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The genetic particle swarm optimization (GPSO) was derived from the original particle swarm optimization (PSO), which is incorporated with the genetic reproduction mechanisms, namely crossover and mutation. To solve traveling salesman problems (TSP), a modified genetic particle swarm optimization (MGPSO) was introduced, where the new solution was generated with local best and individual best solutions with crossover and mutation operators. MGPSO was implemented to the well-known TSP and by comparison with the results of the original PSO, MGPSO has provided much better performance. Furthermore, MGPSO was employed to solve TSP with time windows, where besides minimizing the route, the truck were required to arrive at specifically during a time window, which made the TSP to be a constrained combinatorial optimization. To solve the constraints, the stochastic ranking algorithm was introduced The approach was experimented with the well-known TSP case. The simulation results have shown its robust and consistent effectiveness.
Date of Conference: 1-6 June 2008