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ISPRS Int. J. Geo-Inf. 2017, 6(6), 161; doi:10.3390/ijgi6060161

A Multiple Ant Colony Optimization Algorithm for Indoor Room Optimal Spatial Allocation

1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100864, China
2
Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Sisi Zlatanova, Kourosh Khoshelham, George Sithole and Wolfgang Kainz
Received: 4 March 2017 / Revised: 17 May 2017 / Accepted: 24 May 2017 / Published: 1 June 2017
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
View Full-Text   |   Download PDF [2085 KB, uploaded 21 June 2017]   |  

Abstract

Indoor room optimal allocation is of great importance in geographic information science (GIS) applications because it can generate effective indoor spatial patterns that improve human behavior and efficiency. However, few research concerning indoor room optimal allocation has been reported. Using an office building as an example, this paper presents an integrative approach for indoor room optimal allocation, which includes an indoor room allocation optimization model, indoor connective map design, and a multiple ant colony optimization (MACO) algorithm. The mathematical optimization model is a minimized model that integrates three types of area-weighted costs while considering the minimal requirements of each department to be allocated. The indoor connective map, which is an essential data input, is abstracted by all floor plan space partitions and connectivity between every two adjacent floors. A MACO algorithm coupled with three strategies, namely, (1) heuristic information, (2) two-colony rules, and (3) local search, is effective in achieving a feasible solution of satisfactory quality within a reasonable computation time. A case study was conducted to validate the proposed approach. The results show that the MACO algorithm with these three strategies outperforms other types of ant colony optimization (ACO), Genetic Algorithm (GA), and particle swarm optimization (PSO) algorithms in quality and stability, which demonstrates that the proposed approach is an effective technique for generating optimal indoor room spatial patterns. View Full-Text
Keywords: indoor GIS; optimal spatial allocation; ant colony optimization indoor GIS; optimal spatial allocation; ant colony optimization
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Yang, L.; Sun, X.; Zhu, A.; Chi, T. A Multiple Ant Colony Optimization Algorithm for Indoor Room Optimal Spatial Allocation. ISPRS Int. J. Geo-Inf. 2017, 6, 161.

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