Self-adaptive Ant Colony Algorithm Based on Statistical Analysis and Its Application

Path planning is important in robot field and in this field, many researchers have done a lot of work. This paper proposes an improved ant colony algorithm as traditional ones have a shortage of slowly convergence and easily falling into local optimum. On the basis of traditional ones, the dynamic random statistical analysis and extraction of each generation of Ant Colony are performed the optimal, average and worst ant information constitutes an adaptive operator for adaptive updating of local pheromones. Simulation results demonstrate that it is effective in equilibrium increasing convergence rate and getting into the contradiction of local optimal solution.