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Related Experiment Video

Updated: Aug 19, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

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

Jie Xing¹, Hanli Zhao¹, Huiling Chen¹

¹Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035 China.

Journal of Bionic Engineering

|December 5, 2022

Summary

The improved Whale Optimization Algorithm (QGBWOA) enhances search capabilities and speeds up convergence for complex problems. This novel approach demonstrates superior performance in feature selection and image segmentation tasks.

Keywords:

Bionic algorithm Feature selection Gaussian barebone Image segmentation Quasi-opposition-based learning Whale optimization algorithm

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Area of Science:

Computational Intelligence
Optimization Algorithms
Machine Learning

Background:

Whale Optimization Algorithm (WOA) often gets trapped in local optima and exhibits slow convergence.
Complex optimization problems require robust algorithms with improved exploration and exploitation capabilities.

Purpose of the Study:

To introduce a Quasi-Oppositional Gaussian Barebone Whale Optimization Algorithm (QGBWOA).
To enhance the convergence speed and accuracy of the Whale Optimization Algorithm.
To validate QGBWOA's effectiveness on complex real-world applications.

Main Methods:

Incorporating quasi-opposition-based learning to improve global search.
Integrating a Gaussian barebone mechanism to enhance population diversity.
Conducting comparative experiments on CEC 2014 and CEC 2020 benchmark datasets.
Performing statistical analysis using Wilcoxon signed-rank and Friedman tests.

Main Results:

QGBWOA demonstrated significantly improved convergence accuracy and speed compared to peer algorithms.
Experimental results validated QGBWOA's effectiveness on feature selection and multi-threshold image segmentation.
Statistical tests confirmed the superiority of QGBWOA across various dimensions and datasets.

Conclusions:

QGBWOA effectively addresses the limitations of the standard WOA.
The proposed algorithm shows strong potential for solving complex optimization problems.
QGBWOA outperforms existing methods in practical applications like feature selection and image segmentation.