Improved artificial fish-swarm algorithm based on adaptive vision for solving the shortest path problem

To solve basic artificial fish-swarm algorithm(AFSA)'s drawbacks of low convergence rate in the latter stage, a large amount of computation and easiness of trapping in local optimal solution, caused by the constant vision of the artificial fish, an improved artificial fish-swarm algorithm based on adaptive vision(AVAFSA) was proposed. The improved algorithm only adjusted the vision of the preying behavior of artificial fish to make the vision gradually decrease with the increase of the number of iterations of the algorithm. When the value became less than half the initial value, it made the value be equal to half the initial value. The proposed improved artificial fish swarm algorithm was applied to the static shortest path problem based on road network to provide customers with the best path. Simulation results depict the improved algorithm has higher convergence rate, a smaller amount of calculation, and is more accurate and stable than the basic AFSA and ant colony optimization(ACO).