Spatial obstructed distance based on the combination of Ant Colony Optimization and Particle Swarm Optimization

Obstructed distance is an important research topic in spatial clustering with obstacles now. The obstacles constraint is generally ignored in computing distance between two points, and it leads to the clustering result which is of no value, so obstructed distance has a great effect upon clustering result. The paper proposes an algorithm based on ant colony optimization (ACO) and particle swarm optimization (PSO) for spatial obstructed distance, the new algorithm combines the advantages of ACO and PSO effectively, by employing the merits of PSO algorithm for its high efficiency and concision, and the proposed algorithm can obtain efficient initial path, whereby reducing iterative times and accelerating convergence. At the same time, using the parallelizability of ants and distributed parallelized searching technology, the performance of the algorithm can be efficiently improved. The simulation result demonstrates the effectives of the proposed algorithm.