全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Multiobjective Design Optimization of Grillage Systems according to LRFD-AISC

DOI: 10.1155/2011/932871

Full-Text   Cite this paper   Add to My Lib

Abstract:

Both the entire weight and joint displacements of grid structures are minimized at the same time in this study. Four multiobjective optimization algorithms, NSGAII, SPEAII, PESAII, and AbYSS are employed to perform computational procedures related to optimization processes. The design constraints related to serviceability and ultimate strength of grid structure are implemented from Load and Resistance Factor Design-American Institute of Steel Constructions (LRFD-AISC Ver.13). Hence, while the computational performances of these four optimization algorithms are compared using different combinations of optimizer-related parameters, the various strengths of grid members are also evaluated. For this purpose, multiobjective optimization algorithms (MOAs) employed are applied to the design optimization of three application examples and achieved to generate various optimal designations using different combinations of optimizer-related parameters. According to assessment of these optimal designations considering various quality indicators, IGD, HV, and spread, AbYSSS shows a better performance comparatively to the other three proposed MOAs, NSGAII, SPEAII, and PESAII. 1. Introduction The grillage systems utilized in different structures like bridge or ship decks, building floors and space buildings, and so forth, contain traverse and longitudinal beams, which are made of available steel profiles with different cross-sections. The optimal selection of steel cross-sections from a discrete set of practically available steel profiles provides a big contribution to constructing cost of a grid structure. Therefore, either weight of grid structure or deflection of its joints is minimized according to certain design limitations prescribed by any code of practice, such as LRFD. During the design optimization of grillage systems, designer is frequently faced with a problem related to making a decision about determination of the most appropriate one between these two conflicting and commensurable objective functions. Although a displacement-related constraint is imposed as a (max span/300) according to the provisions of LRFD-AISC specification, the safety margin on displacement constraint is large when taking into account the grid structures with higher sensitivity against displacement, such as ship decks and floors of industrial buildings which bears special machines required an horizontally balanced position for a regular work. This task has been easily overcome in a way of introducing the concept of multiobjective optimization to the design applications of grid

References

[1]  J. D. Schaffer, Multiple objective optimization with vector evaluated genetic algorithms, Ph.D. thesis, Vanderbilt University, 1984.
[2]  I. Das and J. E. Dennis, “A closer look at drawbacks of minimizing weighted sums of objectives for pareto set generation in multicriteria optimization problems,” Structural Optimization, vol. 14, no. 1, pp. 63–69, 1997.
[3]  A. Hertz, B. Jaumard, C. Ribeiro, and W. F. Filho, “A multi-criteria tabu search approach to cell formation problems in group technology with multiple objectives,” RAIRO—Operations Research, vol. 28, no. 3, pp. 303–328, 1994.
[4]  D. E. Goldberg, Genetic Algorithms in Search. Optimization and Machine Learning, Addison-Wesley Publishing, Massachusetts, Mass, USA, 1989.
[5]  N. Srinivas and K. Deb, “Multiobjective optimization using nondominated sorting in genetic algorithms,” Evolutionary Computation, vol. 2, no. 3, pp. 221–248, 1994.
[6]  J. N. Horn, A. L. Nafpliotis, and D. E. Goldberg, “A niched Pareto genetic algorithm for multiobjective optimization,” in Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, pp. 82–87, IEEE Service Center, Piscataway, NJ, USA, Jun 1994.
[7]  C. M. Fonseca and F. J. Fleming, “Genetic algorithms for multiobjective optimization: formulation, discussion and generalization,” in Proceedings of the Fifth International Conference on Genetic Algorithms, S. Forrest, Ed., pp. 416–423, Morgan Kauffman, San Mateo, Calif, USA, June 1993.
[8]  M. Tanaka and T. Tanino, “Global optimization by the genetic algorithm in a multiobjective decision support system,” in Proceedings of the 10th International Conference on Multiple Criteria Decision Making, pp. 261–270, Taipei, China, July 1992.
[9]  K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, Chichester, UK, 2001.
[10]  E. Zitzler and L. Thiele, “Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp. 257–271, 1999.
[11]  J. D. Knowles and D. W. Corne, “Approximating the nondominated front using the Pareto archived evolution strategy,” Evolutionary Computation, vol. 8, no. 2, pp. 149–172, 2000.
[12]  K. Deb and T. Goel, “Controlled elitist non-dominated sorting genetic algorithms for better convergence,” Lecture Notes in Computer Science, vol. 1993, pp. 67–81, 2001.
[13]  S. S. Rao, V. B. Venkayya, and N. S. Khot, “Optimization of actively controlled structures using goal programming techniques,” International Journal for Numerical Methods in Engineering, vol. 26, no. 1, pp. 183–197, 1988.
[14]  J. P. Ignizio, Goal Programming and Extensions, Heath, Boston, Mass, USA, 1976.
[15]  S. S. Rao and T. I. Freiheit, “Modified game theory approach to multiobjective optimization,” Journal of Mechanisms, Transmissions, and Automation in Design, vol. 113, no. 3, pp. 286–291, 1991.
[16]  M. Sunar and R. Kahraman, “A comparative study of multiobjective optimization methods in structural design,” Turkish Journal of Engineering and Environmental Sciences, vol. 25, no. 2, pp. 69–78, 2001.
[17]  J. Koski, “Defectiveness of weighting method in multicriterion optimization of structures,” Communications in Numerical Methods in Engineering, vol. 1, no. 6, pp. 333–337, 1985.
[18]  I. Das and J. E. Dennis, “A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems,” Structural Optimization, vol. 14, no. 1, pp. 63–69, 1997.
[19]  A. Messac and C. A. Mattson, “Generating well-distributed sets of Pareto points for engineering design using physical programming,” Optimization and Engineering, vol. 3, no. 4, pp. 431–450, 2002.
[20]  I. Y. Kim and O. L. Weck, “Adaptive weighted-sum method for bi-objective optimization: Pareto front generation,” Structural and Multidisciplinary Optimization, vol. 29, no. 2, pp. 149–158, 2005.
[21]  A. Molina-Cristobal, L. A. Griffin, P. J. Fleming, and D. H. Owens, “Multiobjective controller design: optimising controller structure with genetic algorithms,” in Proceedings of the 16th IFAC World Congress on Automatic Control, Prague, Czech Republic, July 2005.
[22]  C. A. C. Coello and N. C. Cortes, “Solving multiobjective optimization problems using an artificial immune system,” Genetic Programming and Evolvable Machines, vol. 6, no. 2, pp. 163–190, 2005.
[23]  M. Guntsch, Ant algorithms in stochastic and multi-criteria environments, Ph.D. thesis, Department of Economics and Business Engineering, University of Karlsruhe, Germany, 2004.
[24]  C. A. Coello and P. G. Toscano, “Multiobjective optimization using a micro-genetic algorithm,” in Proceedings of the Genetic And Evolutionary Computation Conference, (GECCO '01), L. Spector, et al., Ed., pp. 174–282, Morgan Kaufmann, San Francisco, Calif, USA, August 2001.
[25]  R. M. Janga and K. D. Nagesh, “An efficient multi-objective optimization algorithm based on swarm intelligence for engineering design,” Engineering Optimization, vol. 39, no. 1, pp. 49–68, 2007.
[26]  N. Keerativuttitumrong, N. Chaiyaratana, and V. Varavithya, “Multi-objective co-operative co-evolutionary genetic algorithm,” Lecture Notes in Computer Science, vol. 2439, pp. 288–297, 2002.
[27]  C. M. Fonseca and P. J. Fleming, “Genetic algorithms for multiobjective optimization: formulation, discussion and generalization,” in Proceedings of the 5th International Conference on Genetic Algorithms, pp. 416–423, Urbana-Champaign, Ill, USA, June 1993.
[28]  J. Horn and N. Nafpliotis, “Multiobjective optimization using the niched Pareto genetic algorithm,” IlliGAL Report 93005, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana-Champaign, Ill, USA, 1993.
[29]  A. R. Khorsand and M. R. Akbarzadeh, “Multi-objective meta level soft computing-based evolutionary structural design,” Journal of the Franklin Institute, vol. 344, no. 5, pp. 595–612, 2007.
[30]  M. P. Saka, A. Daloglu, and F. Malhas, “Optimum spacing design of grillage systems using a genetic algorithm,” Advances in Engineering Software, vol. 31, no. 11, pp. 863–873, 2000.
[31]  F. Erdal and M. P. Saka, “Effect of beam spacing in the harmony search based optimum design of grillages,” Asian Journal of Civil Engineering, vol. 9, no. 3, pp. 215–228, 2008.
[32]  M. P. Saka and F. Erdal, “Harmony search based algorithm for the optimum design of grillage systems to LRFD-AISC,” Structural and Multidisciplinary Optimization, vol. 38, no. 1, pp. 25–41, 2009.
[33]  J. K. Nelson and J. C. McCormac, Structural Analysis 3E WSE: Using Classical and Matrix Methods, John Wiley & Sons, New York, NY, USA, 2003.
[34]  http://jmetal.sourceforge.net/.
[35]  K. Deb, S. Agrawal, A. Pratab, and T. Meyarivan, “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization:NSGA-II,” in Proceedings of the 6th International Conference on Parallel Problem Solving from Nature, (PPSN '00), M. Schoenauer, K. Deb, G. Rudolph, et al., Eds., pp. 849–858, Paris, France, September 2000.
[36]  K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.
[37]  K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, New York, NY, USA, 2001.
[38]  D. W. Corne, J. D. Knowles, and M. J. Oates, “The Pareto envelope-based selection algorithm for multiobjective optimization,” in Proceedings of the 6th International Conference on Parallel Problem Solving from Nature, (PPSN '00), M. Schoenauer, K. Deb, G. Rudolph, et al., Eds., pp. 839–848, Paris, France, September 2000.
[39]  D. W. Corne, N. R. Jerram, J. D. Knowles, and M. J. Oates, “PESA-II: regionbased selection in evolutionary multiobjective optimization,” in Proceedings of the the Genetic and Evolutionary Computation Conference, (GECCO '01), L. Spector, E. D. Goodman, A. Wu, et al., Eds., pp. 283–290, San Francisco, Calif, USA, July 2001.
[40]  K. Deb, M. Mohan, and S. Mishra, “Towards a quick computation of well-spread pareto-optimal solutions,” in Proceedings of the Second International Conference on Evolutionary Multi-Criterion Optimization, (EMO '03), C. M. Fonseca, P. J. Fleming, E. Zitzler, K. Deb, and L. Thiele, Eds., pp. 222–236, Faro, Portugal, April, 2003.
[41]  A. J. Nebro, F. Luna, E. Alba, B. Dorronsoro, J. J. Durillo, and A. Beham, “AbYSS: adapting scatter search to multiobjective optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 4, pp. 439–457, 2008.
[42]  The MathWorks, “Statistical toolbox User's Guide,” 2008.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133