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Related Experiment Video

Updated: Dec 3, 2025

Anesthesia-free Heartbeat Measurements in Freely Moving Zebrafish

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Published on: April 18, 2025

Teaching learning-based whale optimization algorithm for multi-layer perceptron neural network training.

Yong Quan Zhou^1,2,3, Yan Biao Niu^1,2, Qi Fang Luo^1,2,3

¹College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China.

Mathematical Biosciences and Engineering : MBE

|October 30, 2020

Summary

This summary is machine-generated.

A new Teaching Learning-based Whale Optimization Algorithm (TSWOA) enhances multi-layer perceptron (MLP) neural network training. This improved whale optimization algorithm achieves better performance and faster convergence than existing methods.

Keywords:

metaheuristic algorithm multi-layer perceptron (MLP) neural network teaching learning-based whale optimization algorithm

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Area of Science:

Computational Intelligence
Machine Learning Optimization

Background:

Whale Optimization Algorithm (WOA) offers a robust framework for optimization problems.
Balancing exploration and exploitation in WOA remains a challenge.
Teaching Learning-Based Algorithm (TLBO) introduces self-learning capabilities.

Purpose of the Study:

To introduce an improved Teaching Learning-based Whale Optimization Algorithm (TSWOA).
To enhance the optimization performance for training Multi-Layer Perceptron (MLP) neural networks.
To improve convergence speed and accuracy in optimization tasks.

Main Methods:

Integration of WOA with TLBO to balance exploration and exploitation.
Incorporation of the simplex method to optimize the worst-performing agents.
Utilizing TSWOA to train MLP neural networks on diverse datasets.

Main Results:

TSWOA demonstrates a superior balance between exploration and exploitation.
The simplex method integration prevents boundary search and enhances convergence.
TSWOA significantly outperforms WOA and other established algorithms in MLP training.

Conclusions:

TSWOA offers a theoretically enriched and practically effective optimization algorithm.
The proposed method provides a robust solution for training MLP neural networks.
TSWOA shows significant potential for advancing machine learning optimization techniques.