Skip to main content

Applications of Modified Optimization Algorithms to the Unconstrained and Constrained Problems

  • Chapter
  • First Online:
Mechanical Design Optimization Using Advanced Optimization Techniques

Part of the book series: Springer Series in Advanced Manufacturing ((SSAM))

  • 3779 Accesses

Abstract

This chapter presents the applications of the modified PSO, HEA and ABC algorithms. Thirteen unconstrained and twenty-four constrained benchmark problems available in the literature are considered to check the performance of the modified algorithms. In addition, different mechanical element design optimization problems such as design of a simple gear train, radial ball bearing, Belleville spring, multi-plate disc clutch brake, robot gripper, hydrostatic thrust bearing, a four-stage gear train, pressure vessel, welded beam, tension/compression spring, speed reducer, stiffened cylindrical shell, step cone pulley, screw jack, C-clamp, hydrodynamic bearing, cone clutch, cantilever support, hydraulic cylinder and a planetary gear train are presented and the effectiveness of the applications of the modified algorithms is checked. It is observed that the modifications in PSO and HEA are effective than their basic versions. Modifications in ABC are not so effective for the constrained benchmark functions but are found effective for the unconstrained benchmark functions and mechanical design problems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Li R, Chang X (2006) A modified genetic algorithm with multiple subpopulations and dynamic parameters applied in CVAR model. Comput Intell for Model, Control and Autom, Sydney, NSW 151

    Google Scholar 

  2. Liu J, Tang LA (1999) Modified genetic algorithm for single machine scheduling. Comput Ind Eng 37:43–46

    Article  MathSciNet  Google Scholar 

  3. Preechakul C, Kheawhom S (2009) Modified genetic algorithm with sampling techniques for chemical engineering optimization. J Ind Eng Chem 15:101–107

    Google Scholar 

  4. Montalvo I, Izquierdo J, Perez-Garcia R, Herrera M (2010) Improved performance of PSO with self-adaptive parameters for computing the optimal design of water supply systems. Eng Appl Artif Intell 23:727–735

    Article  Google Scholar 

  5. Cai X, Cui Y, Tan Y (2009) Predicted modified PSO with time-varying accelerator coefficients. Int J Bio Inspired Comput 1:50–60

    Article  Google Scholar 

  6. Cui H, Turan O (2010) Application of a new multi-agent hybrid co-evolution based particle swarm optimisation methodology in ship design. Comput Aided Des 2:1013–1027

    Article  Google Scholar 

  7. Yildiz AR (2009) A novel particle swarm optimization approach for product design and manufacturing. Int J Adv Manuf Technol 40:617–628

    Article  Google Scholar 

  8. Shen Q, Jiang J, Tao J, Shen G, Yu R (2005) Modified ant colony optimization algorithm for variable selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors. J chem inf model 45:1024–1029

    Article  Google Scholar 

  9. Karaboga D, Akay B (2010) A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl Soft Comput doi: 10.1016/j.asoc.2010.12.001

  10. Mouti FSA, Hawary MEE (2009) Modified artificial bee colony algorithm for optimal distributed generation sizing and allocation in distribution systems. IEEE Electrical Power and Energy Conference (EPEC), Montreal, QC, pp 1–9

    Google Scholar 

  11. Yue H, Gu G, Liu H, Shen J, Zhao J (2009) A modified ant colony optimization algorithm for tumor marker gene selection. Genomics, Proteomics Bioinf 7:200–208

    Article  Google Scholar 

  12. Hui L, Zixing C, Yong W (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10:629–640

    Article  Google Scholar 

  13. Wen YL (2010) A GA–DE hybrid evolutionary algorithm for path synbook of four-bar linkage. Mech Mach Theor 45:1096–1107

    Article  MATH  Google Scholar 

  14. Yannis M, Magdalene M (2010) Hybrid multi-swarm particle swarm optimization algorithm for the probabilistic travelling salesman problem. Comput Oper Res 37:432–442

    Article  MathSciNet  MATH  Google Scholar 

  15. Ying PC (2010) An ant direction hybrid differential evolution algorithm in determining the tilt angle for photovoltaic modules. Expert Sys Appl 37:5415–5422

    Article  Google Scholar 

  16. Shahla N, Mohammad EB, Nasser G, Mehdi HA (2009) A novel ACO–GA hybrid algorithm for feature selection in protein function prediction. Expert Sys Appl 36:12086–12094

    Article  Google Scholar 

  17. Tung Y, Erwie Z (2008) A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl Soft Comput 8:849–857

    Article  Google Scholar 

  18. Dong HK, Ajith A, Jae HC (2007) A hybrid genetic algorithm and bacterial foraging approach for global optimization. Inf Sci 177:3918–3937

    Article  Google Scholar 

  19. Simon D (2008) Biogeography-based optimization. IEEE Trans on Evol Comput 12:702–713

    Article  Google Scholar 

  20. Liang JJ, Runarsson TP, Montes EM, Clerc M, Suganthan PN, Coello CAC, and Deb K (2006) Problem definitions and evolution criteria for the CEC 2006 special session on constrained real-parameter optimization. Tech Rep, Nanyang Technol Univ, Singapore. http://www.ntu.edu.sg/home/EPNSugan

  21. Sandgren E (1988) Nonlinear integer and discrete programming in mechanical design. In: Proceedings of the ASME design technology conference, Kissimine, FL, pp 95–105

    Google Scholar 

  22. Kannan BK, Kramer SN (1994) An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. ASME J Mech Des 116:318–320

    Article  Google Scholar 

  23. Deb K (1997) GeneAS: a robust optimal design technique for mechanical component design. Evol Algorithms in Eng Appl. Springer, Berlin, pp 497–514

    Google Scholar 

  24. Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41:113–127

    Article  Google Scholar 

  25. Ray T, Liew K (2003) Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evol Comput 7:386–396

    Article  Google Scholar 

  26. Montes ME, Coello CAC (2005) A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Trans Evol Comput 9:1–17

    Article  Google Scholar 

  27. Parsopoulos K, Vrahatis M (2005) Unified particle swarm optimization for solving constrained engineering optimization problems. In: Proceedings of advanced in natural computation, LNCS 3612. Springer-Verlag, Berlin, pp 582–591

    Google Scholar 

  28. He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20:89–99

    Article  Google Scholar 

  29. Huang FA, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356

    Article  MathSciNet  MATH  Google Scholar 

  30. Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10:629–640

    Article  Google Scholar 

  31. Akay B, Karaboga D (2010) Artificial bee colonyArtificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf. Doi: 10.1007/s10845-010-0393-4

  32. Ragsdell KM, Phillips DT (1976) Optimal design of a class of welded structures using geometric programming. ASME J Eng Ind 98(3):1021–1025

    Article  Google Scholar 

  33. Belegundu AD (1982) A study of mathematical programming methods for structural optimization. Doctoral Dissertation, Department of Civil and Environmental Engineering, University of Iowa, USA

    Google Scholar 

  34. Leandro SC, Viviana CM (2008) Use of chaotic sequences in a biologically inspired algorithm. Expert Syst Appl 34(3):1905–1913

    Article  Google Scholar 

  35. Jarmai K, Snyman JA, Farkas J (2006) Minimum cost design of a welded orthogonally stiffened cylindrical shell. Comput Struct 84:787–797

    Article  Google Scholar 

  36. Rao SS (2002) Engineering optimization: theory and practice. New Age International, New Delhi

    Google Scholar 

  37. Simionescu PA, Beale D, Dozier GV (2006) Teeth-number synbook of a multispeed planetary transmission using an estimation of distribution algorithm. J Mech Des 128:108–115

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag London

About this chapter

Cite this chapter

Rao, R.V., Savsani, V.J. (2012). Applications of Modified Optimization Algorithms to the Unconstrained and Constrained Problems. In: Mechanical Design Optimization Using Advanced Optimization Techniques. Springer Series in Advanced Manufacturing. Springer, London. https://doi.org/10.1007/978-1-4471-2748-2_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-2748-2_4

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2747-5

  • Online ISBN: 978-1-4471-2748-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics