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2017 | OriginalPaper | Buchkapitel

A Comparison of Heuristic Algorithms for Bus Dispatch

verfasst von : Hong Wang, Lulu Zuo, Jia Liu, Chen Yang, Ya Li, Jaejong Baek

Erschienen in: Advances in Swarm Intelligence

Verlag: Springer International Publishing

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Abstract

Bus dispatch (BD) system plays an essential role to ensure the efficiency of public transportation, which has been frequently addressed by the heuristic algorithms. In this paper, five well-exploited heuristic algorithms, i.e. Genetic algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony algorithm (ABC), Bacterial Foraging Optimization (BFO) and Differential Evolution algorithm (DE), are employed and compared for solving the problem of BD. The comparison results indicate that DE is the best method in dealing with the problem of BD in terms of mean, minimum, and maximum, while BFO obtains the minor lower value of standard deviation and achieves the similar convergence speed in comparison to DE. The performance of PSO seems to outperform the remaining two algorithms (i.e. ABC and GA) in most cases. However, among five algorithms, GA achieves the worst results in terms of the weight estimated objective (i.e. number of departures and average waiting time).

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Literatur
1.
Zurück zum Zitat Wei, M., Jin, W., Sun, B.: Model and algorithm for regional bus scheduling with stochastic travel time. J. Highw. Transp. Res. Dev. 28(10), 124–129 (2011) Wei, M., Jin, W., Sun, B.: Model and algorithm for regional bus scheduling with stochastic travel time. J. Highw. Transp. Res. Dev. 28(10), 124–129 (2011)
2.
Zurück zum Zitat Zhang, R.H., Jia, J.M.: Genetic algorithm’s application in bus dispatch optimization. In: International Conference of Chinese Transportation Professionals, pp. 137–146 (2011) Zhang, R.H., Jia, J.M.: Genetic algorithm’s application in bus dispatch optimization. In: International Conference of Chinese Transportation Professionals, pp. 137–146 (2011)
3.
Zurück zum Zitat Wang, M., Wang, K.: Study on bus scheduling based on particle swarm optimization. Inf. Technol. 12, 111–113 (2009) Wang, M., Wang, K.: Study on bus scheduling based on particle swarm optimization. Inf. Technol. 12, 111–113 (2009)
4.
Zurück zum Zitat Wei, Z., Zhao, X., Wang, K., et al.: Bus dispatching interval optimization based on adaptive bacteria foraging algorithm. Math. Prob. Eng. 2012(3), 1 (2012) Wei, Z., Zhao, X., Wang, K., et al.: Bus dispatching interval optimization based on adaptive bacteria foraging algorithm. Math. Prob. Eng. 2012(3), 1 (2012)
5.
Zurück zum Zitat Liu, Q.: Differential evolution bacteria foraging optimization algorithm for bus scheduling problem. J. Transp. Syst. Eng. Inf. Technol. 12(2), 156–161 (2012) Liu, Q.: Differential evolution bacteria foraging optimization algorithm for bus scheduling problem. J. Transp. Syst. Eng. Inf. Technol. 12(2), 156–161 (2012)
6.
Zurück zum Zitat Fang, Z.X.: Research of bus scheduling optimization based on chemokine guide BFO algorithm. Doctoral dissertation, Northeastern University (2013). (in Chinese) Fang, Z.X.: Research of bus scheduling optimization based on chemokine guide BFO algorithm. Doctoral dissertation, Northeastern University (2013). (in Chinese)
7.
Zurück zum Zitat Ding, Y., Jiang, F., Wu, Y.Y.: Application of genetic algorithm in public transportation scheduling. Comput. Sci. 43(S2), 601–603 (2016) Ding, Y., Jiang, F., Wu, Y.Y.: Application of genetic algorithm in public transportation scheduling. Comput. Sci. 43(S2), 601–603 (2016)
8.
Zurück zum Zitat Holand, J.H.: Adaption in natural and artificial systems. Control Artif. Intell. 6(2), 126–137 (1975). University of Michigan Press Holand, J.H.: Adaption in natural and artificial systems. Control Artif. Intell. 6(2), 126–137 (1975). University of Michigan Press
9.
Zurück zum Zitat Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Piscataway, pp. 1942–1948 (1995) Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Piscataway, pp. 1942–1948 (1995)
10.
Zurück zum Zitat Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Engineering Faculty, Computer Engineering Department, Erciyes University, Technical report - TR06 (2005) Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Engineering Faculty, Computer Engineering Department, Erciyes University, Technical report - TR06 (2005)
11.
Zurück zum Zitat Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)MathSciNetCrossRefMATH Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)MathSciNetCrossRefMATH
12.
Zurück zum Zitat Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)CrossRef Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)CrossRef
13.
Zurück zum Zitat Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefMATH Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefMATH
14.
Zurück zum Zitat Niu, B., Wang, J., Wang, H.: Bacterial-inspired algorithms for solving constrained optimization problems. Neurocomputing 148, 54–62 (2015)CrossRef Niu, B., Wang, J., Wang, H.: Bacterial-inspired algorithms for solving constrained optimization problems. Neurocomputing 148, 54–62 (2015)CrossRef
15.
Zurück zum Zitat El-Abd, M.: Performance assessment of foraging algorithms vs evolutionary algorithms. Inf. Sci. 182(1), 243–263 (2012)MathSciNetCrossRef El-Abd, M.: Performance assessment of foraging algorithms vs evolutionary algorithms. Inf. Sci. 182(1), 243–263 (2012)MathSciNetCrossRef
Metadaten
Titel
A Comparison of Heuristic Algorithms for Bus Dispatch
verfasst von
Hong Wang
Lulu Zuo
Jia Liu
Chen Yang
Ya Li
Jaejong Baek
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-61833-3_54

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