A novel improved whale optimization algorithm to solve numerical optimization and real-world applications

Chakraborty, Subhankari Prasad; Sharma, Sushmita; Saha, Apu Kumar

doi:10.1007/s10462-021-10114-z

Cited by 55 publications

(17 citation statements)

References 65 publications

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“…An improved butterfly optimization algorithm was proposed by Sharma et al [ 89 ], which is utilized for image segmentation problems. Chakraborty et al [ 90 ] introduced an enhanced version of WOA to tackle image segmentation problems. A new pulse couple neural network model based on grey wolf optimizer was proposed by Wang et al [ 88 ] for medical image segmentation.…”

Section: Applicationsmentioning

confidence: 99%

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

et al. 2022

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Whale optimization algorithm (WOA) tends to fall into the local optimum and fails to converge quickly in solving complex problems. To address the shortcomings, an improved WOA (QGBWOA) is proposed in this work. First, quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions. Second, a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA. To verify the advantages of QGBWOA, comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10, 30, 50, and 100 and on CEC 2020 test with dimension 30. Furthermore, the performance results were tested using Wilcoxon signed-rank (WS), Friedman test, and post hoc statistical tests for statistical analysis. Convergence accuracy and speed are remarkably improved, as shown by experimental results. Finally, feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems. QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics. Supplementary Information The online version contains supplementary material available at 10.1007/s42235-022-00297-8.

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Section: Applicationsmentioning

confidence: 99%

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

et al. 2022

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“…Similarly, Ju et al 44 suggested a hybrid strategy of WOA based on nonlinear convergence factor, chaos initialization, and mutation concepts. Further, Chakraborty et al proposed various artificial intelligence models using WOA and their variants for numerous applications, such as for COVID-19 X-ray image segmentation 45 , global optimization 46 , 47 , numerical optimization 48 , and other applications 49 – 52 .…”

Section: Introductionmentioning

confidence: 99%

Modified Whale Optimization Algorithm based ANN: a novel predictive model for RO desalination plant

Mahadeva

Kumar

Gupta

et al. 2023

Sci Rep

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In recent decades, nature-inspired optimization methods have played a critical role in helping industrial plant designers to find superior solutions for process parameters. According to the literature, such methods are simple, quick, and indispensable for saving time, money, and energy. In this regard, the Modified Whale Optimization Algorithm (MWOA) hybridized with Artificial Neural Networks (ANN) has been employed in the Reverse Osmosis (RO) desalination plant performance to estimate the permeate flux (0.118‒2.656 L/h m2). The plant’s datasets have been collected from the literature and include four input parameters: feed flow rate (400‒600 L/h), evaporator inlet temperature (60‒80 °C), feed salt concentration (35‒140 g/L) and condenser inlet temperature (20‒30 °C). For this purpose, ten predictive models (MWOA-ANN Model-1 to Model-10) have been proposed, which are capable of predicting more accurate permeate flux (L/h m2) than the existing models (Response Surface Methodology (RSM), ANN and hybrid WOA-ANN models) with minimum errors. Simulation results suggest that the MWOA algorithm demonstrates a stronger optimization capability of finding the correct weights and biases so as to enable superior ANN based modeling without limitation of overfitting. Ten MWOA-ANN models (Model-1 to Model-10) have been proposed to investigate the plant’s performance. Model-6 with a single hidden layer (H = 1), eleven hidden layer nodes (n = 11) and the thirteen search agents (SA = 13) produced most outstanding regression results (R2 = 99.1%) with minimal errors (MSE = 0.005). The residual errors for Model-6 are also found to be within limits (span of − 0.1 to 0.2). Finally, the findings show that the screened MWOA-ANN models are promising for identifying the best process parameters in order to assist industrial plant designers.

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“…Thus, it is essential to boost the threshold statuses from bi-level to multi-level [12]. The multi-level threshold (MTH) divides an image into multi categories belonging to multiple entities in the image [13,14]. This way, it is feasible to indicate more details and entities from the segmented image.…”

Section: Introductionmentioning

confidence: 99%

Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation

et al. 2023

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This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. The proposed method introduces a better search space to find the optimal solution at each iteration. However, we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds. The obtained solutions by the proposed method are represented using the image histogram. The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level. The performance measure for the proposed method is valid by detecting fitness function, structural similarity index, peak signal-to-noise ratio, and Friedman ranking test. Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA. The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.

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A novel improved whale optimization algorithm to solve numerical optimization and real-world applications

Cited by 55 publications

References 65 publications

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

Modified Whale Optimization Algorithm based ANN: a novel predictive model for RO desalination plant

Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation

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