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Optimization of Mechanical Components by the Improved Grey Wolf Optimization

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Optimization Methods in Engineering

Part of the book series: Lecture Notes on Multidisciplinary Industrial Engineering ((LNMUINEN))

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Abstract

Optimization means the demonstration of detecting the optimal outcome under specific circumstances. Engineers need to take numerously specialized as well as supervisory decisions in various design phases, construction, and industrial system maintenance. The main target of every such decision is either to restrain the work necessitated or raise the profit. As the profit wanted or the work required in any situation of real world can be expressed as a specific decision variable’s function, optimization can be characterized as the way towards discovering the conditions that provide maximum or minimum function values. Complex problems of optimization while solving with traditional numerical methods had mathematical downsides. This is the reason because of which researchers had to be dependent on meta-heuristic techniques. Meta-heuristics have turned out to be amazingly usual due to its local optima avoidance, simplicity and flexibility characteristics. IGWO is a new meta-heuristic inspired by grey wolves. In this paper, the implementation of improved grey wolf optimization is done for the optimization of pressure vessel design and welded beam design that have various salient engineering applications. The comparison of achieved results with some other approaches reveals the efficacy of IGWO algorithm to solve these significant mechanical engineering design optimization problems.

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References

  1. Kaveh, A., Talatahari, S.: An improved ant colony optimization for constrained engineering design problems. Eng. Comput. (Swansea, Wales) 27(1), 155–182 (2010)

    Google Scholar 

  2. Man, K.F., Tang, K.S., Kwong, S.: Genetic algorithms: concepts and applications. IEEE Trans. Ind. Electron. 43(5), 519–534 (1996)

    Article  Google Scholar 

  3. Storn, R., Price, K.: Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MATH  Google Scholar 

  4. Yong, W., Gang, Z., Pei-chann, C.: Improved evolutionary programming algorithm and its application research on the optimization of ordering plan. Syst. Eng. Theory Pract. 29(6), 172–177 (2009)

    Article  Google Scholar 

  5. Hansen, N., Müller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). E Comput. 11(1), 1–18 (2003)

    Google Scholar 

  6. Mezura-Montes, E., Coello Coello, C.A., Hernandez-Aguirre, A.: Using evolution strategies to solve constrained optimization problems. E Algorithms Intell. Tools Eng. Optim. 04 (2004)

    Google Scholar 

  7. Simon, D.: Biogeography-based optimization. IEEE Trans. E Comput. 12(6), 702–713 (2008)

    Article  Google Scholar 

  8. Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)

    Article  Google Scholar 

  9. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95—International Conference on Neural Networks, vol. 4, 1942–1948 (1995)

    Google Scholar 

  10. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  11. Webster, B., Bernhard, P.: A local search optimization algorithm based on natural principles of gravitation. In: Proceedings of the International Conference on Information and Knowledge Engineering, vol. 1, p. 18 (2003)

    Google Scholar 

  12. Erol, O.K., Eksin, I.: A new optimization method: Big Bang-Big Crunch. Adv. Eng. Softw. 37(2), 106–111 (2006)

    Article  Google Scholar 

  13. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. (NY) 179(13), 2232–2248 (2009)

    Article  MATH  Google Scholar 

  14. Kaveh, A., Talatahari, S.: A novel heuristic optimization method: charged system search. Acta Mech. 213(3–4), 267–289 (2010)

    Article  MATH  Google Scholar 

  15. Formato, R.A.: Central force optimization: a new metaheuristic with applications in applied. Prog. Electromagn. Res. 77, 425–491 (2007)

    Google Scholar 

  16. Alatas, B.: ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst. Appl. 38(10), 13170–13180 (2011)

    Article  Google Scholar 

  17. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  18. David, C.N.D., Stephen, S.E.A., Joe, A.: Cost minimization of welded beam DESIGN problem using PSO, SA, PS, GODLIKE, CUCKOO, FF, FP, ALO, GSA and MVO. Int. J. Appl. Math. Stat. Sci. 5(1), 1–14 (2016)

    Google Scholar 

  19. Deb, K.: Optimal design of a welded beam via genetic algorithms. AIAA J. 29(11), 2013–2015 (1991)

    Article  Google Scholar 

  20. Ragsdell, K.M., Phillips, D.T.: Optimal design of a class of welded structures using geometric programming. J. Eng. Ind. 98(3), 1021–1025 (1976)

    Article  Google Scholar 

  21. Kannan, B.K.: 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)

    Google Scholar 

  22. Deb, K., Pratap, A., Moitra, S.: Mechanical component design for multiple objectives using elitist non-dominated sorting GA. In: Parallel Problem Solving from Nature PPSN VI, pp. 859–868. Springer, Berlin, Heidelberg (2000)

    Google Scholar 

  23. Coello Coello, C.A.: Use of a self-adaptive penalty approach for engineering optimization problems. Comput. Ind. 41(2), 113–127 (2000)

    Google Scholar 

  24. Yildiz, A.R.: Hybrid Taguchi-harmony search algorithm for solving engineering optimization problems. Int. J. Ind. Eng. Theory Appl. Pract. 15(3), 286–293 (2008)

    Google Scholar 

  25. Mosavi, A., Vaezipour, A.: Reactive search optimization: application to multiobjective optimization problems. Appl. Math. 03(10), 1572–1582 (2012)

    Article  Google Scholar 

  26. Cagnina, L.C., Esquivel, S.C., Nacional, U., Luis, D.S., Luis, S., Coello, C.A.C.: Solving engineering optimization problems with the simple constrained particle swarm optimizer. Eng. Optim. 32, 319–326 (2008)

    Google Scholar 

  27. Hasançebi, O., Azad, K.: An efficient metaheuristic algorithm for engineering optimization: SOPT. Int. J. Optim. Civ. Eng. 2(4), 479–487 (2012)

    Google Scholar 

  28. Thakur, N., Keane, A.J., Nair, P.B.: Estimating the effect of manufacturing variability on turbine blade life. In: 4th International Workshop on Reliable Engineering Computing (REC 2010) (2010). https://doi.org/10.3850/978-981-08-5118-7_030

  29. Reddy, M.J., Kumar, D.N.: An efficient multi-objective optimization algorithm based. Eng. Optim. 39(1), 49–68 (2007)

    Article  MathSciNet  Google Scholar 

  30. Janga Reddy, M., Nagesh Kumar, D.: An efficient multi-objective optimization algorithm based on swarm intelligence for engineering design. Eng. Optim. 39(1), 49–68 (2007)

    Google Scholar 

  31. Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194(36–38), 3902–3933 (2005)

    Article  MATH  Google Scholar 

  32. Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188(2), 1567–1579 (2007)

    MathSciNet  MATH  Google Scholar 

  33. Kazemzadeh Azad, S., Hasancebi, O. Erol, O.K.: Evaluating efficiency of big-bang big-crunch algorithm in benchmark engineering optimization problems. Int. J. Optim. Civ. Eng. 1(3), 495–505 (2011)

    Google Scholar 

  34. Sarkar, M.: Multi-objective welded beam design optimization using T-norm and T-co-norm based intuitionistic fuzzy optimization technique. Adv. Fuzzy Math. 12(3), 549–575 (2017)

    Google Scholar 

  35. Akay, B., Karaboga, D.: Artificial bee colony algorithm for large-scale problems and engineering design optimization. J. Intell. Manuf. 23(4), 1001–1014 (2012)

    Article  Google Scholar 

  36. Pham, D.T., Ghanbarzadeh, A., Otri, S., Koç, E.: Optimal design of mechanical components using the bees algorithm. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 223(5), 1051–1056 (2009)

    Google Scholar 

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

    Google Scholar 

  38. Zhou, Y., Zhou, G., Zhang, J.: A hybrid glowworm swarm optimization algorithm for constrained engineering design problems. Appl. Math. Inf. Sci. 7(1), 379–388 (2013)

    Article  Google Scholar 

  39. Lin, G.H., Zhang, J., Liu, Z.H.: Hybrid particle swarm optimization with differential evolution for numerical and engineering optimization. Int. J. Autom. Comput. 15(1), 103–114 (2018)

    Article  Google Scholar 

  40. Parsopoulos, K.E., Vrahatis, M.N.: Unified particle swarm optimization for solving constrained engineering optimization problems. Adv. Nat. Comput. 3612, 582–591 (2005)

    Google Scholar 

  41. Ray, T., Liew, K.M.: Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans. E Comput. 7(4), 386–396 (2003)

    Article  Google Scholar 

  42. Muangkote, N., Sunat, K., Chiewchanwattana, S.: An improved grey wolf optimizer for training q-Gaussian radial basis functional-link nets. In: 2014 International Computer Science and Engineering Conference ICSEC 2014, pp. 209–214 (2014)

    Google Scholar 

  43. Gupta, S.R., Vora, C.P.: A review paper on pressure vessel design and analysis. Int. J. Eng. Res. Technol. 3(3), 295–300 (2014)

    Google Scholar 

  44. Vasava, A., Vasava, J., Patel, S.: Design and analysis of pressure vessel amalgamating with selection of material used in marine application. Int. J. Innov. Res. Sci. Eng. Technol. 2(6), 2035–2042 (2013)

    Google Scholar 

  45. Lu, Y.Q., Hui, H.: Investigation on mechanical behaviors of cold stretched and cryogenic stretched austenitic stainless steel pressure vessels. Procedia Eng. 130, 628–637 (2015)

    Article  Google Scholar 

  46. Weil, N.A., Murphy, J.J.: Design and analysis of welded pressure-vessel skirt supports. J. Eng. Ind. 82(1), 1 (1960)

    Google Scholar 

  47. Raparla, S.K.: Design and analysis of multilayer high pressure vessels. Int. J. Eng. Res. Appl. 2(1), 355–361 (2012)

    Google Scholar 

  48. Gedam, A.C.: Stress analysis of pressure vessel with different end connections. Int. J. Mech. Eng. 3(11), 19–27 (2015)

    MathSciNet  Google Scholar 

  49. Saidpatil, V.V., Thakare, A.S.: Design & weight optimization of pressure vessel due to thickness using finite element analysis. Int. J. Emerg. Eng. Res. Technol. 2(3), 1–8 (2014)

    Google Scholar 

  50. Liu, P.-F., Xu, P., Han, S.-X., Zheng, J.-Y.: Optimal design of pressure vessel using an improved genetic algorithm. J. Zhejiang Univ. Sci. A 9(9), 1264–1269 (2008)

    Article  MATH  Google Scholar 

  51. Dalgleish, T., et al.: Chemical engineering design. J. Exp. Psychol. Gen. 136(1), 23–42 (2007)

    Article  Google Scholar 

  52. Rahman, N.A., Sheikh Abdullah, S.R., Ali, N.M.: Interactive short cut method for pressure vessel design based on asme code. J. Eng. Sci. Technol. 10(1), 53–60 (2015)

    Google Scholar 

  53. Sathe, A.A., Maurya, V.R., Tamhane, S.V., Save, A.P., Nikam, P.V.: Design and analysis of pressure vessel components as per ASME Sec. VIII Div. III. IJEDR 6(1), 834–840 (2018)

    Google Scholar 

  54. Zhu, L., Boyle, J.T.: Optimal shapes for axisymmetric pressure vessels: a brief overview. J. Press. Vessel Technol. Trans. ASME 122(4), 443–449 (2000)

    Article  Google Scholar 

  55. Ramakrishnan, C.V., Francavilla, A.: Structural shape optimization using penalty functions. J. Struct. Mech. 3(4), 403–422 (1974)

    Article  Google Scholar 

  56. Queau, J.P., Trompette, P.: Two-dimensional shape optimal design by the finite element method. Int. J. Numer. Methods Eng. 15(11), 1603–1612 (1980)

    Article  MATH  Google Scholar 

  57. Li, Y.: Sensitivity analysis in shape optimization design for a pressure vessel. Int. J. Press. Vessels Pip. 49(3), 387–397 (1992)

    Article  Google Scholar 

  58. Hinton, E., Rao, N.V.R., Sienz, J.: Finite element structural shape and thickness optimization of axisymmetric shells. Eng. Comput. 9(5), 499–527 (1992)

    Article  Google Scholar 

  59. Boisserie, J.M., R. Glowinski, Optimization of the thickness law for thin axisymmetric shells, 1978

    Google Scholar 

  60. Marcelin, J.L., Trompette, P.: Optimal shape design of thin axisymmetric shells. Eng. Optim. 13(2), 109–117 (1988)

    Article  Google Scholar 

  61. Błachut, J.: Minimum weight of internally pressurised domes subject to plastic load failure. Thin-Walled Struct. 27(2), 127–146 (1997)

    Article  Google Scholar 

  62. Błachut, J., Ramachandra, L.S.: Optimization of internally pressurised torispheres subject to shakedown via GAs. Eng. Optim. 29(1–4), 113–129 (1997)

    Article  Google Scholar 

  63. Pornsing, C., Saichareon, K., Karot, T.: A modified particle swarm optimization for engineering constrained optimization problems. Int. J. Comput. Sci. Electron. Eng. 3(1), 1–5 (2015)

    Google Scholar 

  64. Mech, L.D.: Alpha status, dominance, and division of labor in wolf packs. Can. J. Zool. 77(8), 1196–1203 (1999)

    Article  Google Scholar 

  65. Muro, C., Escobedo, R., Spector, L., Coppinger, R.P.: Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behav. Processes 88(3), 192–197 (2011)

    Article  Google Scholar 

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Authors and Affiliations

  1. I.K. Gujral Punjab Technical University, Kapurthala, 144603, India

    Prabhjit Singh

  2. Department of Mechanical Engineering, DAV Institute of Engineering and Technology, Jalandhar, 144008, India

    Sanjeev Saini & Ankush Kohli

Authors
  1. Prabhjit Singh
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  2. Sanjeev Saini
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  3. Ankush Kohli
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Correspondence to Sanjeev Saini .

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Editors and Affiliations

  1. Department of Industrial and Production Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Mohit Tyagi

  2. Department of Industrial and Production Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Anish Sachdeva

  3. Department of Industrial and Production Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Vishal Sharma

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Singh, P., Saini, S., Kohli, A. (2021). Optimization of Mechanical Components by the Improved Grey Wolf Optimization. In: Tyagi, M., Sachdeva, A., Sharma, V. (eds) Optimization Methods in Engineering. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-4550-4_5

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  • DOI: https://doi.org/10.1007/978-981-15-4550-4_5

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