Ant Colony Optimization Algorithm

ACO is a potent algorithm inspired by ants' social behavior. It can solve complex computational problems and offers a promising approach to finding near-optimal solutions in various domains. This essay focuses on ACO's potential to solve the Travelling Salesman Problem (TSP) by mimicking ants' pheromone-based path finding. To examine how different ACO algorithm parameters, such as pheromone power, ant speed, and distance power, affect the algorithm's performance, we aim to uncover optimal parameter ranges that improve the ACO algorithm's efficiency and efficacy in solving TSP situations through thorough experimentation and research. Our results reveal the complex balancing act between exploration and exploitation inside the ACO algorithm, offering insightful information for both scholarly work and real-world applications.