A novel hybrid PSO based on levy flight and wavelet mutation for global optimization

Gao, Yaohui; Zhang, Hao; Duan, Yingying; Zhang, Huaifeng

doi:10.1371/journal.pone.0279572

Cited by 9 publications

(3 citation statements)

References 49 publications

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“…The Levy flight strategy is often used in swarm intelligence algorithms based on birds or swimming fungi [44,45] to simulate the gliding behavior of organisms. Levy flight has the characteristics of alternating long and short lengths, strong randomness, and the ability to jump out of local optima, which is relatively consistent with heuristic algorithms.…”

Section: Strategy Condition Formulamentioning

confidence: 99%

A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images

Zhang,

Liu,

2024

Symmetry

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This paper presents an improved swarming algorithm that enhances low-illumination images. The algorithm combines a hybrid Harris Eagle algorithm with double gamma (IHHO-BIGA) and incomplete beta (IHHO-NBeta) functions. This paper integrates the concept of symmetry into the improvement steps of the image adaptive enhancement algorithm. The enhanced algorithm integrates chaotic mapping for population initialization, a nonlinear formula for prey energy calculation, spiral motion from the black widow algorithm for global search enhancement, a nonlinear inertia weight factor inspired by particle swarm optimization, and a modified Levy flight strategy to prevent premature convergence to local optima. This paper compares the algorithm’s performance with other swarm intelligence algorithms using commonly used test functions. The algorithm’s performance is compared against several emerging swarm intelligence algorithms using commonly used test functions, with results demonstrating its superior performance. The improved Harris Eagle algorithm is then applied for image adaptive enhancement, and its effectiveness is evaluated on five low-illumination images from the LOL dataset. The proposed method is compared to three common image enhancement techniques and the IHHO-BIGA and IHHO-NBeta methods. The experimental results reveal that the proposed approach achieves optimal visual perception and enhanced image evaluation metrics, outperforming the existing techniques. Notably, the standard deviation data of the first image show that the IHHO-NBeta method enhances the image by 8.26%, 120.91%, 126.85%, and 164.02% compared with IHHO-BIGA, the single-scale Retinex enhancement method, the homomorphic filtering method, and the limited contrast adaptive histogram equalization method, respectively. The processing time of the improved method is also better than the previous heuristic algorithm.

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Section: Strategy Condition Formulamentioning

confidence: 99%

A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images

Zhang,

Liu,

2024

Symmetry

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“…This restricts the scope of the search space and could produce less-than-ideal results. However, the algorithm can search outside the immediate search space of initial solutions and potentially find better solutions by using Levy flight to initialize the solution, which generates huge leaps in the search space [17,18,19]. Furthermore, Levy Flight keeps the algorithm from prematurely converging to poor solutions by avoiding local optima.…”

Section: Stepmentioning

confidence: 99%

Improved Driving Training-Based Optimization Algorithm Using Levy Flight and Crowding Distance Techniques

Kwegyir,

Terkper,

Effah

et al. 2024

Res. Rep. Comput. Sci.

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This study presents an improved version of the Driving Training-Based Optimization (DTBO) algorithm, the Improved Driving Training-Based Optimization (IDTBO). The work addresses fundamental issues in selecting drivers and learners for the conventional DTBO, which substantially impact the algorithm's accuracy and convergence speed. Two significant improvements are proposed: including the crowding distance technique for more diverse driver and learner selection and incorporating the Levy Flight distribution for better exploration and local optima avoidance. The IDTBO's performance is evaluated using twelve benchmark functions, including unimodal and high-dimensional multimodal optimization functions. The results indicate that IDTBO performs exceptionally well, with more extraordinary exploitation ability on unimodal functions and consistent achievement of the global optima. The proposed IDTBO demonstrated exceptional exploration capabilities on high-dimensional multimodal functions and performed competitively with other algorithms in the literature. From six functions, the IDTBO obtained zero optimal values. Again, the rate of convergence analysis demonstrates that IDTBO finds optimal solutions in fewer iterations, demonstrating its capacity to balance exploration and exploitation. To assess the strength of the IDTBO in solving real-world problems, the improved DTBO is further tested on two practical benchmark engineering problems. The IDTBO again produced a competitive performance against other algorithms in the literature. The study shows that IDTBO is a valuable metaheuristic algorithm that can tackle various real-world optimization problems.

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“…The nature-inspired WOA is widely applied for addressing optimization problems. The concept is derived from the social behaviors shown by humpback whales, particularly their bubble-net hunting strategy [47]. The primary purpose of WOA is to identify the most favorable solution or collection of alternatives that either decrease or maximize a specified objective function.…”

Section: Feature Selectionmentioning

confidence: 99%

Artificial Intelligence-Driven Eye Disease Classification Model

Wahab Sait

2023

Applied Sciences

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Eye diseases can result in various challenges and visual impairments. These diseases can affect an individual’s quality of life and general health and well-being. The symptoms of eye diseases vary widely depending on the nature and severity of the disease. Early diagnosis can protect individuals from visual impairment. Artificial intelligence (AI)-based eye disease classification (EDC) assists physicians in providing effective patient services. However, the complexities of the fundus image affect the classifier’s performance. There is a demand for a practical EDC for identifying eye diseases in the earlier stages. Thus, the author intends to build an EDC model using the deep learning (DL) technique. Denoising autoencoders are used to remove the noises and artifacts from the fundus images. The single-shot detection (SSD) approach generates the key features. The whale optimization algorithm (WOA) with Levy Flight and Wavelet search strategy is followed for selecting the features. In addition, the Adam optimizer (AO) is applied to fine-tune the ShuffleNet V2 model to classify the fundus images. Two benchmark datasets, ocular disease intelligent recognition (ODIR) and EDC datasets, are utilized for performance evaluation. The proposed EDC model achieved accuracy and Kappa values of 99.1 and 96.4, and 99.4 and 96.5, in the ODIR and EDC datasets, respectively. It outperformed the recent EDC models. The findings highlight the significance of the proposed EDC model in classifying eye diseases using complex fundus images. Healthcare centers can implement the proposed model to improve their standards and serve a more significant number of patients. In the future, the proposed model can be extended to identify a comprehensive range of eye diseases.

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A novel hybrid PSO based on levy flight and wavelet mutation for global optimization

Cited by 9 publications

References 49 publications

A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images

A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images

Improved Driving Training-Based Optimization Algorithm Using Levy Flight and Crowding Distance Techniques

Artificial Intelligence-Driven Eye Disease Classification Model

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