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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

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An improved butterfly optimization algorithm for training the feed-forward artificial neural networks.

Büşra Irmak¹, Murat Karakoyun¹, Şaban Gülcü¹

¹Department of Computer Engineering, Necmettin Erbakan University, Konya, Turkey.

|October 26, 2022

Summary

This summary is machine-generated.

An improved butterfly optimization algorithm (IBOA) enhances artificial neural network (ANN) training. This chaotic optimization method, IBOA-MLP, shows superior performance in benchmark tests and classification tasks.

Keywords:

Artificial neural networks Butterfly optimization algorithm Chaos Multilayer perceptron Training artificial neural networks

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Area of Science:

Computational Intelligence
Machine Learning
Optimization Algorithms

Background:

Artificial neural networks (ANNs) are powerful learning methods inspired by the human brain.
Efficient training algorithms are crucial for ANN performance.
Existing optimization algorithms may lack dynamic and global search capabilities.

Purpose of the Study:

To propose an improved butterfly optimization algorithm (IBOA) for training feed-forward ANNs.
To incorporate chaotic properties into the optimization process for enhanced exploration.
To evaluate the effectiveness of the IBOA-MLP algorithm on benchmark functions and classification datasets.

Main Methods:

Developed an improved butterfly optimization algorithm (IBOA) incorporating chaotic maps.
Tested IBOA on 13 benchmark global optimization functions.
Applied the IBOA-MLP algorithm to five classification datasets (xor, balloon, iris, breast cancer, heart).

Main Results:

The Tent-mapped IBOA algorithm demonstrated superior performance across most benchmark functions.
The IBOA-MLP algorithm outperformed four other literature algorithms on classification tasks.
Statistical metrics including sensitivity, specificity, precision, and F1-score confirmed IBOA-MLP's success.

Conclusions:

The proposed IBOA algorithm, particularly with chaotic maps, enhances global optimization capabilities.
IBOA-MLP is a successful and effective algorithm for training feed-forward artificial neural networks.
The chaotic property improves the dynamic and global exploration of the search space in optimization.