Elevator group control system using genetic network programming with ACO considering transitions

Lu Yu; Jin Zhou; Shingo Mabu; Kotaro Hirasawa; Jinglu Hu; Sandor Markon

doi:10.1109/SICE.2007.4421189

Elevator group control system using genetic network programming with ACO considering transitions

Lu Yu^*, Jin Zhou, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu, Sandor Markon

^*Corresponding author for this work

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Citations (Scopus)

Abstract

Genetic Programming Network (GNP), a graph-based evolutionary method, has been proposed several years ago as an extension of Genetic Algorithm (GA) and Genetic Programming (GP). The behavior of GNP is characterized by a balance between exploitation and exploration. To improve the evolving speed and efficiency of GNP, we developed a hybrid algorithm that combines GNP with Ant Colony Optimization (ACO). Pheromone information in the algorithm is updated not only by the fitness but also the frequency of the transitions as dynamic updating. We applied the hybrid algorithm to Elevator Group Supervisory Control Systems (EGSCS), a complex real-world problem. Finally, the simulations verified the efficacy of our proposed method.

Original language	English
Title of host publication	SICE Annual Conference, SICE 2007
Pages	1330-1336
Number of pages	7
DOIs	https://doi.org/10.1109/SICE.2007.4421189
Publication status	Published - 2007 Dec 1
Event	SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan Duration: 2007 Sept 17 → 2007 Sept 20

Publication series

Name	Proceedings of the SICE Annual Conference

Conference

Conference	SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
Country/Territory	Japan
City	Takamatsu
Period	07/9/17 → 07/9/20

Keywords

Ant colony optimization
Elevator group supervisory control system
Genetic network programming
Hybrid algorithm

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/SICE.2007.4421189

Cite this

Yu, L, Zhou, J, Mabu, S, Hirasawa, K, Hu, J & Markon, S 2007, Elevator group control system using genetic network programming with ACO considering transitions. in SICE Annual Conference, SICE 2007., 4421189, Proceedings of the SICE Annual Conference, pp. 1330-1336, SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007, Takamatsu, Japan, 07/9/17. https://doi.org/10.1109/SICE.2007.4421189

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abstract = "Genetic Programming Network (GNP), a graph-based evolutionary method, has been proposed several years ago as an extension of Genetic Algorithm (GA) and Genetic Programming (GP). The behavior of GNP is characterized by a balance between exploitation and exploration. To improve the evolving speed and efficiency of GNP, we developed a hybrid algorithm that combines GNP with Ant Colony Optimization (ACO). Pheromone information in the algorithm is updated not only by the fitness but also the frequency of the transitions as dynamic updating. We applied the hybrid algorithm to Elevator Group Supervisory Control Systems (EGSCS), a complex real-world problem. Finally, the simulations verified the efficacy of our proposed method.",

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author = "Lu Yu and Jin Zhou and Shingo Mabu and Kotaro Hirasawa and Jinglu Hu and Sandor Markon",

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AU - Yu, Lu

AU - Zhou, Jin

AU - Mabu, Shingo

AU - Hirasawa, Kotaro

AU - Hu, Jinglu

AU - Markon, Sandor

PY - 2007/12/1

Y1 - 2007/12/1

N2 - Genetic Programming Network (GNP), a graph-based evolutionary method, has been proposed several years ago as an extension of Genetic Algorithm (GA) and Genetic Programming (GP). The behavior of GNP is characterized by a balance between exploitation and exploration. To improve the evolving speed and efficiency of GNP, we developed a hybrid algorithm that combines GNP with Ant Colony Optimization (ACO). Pheromone information in the algorithm is updated not only by the fitness but also the frequency of the transitions as dynamic updating. We applied the hybrid algorithm to Elevator Group Supervisory Control Systems (EGSCS), a complex real-world problem. Finally, the simulations verified the efficacy of our proposed method.

AB - Genetic Programming Network (GNP), a graph-based evolutionary method, has been proposed several years ago as an extension of Genetic Algorithm (GA) and Genetic Programming (GP). The behavior of GNP is characterized by a balance between exploitation and exploration. To improve the evolving speed and efficiency of GNP, we developed a hybrid algorithm that combines GNP with Ant Colony Optimization (ACO). Pheromone information in the algorithm is updated not only by the fitness but also the frequency of the transitions as dynamic updating. We applied the hybrid algorithm to Elevator Group Supervisory Control Systems (EGSCS), a complex real-world problem. Finally, the simulations verified the efficacy of our proposed method.

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KW - Hybrid algorithm

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Elevator group control system using genetic network programming with ACO considering transitions

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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