A novel heuristic algorithm for minimum compliance topology optimization

• Autorzy: Bochenek B. , Mazur M.
• Języki publikacji: EN

Abstrakty

EN

The implementation of efficient and versatile methods to the generation of optimal topologies for engineering structural elements is one of the most important issues stimulating progress within the structural topology optimization area. Over the years, optimization problems have been typically solved by the use of classical gradient-based mathematical programming algorithms. Nowadays, these traditional techniques are more often replaced by other algorithms, usually by the ones based on heuristic rules. Heuristic optimization techniques are gaining widespread popularity among researchers because they are easy to implement numerically, do not require gradient information, and one can easily combine this type of algorithm with any finite element structural analysis code. In this paper, a novel heuristic algorithm for a minimum compliance topology optimization is proposed. Its effectiveness is illustrated by the results of numerical generation of optimal topologies for selected plane structures.

Słowa kluczowe

EN

topology optimization; heuristic algorithm; mathematical programming algorithm

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Engineering Transactions
• Rocznik: 2016
• Tom: Vol. 64, iss. 4
• Strony: 541--546
• Opis fizyczny: Bibliogr. 9 poz., rys.

Twórcy

autor

Bochenek B.

• Institute of Applied Mechanics Cracow University of Technology Jana Pawla II 37, 31-864 Kraków, Poland

autor

Mazur M.

• Institute of Applied Mechanics Cracow University of Technology Jana Pawla II 37, 31-864 Kraków, Poland

Bibliografia

• 1. Andreassen E., Clausen A., Schvenels M., Lazarov B.S., Sigmund O., Efficient topology optimization in MatLab using 88 lines of code, Structural and Multidisciplinary Optimization, 43(1): 1–16, 2011, doi: 10.1007/s00158-010-0594-7.
• 2. Bendsoe M.P., Kikuchi N., Generating optimal topologies in optimal design using a homogenization method, Computer Methods in Applied Mechanics and Engineering, 71(2): 197–224, 1988, doi: 10.1016/0045-7825(88)90086-2.
• 3. Bendsoe M.P., Sigmund O., Topology optimization – theory, methods and applications, Springer, Berlin, Heidelberg, New York, 2004.
• 4. Bochenek B., Tajs-Zielińska K., Novel local rules of Cellular Automata applied to topology and size optimization, Engineering Optimization, 44(1): 23–35, 2012, doi: 10.1080/0305215X.2011.561843.
• 5. Deaton J.D., Grandhi R.V., A survey of structural and multidisciplinary continuum topology optimization: post 2000, Structural and Multidisciplinary Optimization, 49(1): 1–38, 2014, doi: 10.1007/s00158-013-0956-z.
• 6. Rozvany G.I.N., A critical review of established methods of structural topology optimization, Structural and Multidisciplinary Optimization, 37(3): 217–237, 2008, doi: 10.1007/s00158-007-0217-0.
• 7. Sigmund O., Maute K., Topology optimization approaches, Structural and Multidisciplinary Optimization, 48(6): 1031–1055, 2013, doi: 10.1007/s00158-013-0978-6.
• 8. Sigmund O., A 99 line topology optimization code written in Matlab, Structural and Multidisciplinary Optimization, 21(2): 120–127, 2001, doi: 10.1007/s001580050176.
• 9. Xing B., Gao W.-J., Innovative computational intelligence: a rough guide to 134 clever algorithms, Springer, 2014.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).