Disease Prediction using Hybrid Optimization Methods based on Tuning Parameters

Abstract

Swarm Intelligence (SI) is increasing day by day in the various research fields. There are many swarm-based optimizations introduced since the early ’60s, Evolutionary Algorithms (EA) is the most updated one. All Evolutionary Algorithms have proved their capability to resolve most of the optimization problems. These algorithms are using for training the neural networks in this paper. The main difficulty for any optimization problem is selecting the correct values of parameters to get possible results. The main idea to get the best convergence rate and best performance is to vary the parameters of the algorithms. This paper provides a comparison of the most used and essential swarm-based optimization algorithms. Here, comparing the optimization algorithms, Particle Swarm Optimization (PSO), and Multi-Verse Optimization (MVO) before and after tuning the parameters with three different datasets.