Kernel semi-parametric model improvement based on quasi-oppositional learning pelican optimization algorithm

Abstract

Statistical modeling is essential in many scientific research areas because it explains the relationship between the response variable of interest and a number of explanatory variables. However, it is not easy to determine the optimal model beforehand. Therefore, in this paper, we look at how to choose a hyper-parameter in a kernel semi-parametric regression model. A quasi-oppositional learning pelican optimization algorithm strategy is used to select the smoothness parameter. In comparison to other competitor approaches, simulation results revealed that the suggested method, the quasi-oppositional learning pelican optimization algorithm, is superior in terms of MSE. The experimental findings and statistical analysis show that when compared to the CV and GCV, our proposed quasi-oppositional learning pelican optimization algorithm provides greater performance in terms of computational time.

Optimizing Northern Goshawk Algorithm with Fuzzy Logic and Whale Algorithm Strategies

“…The Meta-Heuristic Algorithm (MHA) relies on the characteristics and behaviours of organisms in their search for food and predation avoidance, including those of solitary and swarming animals [1]. Where scientists developed these behaviours into mathematical models, which are used to address complex mathematical problems and problems of improvement and search for optimal solutions [2].…”

Measurement Enhancement of Ultrasonic Sensor using Pelican Optimization Algorithm for Robotic Application

“…It was a clever adaptation that allowed the pelican birds to become skilled hunters, based on the method in which pelicans go about their hunts and the techniques they employ. Other researchers worked on the modern method called POA (Algamal et al, 2023).…”

“…where 𝑋 𝑖 𝑝 2 = updated position pelican of exploitation phase and 𝑓 𝑖 𝑝 2 = fitness function of exploitation phase (Algamal, et al, 2023).…”