Evolutionary Multi-objective Optimization for Positioning of Residential Houses
Authors: Ayman El Ansary, Mohamed Shalaby
Abstract:
The current study describes a multi-objective optimization technique for positioning of houses in a residential neighborhood. The main task is the placement of residential houses in a favorable configuration satisfying a number of objectives. Solving the house layout problem is a challenging task. It requires an iterative approach to satisfy design requirements (e.g. energy efficiency, skyview, daylight, roads network, visual privacy, and clear access to favorite views). These design requirements vary from one project to another based on location and client preferences. In the Gulf region, the most important socio-cultural factor is the visual privacy in indoor space. Hence, most of the residential houses in this region are surrounded by high fences to provide privacy, which has a direct impact on other requirements (e.g. daylight and direction to favorite views). This investigation introduces a novel technique to optimally locate and orient residential buildings to satisfy a set of design requirements. The developed technique explores the search space for possible solutions. This study considers two dimensional house planning problems. However, it can be extended to solve three dimensional cases.
Keywords: Evolutionary optimization, Houses planning, Urban modeling, Daylight, Visual Privacy, Residential compounds.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332362
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[1] I.C. Yeh, Construction-site layout using annealed neural network, Journal of Computing in Civil Engineering, ASCE 9, 1995, pp. 201-208.
[2] H. Li, P.E. Love, Site level facilities using genetic algorithms, Journal of Computing in Civil Engineering, ASCE 12, 1998, pp. 227-231.
[3] T. Hegazy , E. Elbeltagi, EvoSite: evolution based model for site layout planning, Journal of Computing in Civil Engineering, ASCE 13, 1999, pp. 198-206.
[4] H.M. Osman, M.E. Georgy, M.E. Ibrahim, A hybrid CAD-based con-struction site layout planning system using genetic algorithms Journal of Automation in Construction, 12 (2003) 749-764.
[5] W. Wang , R. Zmeureanu, H. Rivard, Applying multi-objective algorithms in green building design optimization Building and Environment, 2005; 40: 1512-25.
[6] D. Tuhus-Dubrow, M. Krarti, Genetic-algorithm based approach to op-timize building envelope design for residential buildings, Building and Environment, 45: 2010, 1574-1581.
[7] S. Sariyildiz, M.S. Bittermann, O. Ciftcioglu, Multi-objective optimiza-tion in the construction industry, in: Proceeding of AEC 2008 Interna-tional Conference, Antalya, Turkey, 2008.
[8] H. Akbari, M. Morsy, N. Al-Bahama, Electricity saving potentials in the residential sector of Bahrain, Vol. 1. Lawrence Berkely National Laboratory Publication; 1996, pp. 11-20.
[9] 0. Alnatheer, Environmental benefits of energy effeciency and renewable energy in Saudi Arabia's electric sector, Energy Policy; 2006, 34(1):2-10.
[10] R. Hyde, Climate responsive design: a study of buildings in moderate and hot humid climates, London: E. F.N. Spon (2000)
[11] R. Orti, S. Riviere, F. Durand, and C. Puech, Radiosity for dynamic scenes in flatland with the visibility complex, Computer Graphics Forum, 1996, 15(3):237-248.
[12] D. Wolpert and W. MacReady, No free luch theorems for optimization. IEEE Trans. on Evolutionary Computing., (1997).
[13] I. Parmee, Evolutionary and adaptive computing in engineering design, Springer-Verlag, 1st edition, (2001).
[14] DE. Goldberg, Real coded genetic algorithms, virtual alphabets, and blocking, Complex Systems; 1991, 5(2): 139-68