From nature-inspired computation to swarm intelligence applied to landscaping
DOI:
https://doi.org/10.11606/issn.2359-5361.paam.2024.225968Keywords:
Artificial intelligence, Swarm intelligence, Ant Colony OptimizationAbstract
This study aims to introduce swarm intelligence as a strategy to plan the maintenance of green areas on university campuses, illustrating how this computational tool can contribute to landscaping and architecture. Regarding methodology, the problem was interpreted as discrete, and an analogy to the multiple traveling salesman problem was proposed. The ant colony optimization metaheuristic was applied to solve the problem, and the program AutoCAD was used as a platform to develop the problem-solving plug-in. This study expects a robust application as a result that will prove the potential of using swarm intelligence for green area maintenance planning.
Downloads
References
AUTODESK. API developer’s guide: AutoCAD Civil 3D 2013. [S. l.: s. n.], 2013.
BALL, Philip. Forging patterns and making waves from biology to geology: a commentary on Turing. Philosophical Transactions of the Royal Society B., London, v. 370, n. 1666, 2015. DOI: 10.1098/rstb.2014.0218.
BAR-YAM, Yaneer. Reviews (book & software): complex systems with Herbert Simon. Complexity, v. 5, n. 3, p. 47-48, 1998.
BARBOSA, Denilson; SILVA JR., Calos; KASHIWABARA, André. Aplicação da otimização por colônia de formigas ao problema de múltiplos caixeiros viajantes no atendimento de ordens de serviço nas empresas de distribuição de energia elétrica. In: BRAZILIAN SYMPOSIUM ON INFORMATION SYSTEM, 11., 2015, Goiânia. Anais […]. Goiânia: SBC, 2015. p. 23-30.
BELLMORE, Mandell; HONG, Saman. Transformation of multisalesman problem to the standard traveling salesman problem. Journal of the Association for Computing Machinery, [s. l.], v. 21, n. 33, p. 500-504, 1974. DOI: 10.1145/321832.321847.
BONABEAU, Eric; DORIGO, Marco; THERAULAZ, Guy. Swarm intelligence: from natural to artificial systems. New York: Oxford University Press, 1999.
DENEUBOURG, Jean-Louis et al. The self-organizing exploratory pattern of the Argentine ant. Journal of Insect Behavior, New York, v. 3, n. 2, p. 159-168, 1990. DOI: 10.1007/BF01417909.
DORIGO, Marco; BIRATTARI, Mauro; STÜTZLE, Thomas. Ant colony optimization: artificial ants as a computational intelligence technique. Bruxelas: [s. n.], 2006.
DORIGO, Marco; DI CARO, Gianni. Ant colony optimization: a new meta-heuristic. In: CONFERENCE EVOLUTIONARY COMPUTATION, 99., 1999, New York. Anais […]. New York: IEEE, 1999. p. 1470-1477.
DORIGO, Marco; STÜTZLE, Thomas. Ant colony optimization. Cambridge: MIT Press, 2004.
DORIGO, Marco; STÜTZLE, Thomas. Ant colony optimization: overview and recent advances. Bruxelas: IRIDIA, 2009.
GENDREAU, Michel; POTVIN, Jean-Yves. Handbook of metaheuristics. New York: Springer Science, 2010.
GOSS, S. et al. How trail laying and trail following can solve foraging problems for ant colonies. Behavioural Mechanisms of Food Selection, New York, p. 661-678, 1990. DOI: 10.1007/978-3-642-75118-9_32.
HOLLAND, John. Genetic algorithms and adaptation. In: SELFRIDGE, Oliver. Adaptive control of ill-defined systems. New York: Plenum Press, 1984, cap. 21, p. 317-333.
HOFSTADTER, Douglas. Gödel, Escher, Bach: an eternal golden brain: a metaphorical fugue on minds and machines in the spirit of Lewis Carroll. New York: Basic Books 1979.
JOHNSON, Steven. Emergence: the connected lives of ants, brains, cities and software. Marin: Simon & Schuster, 2012.
JUNJIE, Pan; DINGWEI, Wang. An ant colony optimization algorithm for multiple travelling salesman problem. In: INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING, INFORMATION AND CONTROL, 1., 2006, Beijing. Anais […]. Beijing: IEEE, 2006. p. 1-4.1
KENNEDY, James; EBERHART, Russel; SHI, Yuhui. Swarm intelligence. San Francisco: Morgan Haufmann, 2001.
LANGTON, Christopher. Studying artificial life with cellular automata. Physica D: Nonlinear Phenomena, Amsterdam, v. 22, n. 1-3, p. 120-149, 1986. DOI: 10.1016/0167-2789(86)90237-X.
LU, Li-Chih; YUE, Tai-Wen. Mission-oriented ant-team ACO for min–max MTSP. Applied Soft Computing Journal, Amsterdam, v. 76, p. 436-444, 2019. DOI: 10.1016/j.asoc.2018.11.048.
LUPOAIE, Vlad-Ioan et al. SOM-guided evolutionary search for solving MinMax Multiple-TSP. In: IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, 2019, Wellington. Anais […]. Wellington: IEEE, 2019. p. 73-80.
ROCHA, Bruno; BOLSSONI, Gabriela; BUSSOLOTI, Victor. Ecologias de projeto: métodos e processos em arquitetura digital. In: FÓRUM DE PESQUISA MACKENZIE, 9., 2019, São Paulo. Anais […]. São Paulo: [s. n.], 2019. p. 265-276.
SALAS, Yasel et al. Multi-type ant colony system for solving the multiple traveling salesman problem. Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia, Maracaibo, v. 35, n. 3, p. 311-320, 2012.
SIMON, Herbert. The sciences of the artificial. Cambridge: MIT Press, 1996.
SVESTKA, Joseph; HUCKFELDT, Vaughn. Computational experience with an M-Salesman traveling salesman algorithm. Management Service, Catonsville, v. 19, n. 7, p. 790-799, 1973. DOI: 10.1287/mnsc.19.7.790
TURING, Alan. The prof’s book: Turing’s treatise on the Enigma. [S. l.: s. n.], 1940. Disponível em: https://archive.org/details/hw-25-3. Acesso em: 4 dez. 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 André Barcellos Ferreira, Dr. Jarryer Andrade de Martino
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
A revista Paisagem e Ambiente: ensaios tem licença Creative Commons
Creative Commons - Atribuição-NãoComercial-CompartilhaIgual 4.0 Internacional
DIADORIM - Diretório de Políticas Editoriais
O detentor dos direitos autorais é o autor do artigo. A revista exige apenas o ineditismo na publicação do artigo. O autor tem do direito de divulgar seu artigo conforme sua conveniência.
