Navigating Robot Paths: A Comparative Study of Ant Colony Optimization and Firefly Algorithm for Optimization

Nowadays, with the growing advancement of machine learning and artificial intelligence in various fields like robotics and automated machines, real-time algorithms are needed to execute the operation. Different types of problems may occur during operation. To deal with these problems, we apply some algorithms such as the bionic-inspired metaheuristic algorithm, ACO, and FA in one of them. In this paper, we present the comparative study of Ant Colony Optimization (ACO) and firefly algorithm (FA) for robotic path planning in a static environment with obstacles. These algorithms are population-based algorithms. Swarm intelligence optimization algorithms have been a trendy place for study in the field of computational intelligence in recent years. Therefore, we briefly review the fundamentals of both ACO and Firefly algorithm (FA) and its application in different fields. Then, apply it to the real-time grid-based environment having different shapes at different positions of obstacles in the environment. Then we conclude the result on the basis of outcomes such as optimum path (shortest path) length, time of execution, and convergence characteristic of both algorithms.