A chimp-inspired remora optimization algorithm for multilevel thresholding image segmentation using cross entropy

Liu, Qingxin; Li, Ni; Jia, Heming; Qi, Qi; Abualigah, Laith

doi:10.1007/s10462-023-10498-0

Cited by 24 publications

(7 citation statements)

References 61 publications

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“…When this occurs, crayfish compete with each other. The representation of this competitive behavior is shown in Equation (6).…”

Section: Heat Avoidance Behaviormentioning

confidence: 99%

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An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems

Wang,

Zhang,

Zou

2024

Biomimetics

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The crayfish optimization algorithm (COA), proposed in 2023, is a metaheuristic optimization algorithm that is based on crayfish’s summer escape behavior, competitive behavior, and foraging behavior. COA has a good optimization performance, but it still suffers from the problems of slow convergence speed and sensitivity to the local optimum. To solve these problems, an improved multi-strategy crayfish optimization algorithm for solving numerical optimization problems, called IMCOA, is proposed to address the shortcomings of the original crayfish optimization algorithm for each behavioral strategy. Aiming at the imbalance between local exploitation and global exploration in the summer heat avoidance and competition phases, this paper proposes a cave candidacy strategy and a fitness–distance balanced competition strategy, respectively, so that these two behaviors can better coordinate the global and local optimization capabilities and escape from falling into the local optimum prematurely. The directly foraging formula is modified during the foraging phase. The food covariance learning strategy is utilized to enhance the population diversity and improve the convergence accuracy and convergence speed. Finally, the introduction of an optimal non-monopoly search strategy to perturb the optimal solution for updates improves the algorithm’s ability to obtain a global best solution. We evaluated the effectiveness of IMCOA using the CEC2017 and CEC2022 test suites and compared it with eight algorithms. Experiments were conducted using different dimensions of CEC2017 and CEC2022 by performing numerical analyses, convergence analyses, stability analyses, Wilcoxon rank–sum tests and Friedman tests. Experiments on the CEC2017 and CEC2022 test suites show that IMCOA can strike a good balance between exploration and exploitation and outperforms the traditional COA and other optimization algorithms in terms of its convergence speed, optimization accuracy, and ability to avoid premature convergence. Statistical analysis shows that there is a significant difference between the performance of the IMCOA algorithm and other algorithms. Additionally, three engineering design optimization problems confirm the practicality of IMCOA and its potential to solve real-world problems.

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“…When this occurs, crayfish compete with each other. The representation of this competitive behavior is shown in Equation (6).…”

Section: Heat Avoidance Behaviormentioning

confidence: 99%

“…X new = X i,j − X z,j + X cave (6) where t denotes the current number of iterations and T denotes the maximum number of iterations. X z,j is a randomly selected agent from the crayfish.…”

Section: Heat Avoidance Behaviormentioning

confidence: 99%

An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems

Wang,

Zhang,

Zou

2024

Biomimetics

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“…Such algorithms do not depend on the specific form of the problem but rather guide the search process by simulating phenomena in nature, behaviors of organisms, physical principles, and social laws, etc., thus demonstrating excellent adaptability and efficiency in numerous application fields. With the continuous development of metaheuristic algorithms, these algorithms play a crucial role in a variety of fields, such as path planning [2,3], image segmentation [4,5], feature selection [6,7], neural network hyperparameter optimization [8,9], task allocation [10,11], supply chain management [12,13], waste collection [14], wireless sensor optimization problems [15,16], and antenna array synthesis issues [17,18]. And, they show great potential in promoting the development of engineering technology, improving productivity, and solving multi-objective optimization problems [19,20].…”

Section: Introductionmentioning

confidence: 99%

Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization

Zhang,

He,

Wang

et al. 2024

Biomimetics

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The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that has been proposed in recent years. The algorithm optimizes the search ability of individuals by mimicking the hunting behavior of sand cat groups in nature, thereby achieving robust optimization performance. It is characterized by few control parameters and simple operation. However, due to the lack of population diversity, SCSO is less efficient in solving complex problems and is prone to fall into local optimization. To address these shortcomings and refine the algorithm’s efficacy, an improved multi-strategy sand cat optimization algorithm (IMSCSO) is proposed in this paper. In IMSCSO, a roulette fitness–distance balancing strategy is used to select codes to replace random agents in the exploration phase and enhance the convergence performance of the algorithm. To bolster population diversity, a novel population perturbation strategy is introduced, aiming to facilitate the algorithm’s escape from local optima. Finally, a best–worst perturbation strategy is developed. The approach not only maintains diversity throughout the optimization process but also enhances the algorithm’s exploitation capabilities. To evaluate the performance of the proposed IMSCSO, we conducted experiments in the CEC 2017 test suite and compared IMSCSO with seven other algorithms. The results show that the IMSCSO proposed in this paper has better optimization performance.

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“…Furthermore, Rather et al [ 67 ] employed a Levy flight and chaos theory-based Gravity Search Algorithm (LCGSA) to optimize computational efficiency in multi-threshold segmentation, overcoming traditional segmentation issues like local minima and premature convergence. Liu et al [ 68 ] innovated with the HCROA, a primate-inspired WOA, combined with the Chimp Optimization Algorithm, to enhance exploration and exploitation balance, thereby improving segmentation accuracy and noise robustness. Finally, [ 69 ] merged Enhanced Fuzzy Elephant Herd Optimization (EFEHO) with the Otsu method, facilitating rapid diagnosis in Alzheimer’s disease and Mild Cognitive Impairment (MCI) contexts.…”

Section: Introductionmentioning

confidence: 99%

Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques

Xie,

Wu,

Tang

et al. 2024

PLoS ONE

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Addressing the profound impact of Tapping Panel Dryness (TPD) on yield and quality in the global rubber industry, this study introduces a cutting-edge Otsu threshold segmentation technique, enhanced by Dung Beetle Optimization (DBO-Otsu). This innovative approach optimizes the segmentation threshold combination by accelerating convergence and diversifying search methodologies. Following initial segmentation, TPD severity levels are meticulously assessed using morphological characteristics, enabling precise determination of optimal thresholds for final segmentation. The efficacy of DBO-Otsu is rigorously evaluated against mainstream benchmarks like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM), and compared with six contemporary swarm intelligence algorithms. The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm’s practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.

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A chimp-inspired remora optimization algorithm for multilevel thresholding image segmentation using cross entropy

Cited by 24 publications

References 61 publications

An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems

An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems

Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization

Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques

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