Celestial Orbit Optimization as a Metaheuristic Algorithm Inspired by Planetary Motion for Solving Complex Optimization Problems

This paper introduces a new and innovative algorithm named Celestial Orbit Optimization (COO), which is inspired by the orbital dynamics of celestial bodies. In this algorithm, each agent is considered as a point in the search space that moves around the current best position in a controlled orbital manner. The vector structure of the algorithm is based on the definition of 2D local planes and the use of random angles in the analyzable paths, which improves stability, increases accuracy, and reduces the rate of getting stuck in local minima. To evaluate the performance of COO, a set of standard test functions in 30-dimensional space is selected and the algorithm is compared with reference methods such as PSO, GA, GWO, DE, and GSA. Numerical results, Wilcoxon and Friedman statistical analyses, and five-dimensional qualitative studies show that COO performs better in most indicators. The simplicity of implementation, structural transparency, and inherent balance between exploration and exploitation make COO a reliable option in solving complex optimization problems.