Abstract
We propose a new approach for multiobjective shape optimization based on the estimation of probability distributions. The algorithm improves search space exploration by capturing landscape information into the probability distribution of the population. Correlation among design variables is also used for the computation of probability distributions. The algorithm uses finite element method to evaluate objective functions and constraints. We provide several design problems and we show Pareto front examples. The design goals are: minimum weight and minimum nodal displacement, without holes or unconnected elements in the structure.
Keywords
- Pareto Front
- Probability Vector
- True Pareto Front
- Univariate Marginal Distribution Algorithm
- Population Base Incremental Learn
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Chapman, C.D., Saitou, K., Jakiela, M.J.: Genetic algorithms as an approach to configuration and topology design. Jou. Mech. Des. 116, 1005–1011 (1994)
Deb, K., Goell, T.: Multiobjetive Evolutionary Algorithms for Engineering Shape Optimization. KanGal report 200003, Kanpur, India (2000)
Kane, C., Schoenauer, M.: Topological Optimum Design using Genetic Algorithms. In: Control and Cybernetics, 25th edn. (1996)
Li, H., Zhang, Q., Tsang, E.P., Ford, J.A.: Hybrid Estimation of Distribution Algorithm for Multiobjective Knapsack Problem. In: Proceedings of the 4th European Conference on Evolutionary Computation in Combinatorial Optimization, Coimbra, Portugal (2004)
Marroquín, J.L., Velasco, F.A., Rivera, M., Nakamura, M.: Gauss-Markov Measure Field Models for Low-Level Vision. IEEE Trans. On PAMI 23(4), 337–348 (2001)
Mühlenbein, H., PaaB, G.: From reconbination of Genes to the estimation of distributions I. Binary parameters. In: Parallel problem Solving form Nature (PPSN IV), pp. 178–187 (1996)
Baluja, S.: Population Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning School of Computer Science Carnegie Mellon University, Pittsburgh, Pennsylvania 1523. CMU-CS-94-163 (1996)
Pelikan, M., Goldberg, D.E., Paz, C.: Linkage problem, distribution estimation and bayesian networks. IlliGal Report No. 98013 Urbana Il University (1998)
Pelikan, M., Goldberg, D.E., Paz, C.: BOA: The Bayesian Optimization Algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 1999 (1999)
Malvern, L.E.: Introduction to the Mechanics of a Continuous Medium. Pretence Hall Inc., Englewood Cliffs (1969)
Zienkiewicz, O.C., Taylor, R.L.: El Método de los Elementos Finitos, Cuarta Edición, vol. 2. Mc. Graw Hill-CIMNE (1995)
Deb, K., Jain, S.: Running Perfomance Metrics for Evolutionary Multi-Objective Optimization. KanGal report 2002004, Kanpur, India (2000)
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© 2005 Springer-Verlag Berlin Heidelberg
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Peña, S.I.V., Rionda, S.B., Aguirre, A.H. (2005). Multiobjective Shape Optimization Using Estimation Distribution Algorithms and Correlated Information. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_46
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DOI: https://doi.org/10.1007/978-3-540-31880-4_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24983-2
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