Abstract
This paper presents the development of a hybrid approach as a solution to the multiple Traveling Salesman Problem (mTSP) applied to the route scheduling for self-drive cars. First, we use k-means to generate routes that equality distribute delivery locations among the cars. Then, these routes are set as the initial population for bio-inspired algorithms, such as Genetic Algorithm (GA) and Ant Colony System (ACS), that perform an evolutionary process in order to find a route which minimizes the overall distance while keeping the balance of individual tours of each car. The experiments were conducted with our route scheduling system in real and virtual environments. We compared our hybrid approaches using k-means in conjunction with GA and ACS against GA, ACS and Particle Swarm Optimization (PSO) initialized with random population. The results showed that, as the number of cars and target locations increase, the hybrid approaches outperform GA, ACS and PSO without any pre-processing.
Supported by the University of São Paulo, Federal University of Uberlândia, and by the CNPq under Grant 400699/2016-8.
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This work was supported by the University of São Paulo, Federal University of Uberlândia, and by the CNPq under Grant 400699/2016-8.
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Silva, C.E. et al. (2022). Route Scheduling System for Multiple Self-driving Cars Using K-means and Bio-inspired Algorithms. In: Iliadis, L., Jayne, C., Tefas, A., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science, vol 1600. Springer, Cham. https://doi.org/10.1007/978-3-031-08223-8_3
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