Paper Details

Name: Shubhangi Jagdish Kamble
Country: India

Year: 2026
Volume: Volume No: 13, January, Year: 2026 (Special Issue)
Page Number: 36-44
DOI: https://doi.org/10.5281/zenodo.18934242

ABSTRACT
This paper presents a comprehensive survey of the Rao optimization algorithm focusing on its applications in the omnidirectional domain, including robotics, image processing, machine learning, and renewable energy systems. Rao’s adaptability and robustness make it an effective tool for solving complex, high-dimensional, nonlinear, and dynamic optimization problems. A key contribution is the exploration of hybrid Rao algorithms, such as Rao-Particle Swarm Optimization (PSO), Rao-Differential Evolution (DE), and Rao-Genetic Algorithms (GA) to address challenges like slow convergence in high-dimensional spaces. The paper highlights Rao's potential in real-time applications, such as autonomous robot path planning and machine learning hyper-parameter tuning. Additionally, it examines Rao’s role in multi-objective optimization, a crucial aspect of engineering design and system optimization. The study underscores Rao's strengths in handling dynamic optimization tasks, balancing exploration and exploitation, and improving convergence speed through hybrid approaches. A comparative analysis with other meta-heuristic algorithms like GA, PSO, and DE shows Rao’s superior global search capability and computational efficiency. The results demonstrate Rao’s versatility and potential for solving real-world optimization problems, especially in high-dimensional, dynamic environments. This survey provides valuable insights for researchers and practitioners aiming to use Rao optimization for complex, real-time, and
multi-objective tasks in various domains.

Keywords— Rao optimization, renewable energy systems, hybrid algorithms, multi-objective optimization, robotics, image processing, machine learning