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Sensors 2015, 15(9), 21033-21053; doi:10.3390/s150921033

A Combination of Genetic Algorithm and Particle Swarm Optimization for Vehicle Routing Problem with Time Windows

Research Center of Government GIS, Chinese Academy of Surveying and Mapping, 28 Lianhuachi West Road, Haidian District, Beijing 100830, China
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Academic Editor: Vittorio M. N. Passaro
Received: 9 February 2015 / Revised: 22 July 2015 / Accepted: 24 August 2015 / Published: 27 August 2015
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Abstract

A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following. View Full-Text
Keywords: vehicle routing problem; VRPTW; particle swarm optimization; genetic vehicle routing problem; VRPTW; particle swarm optimization; genetic
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MDPI and ACS Style

Xu, S.-H.; Liu, J.-P.; Zhang, F.-H.; Wang, L.; Sun, L.-J. A Combination of Genetic Algorithm and Particle Swarm Optimization for Vehicle Routing Problem with Time Windows. Sensors 2015, 15, 21033-21053.

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