A leader Harris hawks optimization for 2-D Masi entropy-based multilevel image thresholding

Naik, Manoj Kumar; Panda, Rutuparna; Wunnava, Aneesh; Jena, Bibekananda; Abraham, Ajith

doi:10.1007/s11042-020-10467-7

Cited by 62 publications

(25 citation statements)

References 69 publications

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“…The application of t-entropy in the context of Power k-means clustering and sparse signal recovery is also some possible avenues for future research 15 Eagle Strategy—Whale Optimization Algorithm (ES-WOA) Kapur’s, Fuzzy, Tsallis’, Otsu’s thresholding and Cross entropy Ray et al ( 2021 ) Color Hematology Images: Medical Images Proposed method is compared with ES-DE, ES-FA, ES-PSO and WOA PSNR, FSIM and QILV The proposed ES-WOA (Tsallis) acquires the best threshold values generating quicker execution times for all the images considered to be tested. However, other types of medical images namely histology, MRI, CT, Mammogram could be used with the proposed method 16 Multilevel Thresholding Based on Fuzzy-Masi Entropy Fuzzy-Masi entropy He et al ( 2021 ) Standard Color Images Proposed method is compared with Masi and Tsalli’s entropy PSNR, FSIM and SSIM The results illustrate that fuzzy Masi entropy displays advanced quality and performance than other objective functions considered under the banner 17 Fuzzy Entropy Type II (FE-TII) combined with Marine Predators Algorithm (MPA) (FE-TII-MPA) Type-II Fuzzy Entropy Mahajan et al ( 2021 ) Standard Gray Scale Images Proposed method is compared with PFA, PPA, DE, PSO and HPFPPA-D PSNR, SSIM and MSE Results achieved by FE-TII-MPA are comparatively better than other algorithms taken into consideration for the purpose of comparison 18 Leader Harris Hawks Optimization (LHHO) Masi entropy Naik et al ( 2021 ) Standard Gray Scale Images Proposed method is compared with HHO, CS, PSO, FA, WDO, STOA and DE PSNR, FSIM and SSIM The proposed LHHO rapidly attains the optimum threshold values in regard to the iteration count 19 Attacking Manta Ray Foraging Optimization (AMRFO) Maximum 3D Tsallis entropy Jena et al ( 2021 ...…”

Section: Recent Trends In Multi-level Thresholding Using Nature-inspi...mentioning

confidence: 99%

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

2022

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Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

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Section: Recent Trends In Multi-level Thresholding Using Nature-inspi...mentioning

confidence: 99%

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

2022

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“…To evaluate the performance of ESSA, the proposed ESSA was compared with four traditional intelligent algorithms, including butterfly optimization algorithm(BOA) [25], sine cosine algorithm(SCA) [26], whale optimization algorithm(WOA) [27], sparrow search algorithm(SSA), and five advanced intelligent algorithms, like improved grey wolf optimizer (IGWO) [28], leader slime mould algorithm (LSMA) [29], leader Harris hawks optimization (LHHO) [30], adaptive opposition slime mould algorithm (AOSMA) [31], hybrid butterfly optimization algorithm with particle swarm optimization (HPSOBOA) [32]. Table 1 signifies the parameters settings for 10 algorithms.…”

Section: The Evaluation Of Essamentioning

confidence: 99%

Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF

Quan¹,

Li²,

Cui³

et al. 2021

Preprint

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：An effective technology for predicting the end-point phosphorous content of basic oxygen furnace (BOF) can provide theoretical instruction to improve the quality of steel via controlling the hardness and toughness. Given the slightly inadequate prediction accuracy in the existing prediction model, a novel hybrid method was suggested to more accurately predict the end-point phosphorus content by integrating an enhanced sparrow search algorithm (ESSA) and a multi-strategy with a deep extreme learning machine (DELM) as ESSA-DELM in this study. To begin with, the input weights and hidden biases of DELM were randomly selected, resulting in that DELM inevitably had a set of non-optimal or unnecessary weights and biases. Therefore, the ESSA was used to optimize the DELM in this work. For the ESSA, the Trigonometric substitution mechanism and Cauchy mutation were introduced to avoid trapping in local optima and improve the global exploration capacity in SSA. Finally, to evaluate the prediction efficiency of ESSSA-DELM, the proposed model was tested on process data of the converter from the Baogang steel plant. The efficacy of ESSA-DELM was more superior to that of other DELM-based hybrid prediction models and conventional models. The result demonstrated that the hit rate of end-point phosphorus content within ±0.003%, ±0.002%, and ±0.001% was 91.67%, 83.33%, and 63.55%, respectively. The proposed ESSA-DELM model could possess better prediction accuracy compared with other models, which could guide field operations.

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“…The main focus of the research proposed in this manuscript is the development of an enhanced Harris Hawks optimization (HHO) algorithm that addresses the observed flaws of its basic version. The HHO is a recently proposed, yet well-known swarm intelligence metaheuristics [ 6 ] that showed great potential in tackling many real-world challenges [ 7 , 8 ]. In this study, the authors tried to further investigate and expand the HHO’s potential by incorporating a chaotic mechanism and a novel replacement strategy that enhance both the exploitation and exploration of the basic algorithm, with only a small additional overhead in terms of computational complexity and new control parameters.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

Basha

Bačanin

Vukobrat

et al. 2021

Sensors

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The research presented in this manuscript proposes a novel Harris Hawks optimization algorithm with practical application for evolving convolutional neural network architecture to classify various grades of brain tumor using magnetic resonance imaging. The proposed improved Harris Hawks optimization method, which belongs to the group of swarm intelligence metaheuristics, further improves the exploration and exploitation abilities of the basic algorithm by incorporating a chaotic population initialization and local search, along with a replacement strategy based on the quasi-reflection-based learning procedure. The proposed method was first evaluated on 10 recent CEC2019 benchmarks and the achieved results are compared with the ones generated by the basic algorithm, as well as with results of other state-of-the-art approaches that were tested under the same experimental conditions. In subsequent empirical research, the proposed method was adapted and applied for a practical challenge of convolutional neural network design. The evolved network structures were validated against two datasets that contain images of a healthy brain and brain with tumors. The first dataset comprises well-known IXI and cancer imagining archive images, while the second dataset consists of axial T1-weighted brain tumor images, as proposed in one recently published study in the Q1 journal. After performing data augmentation, the first dataset encompasses 8.000 healthy and 8.000 brain tumor images with grades I, II, III, and IV and the second dataset includes 4.908 images with Glioma, Meningioma, and Pituitary, with 1.636 images belonging to each tumor class. The swarm intelligence-driven convolutional neural network approach was evaluated and compared to other, similar methods and achieved a superior performance. The obtained accuracy was over 95% in all conducted experiments. Based on the established results, it is reasonable to conclude that the proposed approach could be used to develop networks that can assist doctors in diagnostics and help in the early detection of brain tumors.

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A leader Harris hawks optimization for 2-D Masi entropy-based multilevel image thresholding

Cited by 62 publications

References 69 publications

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

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