A particle swarm optimization approach to the orienteering problem

Particle Swarm Optimization (PSO), developed by Kennedy and Eberhart in 1995, was inspired by social behavior of birds flocking or fish schooling. It is a population-based algorithm, where a swarm of particles fly in a multi-dimensional space, guided by their social dynamics while optimizing an objective function. The Orienteering Problem (OP), a variation of the traveling salesman problem, is a NP-hard benchmark problem. Given a set of nodes with associated scores, the objective of the OP is to find a path that maximizes the total score subject to a given time or distance constraint. This paper addresses the design of a PSO algorithm to solve one of the problem instances of the OP and discusses the preliminary results.