Application of Swarm Intelligence in Transportation System Optimisation

Swarm intelligence is a branch of artificial and natural intelligence that studies systems with many characters that manage their activities through distributed control and self-organisation. The field focuses on the actions of social insects such as fish schools, bird flocks, ants, termites, bees, and wasp colonies. Particle swarm optimisation (PSO) and ant colony optimisation (ACO) are two of the most common systems reported by swarm intelligence. Ant Colony Optimization (ACO) is a probabilistic method used to find optimal pathways in computationally complex situations by condensing the problem. This paper analyses the use of ACO metaheuristics to find the initial basic feasible solution in an unbalanced and balanced transportation method. Then, it compares it to other traditional methods (The least cost method, Northwest Corner method, and Vogel’s approximation method). The primary goal of this study is to provide a helpful framework for understanding new trends in applying swarm intelligence in system optimisation and implementing/using the ACO algorithm in a real-life situation. Examples were generated online. At the end of the paper, for the unbalanced transportation problem, the Least Cost method, Northwest Corner method, Vogel’s Approximation method, and ACO method gave us (472, 547, 374, and 389) as the total cost, respectively. For the balanced transportation problem, the Least Cost, Northwest Corner, Vogel’s Approximation, and ACO methods gave us (2450, 3700, 2150, and 3650) as the total cost, respectively.