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Generic Cabling of Intelligent Buildings Based on Ant Colony Algorithm

Generic Cabling of Intelligent Buildings Based on Ant Colony Algorithm

Yunlong Wang, Kueiming Lo
Copyright: © 2011 |Volume: 3 |Issue: 2 |Pages: 13
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781613509180|DOI: 10.4018/jssci.2011040104
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MLA

Wang, Yunlong, and Kueiming Lo. "Generic Cabling of Intelligent Buildings Based on Ant Colony Algorithm." IJSSCI vol.3, no.2 2011: pp.49-61. http://doi.org/10.4018/jssci.2011040104

APA

Wang, Y. & Lo, K. (2011). Generic Cabling of Intelligent Buildings Based on Ant Colony Algorithm. International Journal of Software Science and Computational Intelligence (IJSSCI), 3(2), 49-61. http://doi.org/10.4018/jssci.2011040104

Chicago

Wang, Yunlong, and Kueiming Lo. "Generic Cabling of Intelligent Buildings Based on Ant Colony Algorithm," International Journal of Software Science and Computational Intelligence (IJSSCI) 3, no.2: 49-61. http://doi.org/10.4018/jssci.2011040104

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Abstract

Generic cabling is a key component for multiplex cable wiring. It is one of the basic foundations of intelligent buildings. Using operation flow in generic cabling, the index constraints affecting generic cabling have been evolved in this paper. A mathematical model is built based on the ant colony algorithm with multiple constraints, and improvements were made on the original basis to extend the ant colony algorithm from the regular simple ant colony and structure to a multi-ant colony and structure. The equilibrium settlement of multiplex wiring is realized according to the introduction of the multi-ant colony model. The ant cycle model is combined to extend the optimization target from the local wiring path to the entire wiring path, and to solve the drawbacks existing in the regular ant colony algorithm and other search algorithms that take the local wiring path as the optimization target. The introduced retrospective algorithm make the ants avoid the path marked “invalid” in the subsequent search process and improves the search performance and convergence speed of the ant colony algorithm.

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