Comparative Analysis of Optimization Algorithms
Year: 2025
Type: dissertation
Abstract: The performance of four optimization algorithms—SGD, SGD with Momentum,Adam, RMSprop, and Adagrad—was compared in this study on resource-constrainedfeedforward neural networks. The analysis by convergence speed, escape successfrom local minima, which was set to ±0.01 perturbations, and ETR (Escape-TimeRatio) which was proposed as a new method of linking recovery efficiency to trainingtime. In the experiments, the same network architectures and standardized datasets(MNIST, CIFAR-10, Iris, and Wine) were used. The results indicated that Adamhad the highest ETR and accuracy, while SGD with Momentum was the most bal-anced regarding speed, stability, and energy efficiency. The outcomes of the studygive practical recommendations for the choice of optimizers in edge and low-powerAI systems and also help to establish the practice sustainable, energy-aware neuralnetwork training.
Author: BINDELA, AJAY KUMAR REDDY
Institution: Blekinge Business Incubator (Sweden)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7106523702
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