Phong optimization algorithm

Phong optimization algorithm: a new metaheuristic algorithm for solving optimization and classification problems

Effective optimization methods are required due to the increasing complexity and difficulty of real-world

optimization problems. Although several metaheuristic methods have been proposed thus far, only a few have

gained widespread acceptance in the scientific community. To address optimization problems, this study presents

a novel metaheuristic method known as the Phong optimization algorithm (POA). The POA algorithm was

developed using the Phong reflection model, an intriguing model in the field of computer graphics. The Phong

model is an illumination model used to produce realistic 3D images by simulating the interaction of light with

surfaces. Lighting effects at specific locations on a surface are calculated using this local lighting model, which

ignores overall lighting effects. Phong model is fundamental to creating realistic shading and highlights on 3D

objects. These inspirations were mathematically formulated to focus on exploring and exploiting light simulation

and interaction with surfaces inside a certain search space. The ability of the POA algorithm to precisely

search the entire search space with good convergence speed is very significant. Three benchmark test suites,

namely CEC2019, CEC2020, and CEC2022, were used to fully evaluate the performance of the proposed

POA algorithm. These test sets consist of composition, multi-modal, unimodal, and hybrid test functions with

different degrees of complexity and dimensionality. Several classification problems were also solved using the

POA algorithm to demonstrate its dependability and suitability in practical settings. The results of the proposed

POA algorithm surpassed a set of well-known state-of-the-art and newly released metaheuristic algorithms. The

experimental findings of the proposed POA algorithm indicate that POA is an outstanding performance optimization

tool in terms of the balance of exploration and exploitation and convergence speed, as is efficiently

applicable to tackling complex optimization problems.