| 1. |
- Das, Arunita, et al.
(författare)
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Particle Swarm Optimizer Variants for Multi-level Thresholding: Theory, Performance Enhancement and Evaluation
- 2024
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Ingår i: Archives of Computational Methods in Engineering. - : SPRINGER. - 1134-3060 .- 1886-1784.
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Tidskriftsartikel (refereegranskat)abstract
- Multilevel thresholding (MLT) has a significant impact in the realm of image segmentation. It is a simple and efficient method for image segmentation. However, as the number of thresholds increases, the computational complexity of MLT increases exponentially, and proper thresholding crucially depends on the utilized objective function. Swarm intelligence (SI) with the optimal objective function is used to enhance the efficacy of MLT image segmentation. Among the various SI techniques, Particle Swarm Optimization (PSO) and its variants are utilized extensively in literature to improve the performance of MLT. As a result, the objective of this study is to provide an updated survey on this topic. This study provides a comprehensive report of the classical PSO, and its improved variants, and their applications in MLT domains. However, like other SI techniques, PSO and its widely utilized variant called Darwinian PSO (DPSO) have the drawbacks of imbalanced exploration and exploitation and premature convergence. Therefore, along with the updated survey report, this study develops an efficient variant of the DPSO called Cauchy DPSO with Opposition Learning (CaDPSOOL). The Cauchy distribution and opposition learning (OL) have been incorporated to enhance the optimization capability by preventing premature convergence and maintaining the good balance between exploration and exploitation. Furthermore, a new hybrid objective function has also been designed by considering Otsu and Tsallis entropy in MLT domain for better segmentation results. The proposed CaDPSOOL with hybrid objective function has been employed for standard color image and hematopathology image segmentation domains. The results of this study show that the proposed model produces better results than other state-of-the-art MLT models in terms of segmentation quality metrics.
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| 2. |
- Sasmal, Buddhadev, et al.
(författare)
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A Comprehensive Survey on Aquila Optimizer
- 2023
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Ingår i: Archives of Computational Methods in Engineering. - : SPRINGER. - 1134-3060 .- 1886-1784. ; 30, s. 4449-4476
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Tidskriftsartikel (refereegranskat)abstract
- Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.
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| 3. |
- Sasmal, Buddhadev, et al.
(författare)
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Reptile Search Algorithm: Theory, Variants, Applications, and Performance Evaluation
- 2023
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Ingår i: Archives of Computational Methods in Engineering. - : SPRINGER. - 1134-3060 .- 1886-1784.
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Tidskriftsartikel (refereegranskat)abstract
- Reptile Search Algorithm (RSA) is a recently developed nature-inspired meta-heuristics optimization algorithm inspired by the encircling mechanism, hunting mechanism and social behaviours of crocodiles in nature. Since Abualigah et al. introduced RSA in 2022, it has garnered significant interest from researchers and been widely employed to address various optimization challenges across a variety of fields. This is because it has an adequate execution time, an efficient convergence rate, and is more effective than other well-known optimization algorithms. As a result, the objective of this study is to provide an updated survey on the topic. This study provides a comprehensive report of the classical RSA, and its improved variants and their applications in various domains. To adequately analyse RSA, a comprehensive comparison among RSA and its peer NIOAs is performed using mathematical benchmark functions.
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