@inproceedings{23505a5d16ed4b3cb8ab89050b37c74c,
title = "Path planning of tourist scenic area based on variable dimension particle swarm optimization",
abstract = "Tourists sometimes can only experience part of the scenic spots due to time limitation. Thus, these spots should be chosen and a path needs to be found for the tourists. However, the Particle Swarm Optimization (PSO) is on a fixed dimension and it cannot meet the above requirements. Therefore, in this paper we propose the Variable Dimension PSO (VDPSO) to plan the path. The traditional updating methods of the velocity vector and the position vector are not suitable for the discrete domain in PSO, so they are replaced by the crossover and the mutation operations. Our algorithm will redistribute the particles from low dimensions to high dimensions, which can enhance the ability to search in the high dimensional particle space. Experiments on the Summer Palace data demonstrate effectiveness of the proposed algorithm.",
keywords = "Crossover, Mutation, Particle swarm optimization (PSO), Path planning, Redistribute, Scenic area, Variable dimension",
author = "Yunyang Liu and Quanyu Wang and Haoyu Wang and Junjie Wang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017 ; Conference date: 08-09-2017 Through 11-09-2017",
year = "2017",
month = dec,
day = "4",
doi = "10.1109/CIAPP.2017.8167203",
language = "English",
series = "2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "177--181",
booktitle = "2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017",
address = "United States",
}