篇名 |
Using Fitness Dependent Optimizer for Training Multi-layer Perceptron
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並列篇名 | Using Fitness Dependent Optimizer for Training Multi-layer Perceptron |
作者 | Dosti Kh. Abbas、Tarik A. Rashid、Karmand H. Abdalla、Nebojsa Bacanin、Abeer Alsadoon |
英文摘要 | 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. |
起訖頁 | 1575-1585 |
關鍵詞 | Optimization、Multilayer perceptron、Fitness dependent optimizer、Training neural network、Academic student performance |
刊名 | 網際網路技術學刊 |
期數 | 202112 (22:7期) |
出版單位 | 台灣學術網路管理委員會 |
DOI | 10.53106/160792642021122207011 複製DOI |
QR Code | |
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