A Modified Hybrid Particle Swarm Optimization Algorithm for Solving the Traveling Salesmen Problem

The traveling salesman problem (TSP) is a well-known NP-hard combinatorial optimization problem. The problem is easy to state, but hard to solve. Many real-world problems can be formulated as instances of the TSP, for example, computer wiring, vehicle routing, crystallography, robot control, drilling of printed circuit boards and chronological sequencing. In this paper, we present a modified hybrid Particle Swarm Optimization (MHPSO) algorithm in which we combine some principles of Particle Swarm Optimization (PSO), the Crossover operation of the Genetic Algorithm and 2-opt improvement heuristic. The main feature of our approach is that it allows avoiding a major problem of metaheuristics: the parameters setting. In the aim to prove the performance and convergence of the proposed algorithm, we have used it to solve some TSP instances taken from TSPLIB library. Moreover, we have compared our results with those obtained by other algorithms based PSO.