AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence
Abstract
This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks. In order to support our proposed AdaSwarm, a novel Exponentially weighted Momentum Particle Swarm Optimizer (EMPSO), is proposed. The ability of AdaSwarm to tackle optimization problems is attributed to its capability to perform good gradient approximations. We show that, the gradient of any function, differentiable or not, can be approximated by using the parameters of EMPSO. This is a novel technique to simulate GD which lies at the boundary between numerical methods and swarm intelligence. Mathematical proofs of the gradient approximation produced are also provided. AdaSwarm competes closely with several state-of-the-art (SOTA) optimizers. We also show that AdaSwarm is able to handle a variety of loss functions during backpropagation, including the maximum absolute error (MAE).
Cite
@article{arxiv.2006.09875,
title = {AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence},
author = {Rohan Mohapatra and Snehanshu Saha and Carlos A. Coello Coello and Anwesh Bhattacharya and Soma S. Dhavala and Sriparna Saha},
journal= {arXiv preprint arXiv:2006.09875},
year = {2024}
}
Comments
11 pages, 2 figures; Accepted at IEEE TETCI