Employing the Artificial Cuckoo Swarm Algorithm for Estimating Various Multiple Nonlinear Regression Models
DOI:
https://doi.org/10.62933/cpa5x988Abstract
Nonlinear regression models are widely used to model stochastic phenomena, and parameter estimation is essential for interpreting these models. Consequently, this research used the Artificial Cuckoo Swarm Algorithm to improve parameter estimation precision for three nonlinear models: Meyer 1, Meyer 5, and the Nelson Model. We employed two estimation techniques: nonlinear least squares (NLS) and the S estimation approach. To improve the efficacy of these two estimating approaches, they were integrated with the Artificial Cuckoo Swarm Algorithm. This work used simulation to evaluate the effectiveness of these two methodologies before and after the application of the algorithm, using sample sizes (50, 100, 150) and sigma levels (0.1, 0.5, 0.9). The mean squared error (MSE) is used as a benchmark for comparison. Simulation results demonstrate that the s-estimator method, particularly when combined with an AI algorithm, produces the smallest mean squared error (MSE) across all models. Furthermore, this technique achieved the lowest MSE values in almost every instance, even without the algorithm.
In the pollution data for Iraq's Euphrates River, we employed the robust s-estimator method, along with the Cuckoo Swarm Algorithm, identified as optimal in simulation results, to estimate three nonlinear regression models with total dissolved solids as the dependent variable and sulfates and chlorides as the independent variables.
The Cuckoo Swarm Algorithm method significantly improved the three nonlinear regression models included in the study, demonstrating its effectiveness. The findings indicated that chlorine and sulfates directly increase total dissolved solids in the Euphrates River.
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