A Parallel Meta-Heuristic Approach to Reduce Vehicle Travel Time in Smart Cities

Applied Sciences

Hector Migallon-Gomis¹  Hector Rico-Garcia²  Jose-Luis Sanchez-Romero²  Antonio Jimeno-Morenilla² 

[1] Department of Computer Engineering, Miguel Hernandez University;Department of Computer Technology, University of Alicante, San Vicente del Raspeig, Alicante 03690, Spain;

关键词: smart cities;    meta-heuristics;    travelling salesman problem;    TLBO;    parallelism;    GPU;   

DOI  :  10.3390/app11020818

来源: DOAJ

【 摘 要 】

The development of the smart city concept and inhabitants’ need to reduce travel time, in addition to society’s awareness of the importance of reducing fuel consumption and respecting the environment, have led to a new approach to the classic travelling salesman problem (TSP) applied to urban environments. This problem can be formulated as “Given a list of geographic points and the distances between each pair of points, what is the shortest possible route that visits each point and returns to the departure point?”. At present, with the development of Internet of Things (IoT) devices and increased capabilities of sensors, a large amount of data and measurements are available, allowing researchers to model accurately the routes to choose. In this work, the aim is to provide a solution to the TSP in smart city environments using a modified version of the metaheuristic optimization algorithm Teacher Learner Based Optimization (TLBO). In addition, to improve performance, the solution is implemented by means of a parallel graphics processing unit (GPU) architecture, specifically a Compute Unified Device Architecture (CUDA) implementation.

【 授权许可】

Unknown