A hybrid multi-objective carpool route optimization technique using genetic algorithm and A* algorithm

Carpooling has gained considerable importance as an effective solution for reducing pollution, mitigation of traffic and congestion on the roads, reduced demand parking facilities, lesser energy fuel consumption most importantly, reduction in carbon emission, thus improving quality life cities. This work presents a hybrid GA-A* algorithm to obtain optimal routes carpooling problem domain multiobjective optimization having multiple conflicting objectives. Though Genetic Algorithm provides solutions, A* because its efficiency providing shortest route between any two points based heuristics, enhances obtained using algorithm. The refined algorithm, are further subjected dominance test non-dominating solutions Pareto-Optimality. maximize profit service provider by minimizing travel detour distance well pick-up/drop costs while maximizing utilization car. proposed been implemented over Salt Lake area Kolkata. Route consistently same number passengers when compared corresponding results from existing Various statistical analysis like boxplots have also confirmed that regularly performed better than only Algorithm.