Ant Colony Optimization For Bus Rapid Transit System Research And Application

Ant colony algorithm is a computational intelligence inside the classic case of animal groups,bionic,the imitation of ants foraging groups and other activities to complete in the graph to find the optimal path of the random probability-based algorithm.Ant colony algorithm and genetic algorithms are derived from the same inspiration in nature,is a global optimization search method,has a good search performance.Is a gift of nature to solve discrete combinatorial optimization problems of the natural methods.These theories of natural revelation of ant colony algorithm, the basic principles, algorithms formulas and basic steps to achieve a systematic study, and in many areas of its previous research results were summarized. Through research and analysis based on ant colony optimization algorithm and the hybrid particle swarm optimization algorithm, and apply it to solve the TSP. By studying the use of Particle Swarm Optimization for the basic Ant Colony pheromone heuristic factor a and improve correction factor β and the random search, which has been the best combination of values of two parameters, and further realized the TSP Problem. The simulation results obtained the solution of the optimal path to achieve the expected results of this papers, its application has a good meaning and value. Ant colony algorithm and then proposes some improvements in strategy, the update of pheromone manner proposed to improve the Rewards and Punishments of rand-based on Ant Colony System (RP-rand-ACS).In application, select the City Bus Rapid Transit system optimization problem, proposed in this article an improved ant colony algorithm to solve it. Finally, the algorithm is applied to the actual path of the logistics or the passenger flow optimization problem. The results show that the improved algorithm in the theory and practice of feasible.