OPTIMIZING DEEP NEURAL NETWORKS USING HEURISTIC AND META-HEURISTIC ALGORITHMS

Abstract

The main objective is to use heuristic and
meta-heuristic methods to optimize deep neural
networks.The increasing popularity of deep learning and
artificial intelligence, which calls for quicker
optimization techniques to produce more accurate results,
is the driving force behind this effort. Particle Swarm
Optimization (PSO), Backpropagation (BP), Resistant
Propagation (Rprop), and Genetic Algorithm (GA) are
the algorithms used. Several datasets are subjected to
numerical analysis using the techniques.In order to
reduce training loss, the performance of PSO, BP, Rprop,
and GA are compared in this analysis. Finding out which
algorithms find optimal solutions more effectively is the
aim. It is underlined that meta-heuristic algorithms such
as GA and PSO are higher-level, problem-independent
methods that can be used to a wide variety of problems. It
is well known that heuristic algorithms have extremely
specialized characteristics that change according to the
task at hand. All of the standard algorithms are
extensively presented, including BP, GA, PSO, and
Rprop.How these processes are used to optimize artificial
deep neural networks is explained in the abstract.
Numeral simulations applied to several datasets are run.
Based on error convergence and training epochs, the
results are assessed. The algorithms are evaluated, and it
is noted that over the datasets, meta-heuristic algorithms
(PSO and GA) fared better than traditional heuristic
algorithms (BP and Rprop). The analysis is predicated on
error convergence and training epochs, suggesting a
thorough appraisal of algorithm performance.