An Improved Flower Pollination Algorithm for Global Numerical Optimization

Lu, Kezhong; Ma, Zongmin; Yang, Xiaofei; Zhou, Hao

doi:10.1109/dcabes50732.2020.00077

Cited by 2 publications

(1 citation statement)

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“…Three variables control the stopping criteria: , , and . Another parameter set for QIFPNN and FPNN is switch probability [16,17,[22][23][24]. Specifically for PSONN, the learning parameters are α β , while for BANN, the parameter and the parameter [25].…”

Section: B Parameter Settingsmentioning

confidence: 99%

A Novel Approach to Multi-Layer-Perceptron Training using Quadratic Interpolation Flower Pollination Neural Network on Non-Binary Datasets

Polly¹,

Hartati²,

Suprapto³

et al. 2023

IJACSA

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Machine Learning (ML) algorithms are widely used in solving classification problems. The biggest challenge of classification lies in the robustness of the ML algorithm in various dataset characteristics. Quadratic Interpolation Flower Pollination Neural Network (QIFPNN) is categorised into ML algorithm. The new QIFPNN's extraordinary capabilities are measured on binary-type datasets. This research ensures that the remarkable ability of QIFPNN also applies to non-binary datasets with balanced and unbalanced data class characteristics. Flower Pollination Neural Network (FPNN), Particle Swarm Optimisation Neural Network (PSONN), and Bat Neural Network (BANN) were used as comparisons. The QIFPNN, FPNN, PSONN, and BANN were used to train Multi-Layer-Perceptron (MLP). The test results on five datasets show that QIFPNN obtains an average classification accuracy higher than its comparison in three datasets with balanced and unbalanced data class characteristics. The three datasets are Iris, Wine, and Glass. The highest classification accuracy obtained by QIFPNN in the three datasets is 97.1462%, 98.6551%, and 73.1979%, respectively. Based on the F1-score test from QIFPNN, it is higher than all the comparisons in four datasets: Iris, Wine, Vertebral column, and Glass. Sequentially, 96.4599%, 98.7155%, 90.7517%, and 60.2843%. It proves that QIFPNN can also classify datasets with non-binary data types with balanced and unbalanced data class characteristics because they are more consistently tested on various datasets and are not susceptible to the influence of variations in dataset characteristics so that they can be applied to various types of data or cases.

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