Glowworm Swarm Optimization for Dispatching System of Public Transit Vehicles
- 354 Downloads
The intelligent schedule of vehicles operation is one of the problems which need to be solved in the dispatching system of public transit vehicles, it relates to the development of the city and civic daily life. In this paper, a transit vehicle scheduling optimization algorithm which balancing between the benefits of bus companies and passengers is proposed. The glowworm swarm optimization (GSO) with random disturbance factor, namely R-GSO is applied to the schedule of vehicles. Finally, we provide some comparisons of R-GSO with artificial fish-swarm algorithm, particle swarm optimization and GSO, the simulation results show R-GSO algorithm has higher efficiency and is an effective way to optimize the public transit vehicle dispatching.
KeywordsPublic transit vehicle dispatching Glowworm swarm optimization (GSO) Random disturbance factor R-GSO
This work is supported by National Science Foundation of China under Grant No. 61165015. Key Project of Guangxi Science Foundation under Grant No. 2012GXNSFDA053028, Key Project of Guangxi High School Science Foundation under Grant No. 20121ZD008 and the Funded by Open Research Fund Program of Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China under Grant No. IPIU01201100.
- 1.Zhang F (2000) Intelligent dispatch for public traffic vehicles and its related technologies. Beihang University Press, BeijingGoogle Scholar
- 2.Bo L, Yao W, Wang Y (2002) The optimum mathematical model on the bus dispatch. J Eng Math 19:67–74Google Scholar
- 3.Ren C, Xun Y, Yin C (2008) Research of bus dispatching based on genetic taboo search algorithm. J Shandong Univ Sci 27(4):53–56Google Scholar
- 4.Yan F, Guang X (2008) Model and algorithm analysis based on the two-tiered programming on bus scheduling. J Landzhou Jiaotong Univ 27(6):75–79Google Scholar
- 7.Krishnanand KN (2007) Glowworm swarm optimization: a multimodal function optimization paradigm with applications to multiple signal source localization tasks. Ph.D thesis, Department of Aerospace Engineering, Indian Institute of ScienceGoogle Scholar
- 9.Krishnanand KN, Ghose D (2009) A glowworm swarm optimization based multi-robot system for signal source localization. Design and control of intelligent robotic systems, studies in computational intelligence, vol 177, pp 49–68Google Scholar
- 10.Krishnan KN, Goose D (2007) chasing multiple mobile signal sources: a glowworm swarm optimization approach. In: 3rd International conference on artificial intelligence, India, pp 54–58Google Scholar
- 11.Fu A, Lei S (2008) Intelligent dispatching of public transit vehicles using particle swarm optimization algorithm. Comput Eng Appl 44(15):239–241Google Scholar
- 12.Xiaolei L, Jixian Q (2001) Artificial fish-swarm algorithm: bottom-up optimization model. Translation annual meeting of Chinese process systems engineering society, pp 76–82Google Scholar
- 13.Xiaolei L, Zhijiang S, Jixian Q (2002) An optimizing method based on autonomous animats: fish-swarm algorithm. Syst Eng Theory Pract 22(11):32–38Google Scholar