Swarm Intelligence for Solving a Traveling Salesman Problem

Abstract

Learning from the social behavior of animals, like bees or ants, opens the field for Swarm Intelligence (SI) algorithms. They can be applied to solve optimization problems, like the Traveling Salesman Problem (TSP). For SI algorithms, each member of the swarm benefits from the whole swarm and the whole swarm benefits from each individual member. The members communicate either directly or indirectly with each other in order to find an optimal solution. This paper presents an overview of three state-of-the-art SI algorithms, namely, the Ant Colony Optimization (ACO), the Particle Swarm Optimization (PSO), and the Bee Colony Optimization (BCO) for solving a TSP. All three algorithms have been implemented and tested. They have been evaluated with respect to the balance between exploration and exploitation.