An improved hybrid ant particle optimization (IHAPO) algorithm for reducing travel time in VANETs

Abstract With the increase in traffic volume day by day, cities are facing the problem of extreme congestion in most of the developing countries. The congestion on the road leads to increase in travel time and travel cost as well as having a significant impact on the health of people. This paper proposes a novel Improved Hybrid Ant Particle Optimization (IHAPO) algorithm for reducing the travel time for enabling smart transportation. The aim of the proposed algorithm is to select a best path in peak hours by avoiding the optimal path, if congested and resuming the optimal path when congestion eases. This algorithm is an improvement of the existing Modified Ant Colony Optimization (MACO) algorithm. It combines both MACO and Particle Swarm Optimization (PSO) algorithms using the global best exchange method. Initially, both algorithms work separately and produce their best solutions. Then a comparison has been made between both the solutions and a new global best solution found for the whole network. According to the best solution obtained, the position of both ants and particles are changed for the next iterations. MACO algorithm works under the assumption that all roads are in working condition, whereas the proposed IHAPO algorithm works under normal road conditions. Another difference between these algorithms is the pheromone update process that makes the new algorithm more effective. The proposed algorithm is tested on a map of North-West Delhi, India using Simulation of Urban MObility (SUMO) for traffic simulation. It was found that the travel time is reduced significantly by using the proposed IHAPO algorithm over the existing algorithms in consideration.