| id |
ijac202220206 |
| authors |
Khalil, Randa; Ahmed El-Kordy; Hesham Sobh |
| year |
2022 |
| title |
A review for using swarm intelligence in architectural engineering |
| source |
International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 254–276 |
| summary |
Swarm intelligence algorithms are natural-inspired computational methods that mimic the social interactionbetween creatures to solve certain problems. Swarmative computational architecture (SCA) is a novelnomenclature proposed by the authors to present the use of various swarm algorithms in solving architectural problems. It includes three main aspects: form generation/adaptation, performance evaluation, andoptimization. This study provides a systematic review and comparative analysis for the major publicationswithin the review scope. The correspondence between dynamic subjects and the objective functions for theoptimization process is presented. Particularly, dynamic subjects such as building formation parameters andobjective functions such as occupant comfort and energy consumption. The main results and criteria arecategorized into the design approach, case study, form generation/adaptation, and performance evaluation/optimization. Finally, this review presents the current trends and highlights the gaps in the use of swarmalgorithms to solve architectural engineering problems |
| keywords |
Swarm intelligence, evolutionary algorithm, performative computational architecture, architectural design,building design, computational optimization |
| series |
journal |
| references |
Content-type: text/plain
|
Agirbas A. (2019)
 Façade form-finding with swarm intelligence
, Autom Constr 2019; 99: 140–151
|
|
|
|
|
Azhar S. (2011)
Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry
, ASCE 2011; 11: 241–252.
|
|
|
|
|
Bamdad K., Cholette M., E., Guan L., et al. (2017)
Ant colony algorithm for building energy optimisation problems and comparison with benchmark algorithms
, Energy Build 2017; 154: 404–414
|
|
|
|
|
Bonabeau E., Theraulaz G. V (1999)
Swarm intelligence: From natural to artificial systems
, 1 edition. New York, NY: Oxford University Press, U.S.A., 1999.
|
|
|
|
|
Bucking S., Zmeureanu R. and Athienitis A. (2013)
An information driven hybrid evolutionary algorithm for optimal design of a net zero energy house
, Solar Energy 2013; 96: 128–139
|
|
|
|
|
Cacabelos A., Egu´ia P., Febrero L., et al (2017)
Development of a new multi-stage building energy model calibration methodology and validation in a public library
, Energy and Buildings 2017; 146: 182–199
|
|
|
|
|
Camazine S. (1991)
Self-organizing pattern formation on the combs of honey bee colonies
, Behav Ecol Sociobiol 1991; 28(1): 61–76
|
|
|
|
|
Carlucci S., Pagliano L. and Zangheri P. (2013)
Optimization by discomfort minimization for designing a comfortable net zero energy building in the mediterranean climate
, Adv Mater Res 2013; 689: 44–48
|
|
|
|
|
Chu K. (2006)
Metaphysics of genetic architecture and computation
, Architectural Des 2006; 76(4): 38–45
|
|
|
|
|
Delgarm N., Sajadi B., Delgarm S., et al (2016)
A novel approach for the simulation-based optimization of the buildings energy consumption using NSGA-II: Case study in Iran
, Energy Build 2016; 127: 552–560
|
|
|
|
|
Delgarm N., Sajadi B., Kowsary F., et al (2016)
Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO)
, Appl Energ 2016; 170: 293–303
|
|
|
|
|
Dorigo M. and Stützle T. (2010)
Ant colony optimization: Overview and recent advances
, Handbook of Metaheuristics. Boston, MA: Springer US, 2010 Vol. 146, pp. 227–263
|
|
|
|
|
d’Inverno M., Luck M. and Luck M. M. (2004)
Understanding agent systems
, Heidelberg, Germany: Springer Science & Business Media, 2004.
|
|
|
|
|
Eberhart R. and Kennedy J. (1995)
A new optimizer using particle swarm theory
, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 04–06 October 1995, New York, NW: IEEE, 2014, pp. 39–43
|
|
|
|
|
Ekici B., Cubukcuoglu C., Turrin M., et al (2019)
Performative computational architecture using swarm and evolutionary optimisation: A review
, Building Environ 2019; 147: 356–371
|
|
|
|
|
Ferrara M., Fabrizio E., Virgone J., et al (2014)
A simulation-based optimization method for cost-optimal analysis of nearly zero energy buildings
, Energy Build 2014; 84: 442–457
|
|
|
|
|
Ferrara M., Fabrizio E., Virgone J., et al (2016)
Energy systems in cost-optimized design of nearly zero-energy buildings
, Automation in Construction 2016; 70: 109–127, Khalil et al 271
|
|
|
|
|
Ferrara M., Sirombo E., and Fabrizio E. (2018)
Automated optimization for the integrated design process: the energy, thermal and visual comfort nexus
, Energy Build 2018; 168: 413–427
|
|
|
|
|
Futrell B. J., Ozelkan E. C., and Brentrup D., (2015)
Optimizing complex building design for annual daylighting performance and evaluation of optimization algorithms
, Energy and Buildings 2015; 92: 234–245
|
|
|
|
|
Futrell B. J., Ozelkan E. C., and Brentrup D., (2015)
Bi-objective optimization of building enclosure design for thermal and lighting performance
, Building Environ 2015; 92; 591–602
|
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| last changed |
2024/04/17 14:29 |
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