Whale

Quantum mechanics

Algorithm

Gaussian

Biology

Statistics

Fishery

Friedman test

Mann–Whitney U test

Selection (genetic algorithm)

Computer science

Mathematical optimization

Coronavirus disease 2019 (COVID-19)

Image segmentation

Boosting (machine learning)

Physics

Segmentation

Pattern recognition (psychology)

Mathematics

Statistical hypothesis testing

Feature vector

Feature selection

Infectious disease (medical specialty)

Wilcoxon signed-rank test

Artificial intelligence

Medicine

Disease

Machine learning

Computer vision

Pathology

Journal of Bionic Engineering

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.

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