Cited by 10 publications

References 44 publications

A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

An image segmentation fusion algorithm based on density peak clustering and Markov random field

A Review of Enhancing Sine Cosine Algorithm: Common Approaches for Improved Metaheuristic Algorithms

Contact Info

Product

Resources

About