A Hybrid PSO-GA Approach to Solve Vehicle Routing Problem

The Vehicle Routing Problem can be expressed as the problem of designing optimal collection or delivery routes from one or multiple depots to a number of terrestrially scattered customers or cities, subject to side constraints such as time, capacity, mileage etc. The VRP plays a key role in the fields of logistics and transportation. There exist a number of variants of VRPs. Mostly VRPs with fixed demands are considered as compared to uncertainty in demands. In this paper meta-heuristic approaches for VRP, namely: Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are described. These algorithms have some limitations, like pre convergence and local optimization. Thus a new hybrid PSOGA algorithm is proposed in this paper where properties of PSO and GA are integrated to deal with VRP. In the proposed algorithm selection procedure is based on PSO and population is updated using crossover and mutation operators of GA. The result obtained from both the standard PSO and the proposed algorithm has been compared. It is analyzed that the hybrid PSO-GA seems to better as compared to PSO to find the optimal route for given vehicle routing problem. Index Terms Optimization, Vehicle Routing Problem, PSO, GA, Hybrid PSO-GA Methodology. ________________________________________________________________________________________________________