Efficient spark-based framework for solving the traveling salesman problem using a distributed swarm intelligence method

Abstract

Vehicular traffic has become an important research area because of its specific features and applications as road safety and efficient traffic management. Vehicles should be carry enough of communication systems, onboard computing facilities, storage, and increased geographical monitoring. Hence, several technologies have been deployed to promote Vehicular traffic management. Since this work, Ant Colony Optimization (ACO) algorithm that's based on the apache Spark is in parallel to settle the (TSP) Traveling Salesman Problem. To achieve the parallelization of the phase of solution construction in ant colony optimization, a class of ants was encapsulated to a resilient distributed dataset and the corresponding transformation operators were given. The comparison results between MapReduce based ant colony algorithm and the proposed algorithm under the same experimental environment show that the algorithm proposed notably enhance the optimization speed at least ten times than the MapReduce one.