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On dedicated evolutionary algorithms for large non-linear constrained optimization problems

Published: 12 July 2014 Publication History

Abstract

This paper considers advances in development of dedicated Evolutionary Algorithms (EA) for efficiently solving large, non-linear, constrained optimization problems. The EA are precisely understood here as decimal-coded Genetic Algorithms consisting of three operators: selection, crossover and mutation, followed by several newly developed calculation speed-up techniques based on simple concepts. These techniques include: solution smoothing and balancing, a--posteriori solution error analysis and related techniques, non-standard use of distributed and parallel calculations, and adaptive step-by-step mesh refinement. Efficiency of the techniques proposed here has been evaluated using several benchmark problems e.g. residual stresses analysis in chosen elastic-plastic bodies under cyclic loadings. These preliminary tests indicate significant acceleration of the large optimization processes involved. The final objective of our research is development of an algorithm efficient enough for solving real, large engineering problems.

References

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Engelbrecht, A. P. 2007. Computational intelligence: an introduction. Wiley, Chichester.
[2]
Glowacki, M., and Orkisz, J. 2013. Advances in Development of Dedicated Evolutionary Algorithms for Large Non-Linear Constrained Optimization Problems. IPPT Reports on Fund. Tech. Research. 47, 4 (Dec. 2013), 25--29.
[3]
Grosan, C., Abraham, A., and Ishibuchi, H., Eds. 2007. Hybrid Evolutionary Algorithms. Studies in Computational Intelligence. 75, Springer.
[4]
Karmowski, W., and Orkisz, J. 1993. Physically Based Method of Enhancement of Experimental Data - Concepts, Formulation and Application to Identification of Residual Stresses. In Inv. Problems in Engng. Mech. Proc. of IUTAM Symp. on Inv. Problems in Engng. Mech. (Tokyo, Japan, 1992), M. Tanaka, and H. D. Bui, Eds. Springer, 61--70.
[5]
Orkisz, J., and Glowacki, M. 2014. On Acceleration of Evolutionary Algorithms Taking Advantage from A--posteriori Error Analysis. Computing and Informatics. 33, 1 (Feb. 2014), 154--174.
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Salkauskas, K., and Lancaster, P. 1990. Curve and surface fitting. Academic Press.

Cited By

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  • (2017)On advances in development of evolutionary algorithms for chosen large optimization problems of computational mechanics2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8285212(1-5)Online publication date: Nov-2017
  • (2015)On increasing computational efficiency of evolutionary algorithms applied to large optimization problems2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7257214(2639-2646)Online publication date: May-2015

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cover image ACM Conferences
GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
July 2014
1524 pages
ISBN:9781450328814
DOI:10.1145/2598394
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2014

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Author Tags

  1. empirical study
  2. genetic algorithms
  3. mechanical engineering
  4. parallelization
  5. speedup technique

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GECCO '14
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GECCO '14: Genetic and Evolutionary Computation Conference
July 12 - 16, 2014
BC, Vancouver, Canada

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GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2017)On advances in development of evolutionary algorithms for chosen large optimization problems of computational mechanics2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8285212(1-5)Online publication date: Nov-2017
  • (2015)On increasing computational efficiency of evolutionary algorithms applied to large optimization problems2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7257214(2639-2646)Online publication date: May-2015

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