Optimization and simulation research on Ant Colony Algorithm

Aims at remedying the default of precocity and stagnation in the standard Ant Colony Algorithm(ACA),the rule of dynamic updating pheromones is presented, so that the area of feasible solutions are expanded, and the capability of global search is enhanced. Furthermore by introducing the dynamic updating strategy of parameters selection, the probability of solution mutation is increased, so dynamic adjusting the selected path is obtained,also with the improved solving. The simulation result on path planning which contains 32 nodes in a logistics distribution system shows that optimized ACA has excellent global optimization properties and faster the convergence speed, and it can avoid stasis phenomenon of ACA.