Multimodal Truss Structure Design Using Bilevel and Niching Based Evolutionary Algorithms

Islam, M, Li, X and Deb, K 2017, 'Multimodal Truss Structure Design Using Bilevel and Niching Based Evolutionary Algorithms', in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, Germany, 15-19 July 2017, pp. 274-281.


Document type: Conference Paper
Collection: Conference Papers

Title Multimodal Truss Structure Design Using Bilevel and Niching Based Evolutionary Algorithms
Author(s) Islam, M
Li, X
Deb, K
Year 2017
Conference name GECCO 2017
Conference location Berlin, Germany
Conference dates 15-19 July 2017
Proceedings title Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017)
Publisher Association for Computing Machinery
Place of publication New York, United States
Start page 274
End page 281
Total pages 8
Abstract Finding an optimal design for a truss structure involves optimizing its topology, size, and shape. A truss design problem is usually multimodal, meaning that the problem offers multiple optimal designs in terms of topology and/or size of the members, but they are evaluated to have similar or equally good objective function values. From a practical standpoint, it is desirable to find as many alternative designs as possible, rather than finding a single design, as often practiced. A few metaheuristics based methods with niching techniques have been used for finding multiple topologies for the truss design problem, but these studies have ignored any emphasis in finding multiple solutions in terms of size. To overcome this issue, this paper proposes to formulate the truss problem as a bilevel optimization problem, where stable topologies can be found in the upper level and the optimized sizes of the members of these topologies can be found in the lower level. As a result, a new bilevel niching method is proposed to find multiple optimal solutions for topology level as well as for the size level simultaneously. The proposed method is shown to be superior over the state-of-the-art methods on several benchmark truss-structure design problems.
Subjects Optimisation
Neural, Evolutionary and Fuzzy Computation
Keyword(s) Structural optimization
meta-heuristics
bi-level optimization
DOI - identifier 10.1145/3071178.3071251
Copyright notice © 2017 ACM
ISBN 9781450349208
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