A novel adaptive particle swarm optimization to solve traveling salesman problem
Weitang Song, Shumei Zhang · 2009
Particle swarm optimization (PSO) is a kind of evolutionary algorithm to find optimal (or near optimal) solutions for numerical and qualitative problems. In this paper, a new variation on the traditional PSO algorithm, called adaptive particle swarm optimization (APSO), has been proposed, employing adaptive behavior to significantly improve the performance of the original algorithm. Every particle chooses its inertial factor according to the fitness of itself and the optimal particle in the presented algorithm. Finally, traveling salesman problem (TSP) is applied to show the effectiveness of the proposed PSO. Simulation results show that the new algorithm has advantage of global convergence property and can effectively alleviate the problem of premature convergence.