A Meta-Heuristic Routing Optimization Framework for Reducing Traveling Time in Smart Cities

Abstract

Increasing of cities number and urban population has created huge impact on smart cities, intelligent tourism and intelligent logistics. We propose a meta-heuristic routing optimization framework (MROF) based on flower pollination algorithm (FPA) to obtain the planned path with the shortest traveling time. Firstly, we design an iteration-based Gaussian selection method to balance the search capability between global exploration and local exploitation. Then, we design two selection strategies based on Lévy flight and Cauchy distribution to select neighboring structures, in which both self-pollination and cross-pollination require suitable updating strategies to improve search ability. Finally, the tabu strategy, to increase the proposed framework’s search ability, is integrated into the proposed method. The experimental results show that the proposed method outperforms the state-of-the-art algorithms significantly.