Summary
Trusses are common structures that comprise one or more triangular units constructed with straight slender members connected at joints. Outlined in this chapter is a novel scheme for representation of truss geometry and an evolutionary optimization algorithm that operates on the new representation guided by inheritance and learning. Trusses are represented as a set of elements having a collection of properties (e.g. cross-sectional area, type of material). The sets with varying cardinality represent truss structures with different numbers of elements and hence different topologies. The evolutionary algorithm generates topologies by inheriting common elements from the parents and the corresponding element properties are generated via recombination. Depending on the physical problem being solved, specific recombination operators can be designed to aid the optimization process. One such mutation operator is used in this study to reduce the number of elements in an attempt to identify the smallest feasible topology. Another mutation operator is used to perturb the properties of the elements. Algorithm provided in this chapter are useful insights about learning and inheriting topologies and element properties from 3, 5 and 8 parents. A number of case studies are included to highlight the benefits of our proposed set based representation and effects of learning topologies from multiple parents.
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References
Wu, S.J., Chow, P.T.: Steady-state genetic algorithms for discrete optimization of trusses. Computers & Structures 56, 979–991 (1995)
Kaveh, A., Kalatjari, V.: Topology optimization of trusses using genetic algorithm, force method and graph theory. Internationl Journal For Numerical Methods In Engineering 58, 771–791 (2003)
Coello, C.A.C., Rudnick, M., Christiansen, A.D.: Using genetic algorithms for optimal design of trusses, New Orleans, LA, USA, pp. 88–94 (1994)
Deb, K., Gulati, S., Chakrabarti, S.: Optimal truss-structure design using real-coded genetic algorithms, University of Wisconsin, Madison, Wisconsin, USA, pp. 479–486. Morgan Kaufmann, San Francisco (1998)
Deb, K., Gulati, S.: Design of truss-structures for minimum weight using genetic algorithms. Journal of Finite Elements in Analysis and Design 37, 447–465 (2001)
Tang, W.Y., Tong, L.Y., Gu, Y.X.: Improved genetic algorithm for design optimization of truss structures with sizing, shape and topology variables. International Journal for Numerical Methods in Engineering 62, 1737–1762 (2005)
Rajeev, S., Krishnamoorhty, C.S.: Genetic algorithms-based methodologies for design optimization of trusses. Journal of Structural Engineering 123, 350–358 (1997)
Goldberg, D.E., Korb, B., Deb, K.: Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems 3, 493–530 (1989)
Isaacs, A., Ray, T., Smith, W.: Novel evolutionary algorithm with set representation scheme for truss design. In: Proceedings of IEEE Congress on Evolutionary Computation 2007 (CEC 2007), Singapore, pp. 3902–3908 (2007)
Chen, Y.p., Yu, T.L., Sastry, K., Goldberg, D.E.: A survey of linkage learning techniques in genetic and evolutionary algorithms. Technical Report IlliGAL Report No. 2007014, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Chanpaign (2007)
Harik, G.R., Goldberg, D.E.: Learning linkage. Technical Report IlliGAL Report No. 96006, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Chanpaign (1996)
Goldberg, D.E., Deb, K., Kargupta, H., Harik, G.: Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. In: International Conference on Genetic Algorithms, pp. 56–64. Morgan Kaufmann, San Mateo (1993)
Kargupta, H.: The gene expression messy genetic algorithm. In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, pp. 814–819 (1996)
Larranaga, P., Lozano, J.A.: Estimation of Distribution Algorithms. Kluwer Academic Publishers, Dordrecht (2002)
Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter optimization. Evolutionary Computation 10, 371–395 (2002)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley and Sons Pvt. Ltd., Chichester (2001)
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Isaacs, A., Ray, T., Smith, W. (2008). Set Representation and Multi-parent Learning within an Evolutionary Algorithm for Optimal Design of Trusses. In: Chen, Yp., Lim, MH. (eds) Linkage in Evolutionary Computation. Studies in Computational Intelligence, vol 157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85068-7_17
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DOI: https://doi.org/10.1007/978-3-540-85068-7_17
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