An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modelling

Nonlinear regression analysis is an important statistical method widely used in many fields of science to model the complex relationships between variables. Therefore, studies have been conducted estimate parameters nonlinear models using various iterative techniques. In this study, efficient hybrid algorithm, namely PSONM, by combining exploration capability Particle Swarm Optimization (PSO) and exploitation Nelder-Mead (NM) algorithm proposed obtain parameter estimates models. To show performance 20 tasks with levels difficulty, real data sets agriculture field tested. The experimental results indicated that suggested provides accurate estimates, its much superior those NM PSO algorithms.