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Related Experiment Video

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A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

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A modified weighted chimp optimization algorithm for training feed-forward neural network.

Eman A Atta¹, Ahmed F Ali², Ahmed A Elshamy¹

¹Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt.

|March 28, 2023

Summary

This summary is machine-generated.

A new Modified Weighted Chimp Optimization Algorithm (MWChOA) effectively trains feed-forward neural networks. By reducing leader solutions, it enhances exploration and avoids local optima, outperforming 16 other swarm intelligence algorithms.

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Area of Science:

Computational Intelligence
Machine Learning
Optimization Algorithms

Background:

Swarm intelligence (SI) algorithms utilize exploration and exploitation mechanisms for optimal solutions.
Balancing exploration and exploitation is crucial for SI algorithm performance.
Standard and weighted Chimp Optimization Algorithms (ChOA, WChOA) can be prone to local optima.

Purpose of the Study:

To propose a Modified Weighted Chimp Optimization Algorithm (MWChOA) for training feed-forward neural networks (FNNs).
To address the local optima drawback of existing ChOA variants.
To enhance the exploration capabilities of the Chimp Optimization Algorithm.

Main Methods:

Modification of the Chimp Optimization Algorithm by reducing leader solutions from four to three.
Training a feed-forward neural network using the proposed MWChOA.
Comparative analysis against 16 other SI algorithms on the Eleven dataset.

Main Results:

The MWChOA demonstrated improved search capabilities and an enhanced exploration phase.
The algorithm successfully avoided trapping in local optima.
MWChOA achieved superior performance in training FNNs compared to other SI algorithms.

Conclusions:

The proposed MWChOA effectively trains feed-forward neural networks.
Reducing the number of leader solutions is a viable strategy to improve SI algorithm performance.
MWChOA offers a promising alternative for optimization tasks in machine learning.