Analysis & modeling multi-breeded Mean-Minded ant colony optimization of agent based Road Vehicle Routing Management

In this paper Road Vehicle Routing Management is being analyzed and modeled considering multi-parameter scheme and a new modified Mean-Minded ant colony optimization (ACO) heuristic is used to optimized the different options that several vehicle system can avail to reach its destination. The model has taken care so that the busy roads are avoided and congestion never arises. The aim of this work is to uniformly distribute the traffic and the movement of vehicles through some selected points is enumerated to see the distribution of vehicles in all paths. Some modification of ant-colony optimization algorithm is made and instead of running one breed of ants, here multi breeds are being initialized to demarcate multi - objective and multi - capacitive vehicles. The pheromone density no longer depends on the number of ants, but is actually a function of the parameters which it is seeking, instead of the traditional pheromone trail function used. So in a nutshell the pheromone evaporation functions will a different one and evaporation criteria will be how much the ant is happy while passing through that road. Analogy can be derived as a road with scattered food of different type and several types of ants are passing, and each time they see food of their liking they eat them and spread pheromone to attract more insects of its types, however that eaten food is refilled and the supply will never end. The results obtained showed that the ACO has been successful up to a certain extent in channeling the traffic in various routes of the system irrespective of its kind and considering the road network as a dynamic system with varying parameters, the vehicle distribution has been near uniform except fluctuations arising due to dynamicity error.