Discrete particle swarm optimization for TSP based on neighborhood
基于近邻关系求解TSP的离散PSO算法*

Aiming at the NP-hard combinatorial optimization problem, this paper proposed a hybrid discrete particle swam optimization (DPSO)algorithm based on neighborhood. In order to improve the algorithm stability and accelerate the convergence speed, updated kinetic equations for discrete particle swarm optimization and introduced a mechanism of heuristic factor into this algorithm. Introduced adaptive perturbation factor according to the population heterogeneity to keep particle swarm evolutional capability. Experiments on low and high-dimensional data in TSPLIB show that, comparing with others hybrid discrete particle swarm, the proposed algorithm can improve the search performance significantly no matter in convergent speed or precision.