Distributed MOPSO with dynamic pareto front driven population analysis for TSP problem

This paper describe the use of Multi-Objective concept to solve the Traveling Salesman Problem (TSP). The traveling salesman problem is defined as an NP-hard problem. The resolution of this kind of problem is based firstly on exact methods and after that is based on single objective based methods as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Firstly, a short description of the Multi-objective Particles swarm optimization (MOPSO) is given as an efficient technique to use for many real problems. Based on the concept of Pareto dominance, the process of implementation of the algorithm consists of two stages. First, when executing a multi-objective Particle Swarm Optimization (MOPSO), a ranking operator is applied to the population in a predefined iteration to build an initial archive using ε-dominance The TSP problem is characterized by two contradictory objectives as minimize the total distance traveled by a particle and minimize the total time. An experimental study is conducted in this paper. A comparative study with other algorithms existing in the literature has shown a better performance of our algorithm (pMOPSO).