Skip to main content
SpringerLink
Log in

A Hybrid Metaheuristic Algorithm for Truss Structure Domain’s Optimization Problem

  • Chapter
  • First Online:
Theoretical, Modelling and Numerical Simulations Toward Industry 4.0

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 319))

Abstract

For calculating constrained optimization problem various socio/bio-inspired algorithms have adopted a penalty function approach to handle linear and nonlinear constraints. In a general sense, the approach is quite easy to understand, but a precise choice of penalty parameter is very much important. It requires a bunch number of primer preliminaries. So as to beat this restriction another self-adaptive penalty function (SAPF) approach will be proposed and incorporated into Particle Swarm Optimization (PSO) algorithm. This approach is referred to as PSO-SAPF. Besides, PSO-SAPF approach will be hybridized with Colliding Bodies Optimization (CBO) referred to as PSO-SAPF-CBO algorithm. The performance of PSO-SAPF and PSO-SAPF-CBO algorithm will be distinctly validated by solving discrete and mixed variable problems from truss structure domain and linear and nonlinear domain. The effect of behavior of penalty parameter, penalty function and constrained violation will be analyzed and discussed with the advantages over other algorithms.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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, L., Huang, Z., Liu, F., Wu, Q.: A heuristic particle swarm optimizer for optimization of pin connected structures. Comput. Struct. 85(7–8), 340–349 (2007)

    Article  Google Scholar 

  2. Datta, D., Figueira, J.R.: A real-integer-discrete-coded particle swarm optimization for design problems. Appl. Soft Comput. 11(4), 3625–3633 (2011)

    Article  Google Scholar 

  3. Gandomi, A.H., Yang, X.-S., Alavi, A.H.: Mixed variable structural optimization using firefly algorithm. Comput. Struct. 89(23–24), 2325–2336 (2011)

    Article  Google Scholar 

  4. Kulkarni, A.J., Tai, K.: Probability collectives: a multi-agent approach for solving combinatorial optimization problems. Appl. Soft Comput. 10(3), 759–771 (2010)

    Article  Google Scholar 

  5. Kaveh, A., Mahdavi, V.: Colliding bodies optimization method for optimum design of truss structures with continuous variables. Adv. Eng. Softw. 70, 1–12 (2014)

    Article  Google Scholar 

  6. Cheng, M.-Y., Prayogo, D.: Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98–112 (2014)

    Article  Google Scholar 

  7. Sadollah, A., Bahreininejad, A., Eskandar, H., Hamdi, M.: Mine blast algorithm for optimization of truss structures with discrete variables. Comput. Struct. 102, 49–63 (2012)

    Article  Google Scholar 

  8. L. Lamberti, C. Pappalettere, An improved harmony-search algorithm for truss structure optimization, in Proceedings of the Twelfth International Conference on Civil, Structural and Environmental Engineering Computing (Civil-Comp Press, Stirlingshire, UK, 2009), Paper 65

    Google Scholar 

  9. Shih, C., Yang, Y.: Generalized Hopfield network based structural optimization using sequential unconstrained minimization technique with additional penalty strategy. Adv. Eng. Softw. 33(7–10), 721–729 (2002)

    Article  MATH  Google Scholar 

  10. K. Deb, S. Gulati, S. Chakrabarti, Optimal truss-structure design using real-coded genetic algorithms, in Proceedings of the Third Annual Conference Genetic Programming 1998 (1998), pp. 22–25

    Google Scholar 

  11. A.J. Kulkarni, I.P. Durugkar, M. Kumar, Cohort intelligence: a self supervised learning behavior, in 2013 IEEE International Conference on Systems, Man, and Cybernetics (IEEE), pp. 1396–1400

    Google Scholar 

  12. Huan, T.T., Kulkarni, A.J., Kanesan, J., Huang, C.J., Abraham, A.: Ideology algorithm: a socio-inspired optimization methodology. Neural Comput. Appl. 28(1), 845–876 (2017)

    Article  Google Scholar 

  13. Kumar, M., Kulkarni, A.J., Satapathy, S.C.: Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology. Future Generation Comput. Syst. 81, 252–272 (2018)

    Article  Google Scholar 

  14. K. Deb, S. Agrawal, A niched-penalty approach for constraint handling in genetic algorithms, in Artificial Neural Nets and Genetic Algorithms (Springer, 1999), pp. 235–243

    Google Scholar 

  15. Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186(2–4), 311–338 (2000)

    Article  MATH  Google Scholar 

  16. Kulkarni, A.J., Shabir, H.: Solving 0–1 knapsack problem using cohort intelligence algorithm. Int. J. Mach. Learn. Cybernet. 7(3), 427–441 (2016)

    Article  Google Scholar 

  17. D.G. Luenberger, Y. Ye, Linear and Nonlinear Programming (Springer, 1984)

    Google Scholar 

  18. Homaifar, A., Qi, C.X., Lai, S.H.: Constrained optimization via genetic algorithms. Simulation 62(4), 242–253 (1994)

    Article  Google Scholar 

  19. Michalewicz, Z., Schoenauer, M.: Evolutionary algorithms for constrained parameter optimization problems. Evol. Comput. 4(1), 1–32 (1996)

    Article  Google Scholar 

  20. M. Gen, R. Cheng, A survey of penalty techniques in genetic algorithms, in Proceedings of IEEE International Conference on Evolutionary Computation (IEEE, 1996), pp. 804–809

    Google Scholar 

  21. R. Le Riche, C. Knopf-Lenoir, R.T. Haftka, A segregated genetic algorithm for constrained structural optimization, in ICGA (1995), pp. 558–565

    Google Scholar 

  22. Azad, S.K., Hasançebi, O.: Upper bound strategy for metaheuristic based design optimization of steel frames. Adv. Eng. Softw. 57, 19–32 (2013)

    Article  Google Scholar 

  23. Kale, I.R., Kulkarni, A.J.: Cohort intelligence algorithm for discrete and mixed variable engineering problems. Int. J. Parallel Emerg. Distrib. Syst. 33(6), 627–662 (2018)

    Article  Google Scholar 

  24. Kannan, B., Kramer, S.N.: An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J. Mech. Des. 116(2), 405–411 (1994)

    Article  Google Scholar 

  25. Curtis, F.E., Nocedal, J.: Flexible penalty functions for nonlinear constrained optimization. IMA J. Num. Analy. 28(4), 749–769 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  26. C.G. Broyden, N.F. Attia, A smooth sequential penalty function method for solving nonlinear programming problems, in System Modelling and Optimization (Springer Berlin Heidelberg, Berlin, Heidelberg, 1984), pp. 237–245

    Google Scholar 

  27. Parsopoulos, K., Vrahatis, M.: Initializing the particle swarm optimizer using the nonlinear simplex method. Adv. intell. Syst. Fuzzy Syst. Evol. Comput. 216, 1–6 (2002)

    Google Scholar 

  28. G. Coath, S.K. Halgamuge, A comparison of constraint-handling methods for the application of particle swarm optimization to constrained nonlinear optimization problems, in The 2003 Congress on Evolutionary Computation, CEC’03, vol. 4 (IEEE, 2003), pp. 2419–2425

    Google Scholar 

  29. Nie, P.-Y.: A new penalty method for nonlinear programming. Comput. Math Appl. 52(6–7), 883–896 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  30. Hasançebi, O., Azad, S.K.: Adaptive dimensional search: a new metaheuristic algorithm for discrete truss sizing optimization. Comput. Struct. 154, 1–16 (2015)

    Article  Google Scholar 

  31. A.J. Kulkarni, G. Krishnasamy, A. Abraham, Cohort intelligence for solving travelling salesman problems, in Cohort Intelligence: A Socio-inspired Optimization Method (Springer, 2017), pp. 75–86

    Google Scholar 

  32. Shastri, A.S., Kulkarni, A.J.: Multi-cohort intelligence algorithm: an intra-group and inter-group learning behaviour based socio-inspired optimisation methodology. Int. J. Parallel Emerg. Distrib. Syst. 33(6), 675–715 (2018)

    Article  Google Scholar 

  33. Krishnasamy, G., Kulkarni, A.J., Paramesran, R.: A hybrid approach for data clustering based on modified cohort intelligence and K-means. Exp. Syst. Appl. 41(13), 6009–6016 (2014)

    Article  Google Scholar 

  34. S.M. Gaikwad, R.R. Joshi, A.J. Kulkarni, Cohort intelligence and genetic algorithm along with ahp to recommend an ice cream to a diabetic patient, in International Conference on Swarm, Evolutionary, and Memetic Computing (Springer, 2015), pp. 40–49

    Google Scholar 

  35. Patankar, N.S., Kulkarni, A.J.: Variations of cohort intelligence. Soft. Comput. 22(6), 1731–1747 (2018)

    Article  Google Scholar 

  36. Sarmah, D.K., Kulkarni, A.J.: Image steganography capacity improvement using cohort intelligence and modified multi-random start local search methods. Arab. J. Sci. Eng. 43(8), 3927–3950 (2018)

    Article  Google Scholar 

  37. Dhavle, S.V., Kulkarni, A.J., Shastri, A., Kale, I.R.: Design and economic optimization of shell-and-tube heat exchanger using cohort intelligence algorithm. Neural Comput. Appl. 30(1), 111–125 (2018)

    Article  Google Scholar 

  38. Kaveh, A., Mahdavi, V.: A hybrid CBO–PSO algorithm for optimal design of truss structures with dynamic constraints. Appl. Soft Comput. 34, 260–273 (2015)

    Article  Google Scholar 

  39. Kaveh, A., Ghazaan, M.I.: Enhanced colliding bodies optimization for design problems with continuous and discrete variables. Adv. Eng. Softw. 77, 66–75 (2014)

    Article  Google Scholar 

  40. Kaveh, A., Mahdavi, V.R.: Colliding bodies optimization: a novel meta-heuristic method. Comput. Struct. 139, 18–27 (2014)

    Article  Google Scholar 

  41. S. Bansal, A. Mani, C. Patvardhan, Is stochastic ranking really better than feasibility rules for constraint handling in evolutionary algorithms? in 2009 World Congress on Nature and Biologically Inspired Computing (NaBIC) (IEEE, 2009), pp. 1564–1567

    Google Scholar 

  42. Kulkarni, A.J., Baki, M.F., Chaouch, B.A.: Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems. Eur. J. Oper. Res. 250(2), 427–447 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  43. An adaptive penalty function in genetic algorithms for structural design optimization

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia

    Kallol Biswas, Pandian Vasant & Rajalingam Sokkalingam

  2. Department of Petroleum Geosciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia

    Jose Antonio Gamez Vintaned

  3. Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia

    Junzo Watada & Arunava Roy

Authors
  1. Kallol Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pandian Vasant
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jose Antonio Gamez Vintaned
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junzo Watada
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arunava Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rajalingam Sokkalingam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kallol Biswas .

Editor information

Editors and Affiliations

  1. Fundamental and Applied Sciences Department and Centre for Systems Engineering (CSE), Institute of Autonomous System, Universiti Teknologi PETRONAS, Seri Iskandar, Perak Darul Ridzuan, Malaysia

    Samsul Ariffin Abdul Karim

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Biswas, K., Vasant, P., Vintaned, J.A.G., Watada, J., Roy, A., Sokkalingam, R. (2021). A Hybrid Metaheuristic Algorithm for Truss Structure Domain’s Optimization Problem. In: Abdul Karim, S.A. (eds) Theoretical, Modelling and Numerical Simulations Toward Industry 4.0. Studies in Systems, Decision and Control, vol 319. Springer, Singapore. https://doi.org/10.1007/978-981-15-8987-4_2

Download citation

Publish with us

Policies and ethics

Search

Navigation