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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Published on: February 9, 2024

Intelligent incremental classification using a dynamic grasshopper-enhanced neural network for data streams.

Saad M Darwish¹, Noha A El-Shoafy²

¹Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 21526, Alexandria, Egypt. saad.darwish@alexu.edu.eg.

Scientific Reports

|February 26, 2026

Summary

This summary is machine-generated.

This study introduces a Dynamic Grasshopper Optimization Algorithm (DGOA) for real-time hyperparameter tuning in neural networks handling complex data streams. The DGOA-enhanced system achieves superior accuracy and efficiency without retraining, outperforming other optimization methods.

Keywords:

Big data Dynamic grasshopper optimization Incremental learning Intelligent systems Neural network optimization

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Area of Science:

Artificial Intelligence
Machine Learning
Optimization Algorithms

Background:

Complex data streams present challenges for neural networks due to dynamism and distributional shifts.
Frequent retraining is often required to maintain accuracy, impacting efficiency.
Existing optimization methods struggle with real-time adaptation to evolving data characteristics.

Purpose of the Study:

To propose an intelligent incremental learning framework for real-time hyperparameter optimization in neural networks.
To develop a Dynamic Grasshopper Optimization Algorithm (DGOA) for adaptive learning on data streams.
To enhance classification accuracy and efficiency in dynamic environments without constant retraining.

Main Methods:

Integration of a Dynamic Grasshopper Optimization Algorithm (DGOA) with a Multilayer Perceptron (MLP) neural network.
Utilizing DGOA's dynamic parameter control and online swarm reconfiguration for autonomous adaptation.
Incremental tuning of hyperparameters like learning rate and momentum for continuous learning.

Main Results:

The DGOA-based MLP achieved 89.5% classification accuracy on the Australian electricity market dataset.
Outperformed Grid Search, Random Search, PSO, GA, ACO, and standard GOA in classification accuracy.
Demonstrated superior efficiency with reduced computational time (120s), faster convergence (30 iterations), and lowest final loss (0.21).

Conclusions:

The proposed DGOA framework offers a fully online, swarm-intelligence-driven hyperparameter optimization strategy for big data streams.
This approach significantly improves accuracy, generalization, and computational efficiency compared to conventional methods.
The system effectively handles continuous distributional shifts in data streams, enabling robust and adaptive classification.