Using Fitness Dependent Optimizer for Training Multi-layer Perceptron

TL;DR

This paper introduces a new training algorithm based on the Fitness Dependent Optimizer (FDO) for multi-layer perceptrons, demonstrating improved convergence and accuracy in predicting student outcomes compared to traditional and other evolutionary methods.

Contribution

The study develops and validates a novel FDO-based training method for MLPs, showing it outperforms existing algorithms in speed and accuracy for educational data prediction.

Findings

FDO-MLP achieves 97% classification accuracy.

FDO-based training outperforms BP and other evolutionary algorithms.

The approach enhances convergence speed and avoids local optima.

Abstract

This study presents a novel training algorithm depending upon the recently proposed Fitness Dependent Optimizer (FDO). The stability of this algorithm has been verified and performance-proofed in both the exploration and exploitation stages using some standard measurements. This influenced our target to gauge the performance of the algorithm in training multilayer perceptron neural networks (MLP). This study combines FDO with MLP (codename FDO-MLP) for optimizing weights and biases to predict outcomes of students. This study can improve the learning system in terms of the educational background of students besides increasing their achievements. The experimental results of this approach are affirmed by comparing with the Back-Propagation algorithm (BP) and some evolutionary models such as FDO with cascade MLP (FDO-CMLP), Grey Wolf Optimizer (GWO) combined with MLP (GWO-MLP), modified GWO combined with MLP (MGWO-MLP), GWO with cascade MLP (GWO-CMLP), and modified GWO with cascade MLP (MGWO-CMLP). The qualitative and quantitative results prove that the proposed approach using FDO as a trainer can outperform the other approaches using different trainers on the dataset in terms of convergence speed and local optima avoidance. The proposed FDO-MLP approach classifies with a rate of 0.97.