Skip to main content
SpringerLink
Log in

Engineering optimization based on ideal gas molecular movement algorithm

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

The present work introduces a new metaheuristic optimization method based on the ideal gas molecular movement (IGMM) to solve mathematical and engineering optimization problems. Ideal gas molecules scatter throughout the confined environment quickly. This is embedded in the high speed of molecules, collisions between them and with the surrounding barriers. In IGMM algorithm, the initial population of gas molecules is randomly generated and the governing equations related to the velocity of gas molecules and collisions between those are utilized to accomplish the optimal solutions. To verify the performance of the IGMM algorithm, some mathematical and engineering benchmark optimization problems, commonly used in the literature, are inspected. Comparison of results obtained by IGMM with other optimization algorithms show that the proposed method has a challenging capacity in finding the optimal solutions and exhibits significance both in terms of the accuracy and reduction on the number of function evaluations vital in reaching the global optimum.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213:267–289. doi:10.1007/s00707-009-0270-4

    Article  MATH  Google Scholar 

  2. Alberdi R, Khandelwal K (2015) Comparison of robustness of metaheuristic algorithms for steel frame optimization. Eng Struct 102:40–60. doi:10.1016/j.engstruct.2015.08.012

    Article  Google Scholar 

  3. Mirjalili S (2015) Dragonfly algorithm : a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl. doi:10.1007/s00521-015-1920-1

    Google Scholar 

  4. Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput 27:155–182. doi:10.1108/02644401011008577

    Article  MATH  Google Scholar 

  5. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Tech Rep TR06, Erciyes Univ 10. doi:citeulike. Article-id: 6592152

  6. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 World Congr. Nat. Biol. Inspired Comput. NABIC 2009—Proc, pp 210–214

  7. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98. doi:10.1016/j.advengsoft.2015.01.010

    Article  Google Scholar 

  8. Wang G-G, Guo L, Gandomi AH et al (2014) Chaotic Krill Herd algorithm. Inf Sci (Ny) 274:17–34. doi:10.1016/j.ins.2014.02.123

    Article  MathSciNet  Google Scholar 

  9. Guo L, Wang G-G, Gandomi AH et al (2014) A new improved krill herd algorithm for global numerical optimization. Neurocomputing 138:392–402. doi:10.1016/j.neucom.2014.01.023

    Article  Google Scholar 

  10. Wang G-G, Deb S, Coelho LDS (2015) Earthworm optimization algorithm: a bioinspired metaheuristic algorithm for global optimization problems. Int J Bio Inspir Comput (in press)

  11. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (NY) 179:2232–2248. doi:10.1016/j.ins.2009.03.004

    Article  MATH  Google Scholar 

  12. Wang G-G, Deb S, Cui Z (2015) Monarch butterfly optimization. Neural Comput Appl. doi:10.1007/s00521-015-1923-y

    Google Scholar 

  13. Wang G-G, Deb S, Coelho LDS (2015) Elephant herding optimization. 3rd Int Symp Comput Bus Intell. doi:10.1109/ISCBI.2015.8

  14. Wang G-G, Deb S, Gao X-Z, Coelho LDS (2016) A new metaheuristic optimization algorithm motivated by elephant herding behavior. Int J Bio Inspir Comput (in press)

  15. Mirjalili SM, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. doi:10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  16. Dizangian B, Ghasemi MR (2015) A fast decoupled reliability-based design optimization of structures using B-spline interpolation curves. J Brazilian Soc Mech Sci Eng 1–13. doi:10.1007/s40430-015-0423-4

  17. Dizangian B, Ghasemi MR (2015) Ranked-based sensitivity analysis for size optimization of structures. J Mech Des 137:121402-1–121402-10. doi:10.1115/1.4031295

    Article  Google Scholar 

  18. Shobeiri V (2016) The optimal design of structures using ACO and EFG. Eng Comput. doi:10.1007/s00366-016-0443-4

    Google Scholar 

  19. Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015) Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures. Comput Struct 149:1–16. doi:10.1016/j.compstruc.2014.12.003

    Article  Google Scholar 

  20. Safaeian Hamzehkolaei N, Miri M, Rashki M (2015) An enhanced simulation-based design method coupled with meta-heuristic search algorithm for accurate reliability-based design optimization. Eng Comput 1–19. doi:10.1007/s00366-015-0427-9

  21. Fabritius B, Tabor G (2015) Improving the quality of finite volume meshes through genetic optimisation. Eng Comput. doi:10.1007/s00366-015-0423-0

    Google Scholar 

  22. Ghasemi MR, Dizangian B (2010) Size, shape and topology optimization of composite steel box girders using PSO method. ASIAN J Civ Eng (BUILDING HOUSING) 11:699–715

    MATH  Google Scholar 

  23. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82. doi:10.1109/4235.585893

    Article  Google Scholar 

  24. Sonntag RE, Borgnakke C, Van Wylen GJ (2009) Fundamentals of thermodynamics 7th edn. University of Michigan, Willey

  25. Van Wylen GJ, Sonntag RE, Dybbs A (1975) Fundamentals of classical thermodynamics (2nd edition). J Appl Mech 42:522. doi:10.1115/1.3423632

    Article  Google Scholar 

  26. Laurendeau NM (2005) Statistical thermodynamics fundamentals and applications. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  27. Harsha PS (2006) Principles of vapor deposition of thin films. The Boulevard, Oxford

    Google Scholar 

  28. Moore FL (1963) Kinetic theory of gases. Am J Phys 31:213. doi:10.1119/1.1969378

    Article  Google Scholar 

  29. Mirjalili S, Zaiton S, Hashim M et al (2010) A new hybrid PSOGSA algorithm for function optimization. Proc ICCIA 2010 Int Conf Comput Inf Appl 374–377. doi:10.1109/ICCIA.2010.6141614

  30. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, Boston. doi:10.1007/s10589-009-9261-6

    MATH  Google Scholar 

  31. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Neural Networks, 1995. Proceedings, IEEE Int. Conf, vol 4, pp 1942–1948

  32. Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68. doi:10.1177/003754970107600201

    Article  Google Scholar 

  33. Simon D, Member S (2008) Biogeography-based optimization. Evol Comput IEEE Trans 12:702–713. doi:10.1109/TEVC.2008.919004

    Article  Google Scholar 

  34. Wang G-G, Guo L, Wang H et al (2014) Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput Appl 24:853–871. doi:10.1007/s00521-012-1304-8

    Article  Google Scholar 

  35. Patel VK, Savsani VJ (2015) Heat transfer search (HTS): a novel optimization algorithm. Inf Sci (NY) 324:217–246. doi:10.1016/j.ins.2015.06.044

    Article  Google Scholar 

  36. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. Evol Comput IEEE Trans 6:58–73

    Article  Google Scholar 

  37. Coello CAC, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16:193–203

    Article  Google Scholar 

  38. Parsopoulos KE, Vrahatis MN et al (2002) Particle swarm optimization method for constrained optimization problems. Intell Technol Appl New Trends Intell Technol 76:214–220

    MATH  Google Scholar 

  39. Joines J, Houck CR, others (1994) On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA’s. In: Evol. Comput. 1994. IEEE World Congr. Comput. Intell. Proc. First IEEE Conf., pp 579–584

  40. Ahmadi-Nedushan B, Varaee H (2009) Optimal design of reinforced concrete retaining walls using a swarm intelligence technique. In: first Int. Conf. Soft Comput. Technol. Civil, Struct. Environ. Eng. UK, pp 1–12

  41. Rao SS (2009) Engineering optimization: theory and practice. Wiley, New York. doi:10.1002/9780470549124

    Book  Google Scholar 

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

    Article  Google Scholar 

  43. Deb K (1991) Optimal design of a welded beam via genetic algorithms. AIAA J 29:2013–2015. doi:10.2514/3.10834

    Article  Google Scholar 

  44. Coello CAC (2000) Constraint-handling using an evolutionary multiobjective optimization technique. Civ Eng Syst 17:319–346

    Article  Google Scholar 

  45. Mezura-Montes E, Coello CAC (2008) An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 37:443–473

    Article  MathSciNet  MATH  Google Scholar 

  46. 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 

  47. Mirjalili SM, Mirjalili SM, Hatamlou A (2015) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 1–19. doi:10.1007/s00521-015-1870-7

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

    Article  Google Scholar 

  49. Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151–166

    Article  Google Scholar 

  50. Coello CAC, Becerra RL (2004) Efficient evolutionary optimization through the use of a cultural algorithm. Eng Optim 36:219–236

    Article  Google Scholar 

  51. 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 

  52. Zahara E, Kao Y-T (2009) Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained engineering design problems. Expert Syst Appl 36:3880–3886

    Article  Google Scholar 

  53. Lampinen J (2002) A constraint handling approach for the differential evolution algorithm. In: wcci, pp 1468–1473

  54. Coello CAC (2012) Constraint-handling techniques used with evolutionary algorithms. In: Proc. 14th Annu. Conf. companion Genet. Evol. Comput, pp 849–872

  55. Coello CAC, Montes EM (2001) Use of dominance-based tournament selection to handle constraints in genetic algorithms. Intell Eng Syst through Artif Neural Netw 11:177–182

    Google Scholar 

  56. Gao L, Hailu A (2010) Comprehensive learning particle swarm optimizer for constrained mixed-variable optimization problems. Int J Comput Intell Syst 3:832–842

    Article  Google Scholar 

  57. Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798. doi:10.1016/j.compstruc.2004.01.002

    Article  Google Scholar 

  58. Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Des 112:223–229

    Article  Google Scholar 

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

    Article  Google Scholar 

  60. Akhtar S, Tai K, Ray T (2002) A socio-behavioural simulation model for engineering design optimization. Eng Optim 34:341–354. doi:10.1080/03052150212723

    Article  Google Scholar 

  61. Yun YS (2005) Study on Adaptive hybrid genetic algorithm and its applications to engineering design problems. MSc Thesis, Waseda University

  62. Tsai J-F, Li H-L, Hu N-Z (2002) Global optimization for signomial discrete programming problems in engineering design. Eng Optim 34:613–622

    Article  Google Scholar 

  63. Coelho LDS (2010) Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 37:1676–1683

    Article  MathSciNet  Google Scholar 

  64. Cao YJ, Wu QH (1997) Evolutionary programming. In: Evol. Comput. 1997., IEEE Int. Conf., pp 443–446

  65. Deb K (1997) Geneas: A robust optimal design technique for mechanical component design. In: Evol. algorithms Eng. Appl. Springer, Berlin, pp 497–514

  66. Coello CAC (1999) Self-adaptive penalties for GA-based optimization. Evol. Comput. 1999. CEC 99. Proc. 1999 Congr., p 1

  67. Sandgren E (1988) Nonlinear integer and discrete programming in mechanical design. In: Proc. ASME Des. Technol. Conf., pp 95–105

  68. Zhang C, Wang H-P (1993) Mixed-discrete nonlinear optimization with simulated annealing. Eng Optim 21:277–291

    Article  Google Scholar 

  69. Coello CAC, Cortés NC (2004) Hybridizing a genetic algorithm with an artificial immune system for global optimization. Eng Optim 36:607–634

    Article  MathSciNet  Google Scholar 

  70. Homaifar A, Qi CX, Lai SH (1994) Constrained optimization via genetic algorithms. Simulation 62:242–253

    Article  Google Scholar 

  71. Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53:1168–1183

    Article  Google Scholar 

  72. Mirjalili S (2015) Moth-flame optimization algorithm : a novel nature-inspired heuristic paradigm. Knowl Based Syst. doi:10.1016/j.knosys.2015.07.006

    Google Scholar 

  73. Michalewicz Z, Attia N (1994) Evolutionary optimization of constrained problems. In: Proc. 3rd Annu. Conf. Evol. Program, pp 98–108

  74. Gandomi AH, Yang X-S, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22:1239–1255

    Article  Google Scholar 

  75. Ben Hadj-Alouane A, Bean JC (1997) A genetic algorithm for the multiple-choice integer program. Oper Res 45:92–101

    Article  MathSciNet  MATH  Google Scholar 

  76. Fu J-F, Fenton RG, Cleghorn WL (1991) A mixed integer-discrete-continuous programming method and its application to engineering design optimization. Eng Optim 17:263–280

    Article  Google Scholar 

  77. Li H-L, Chou C-T (1993) A global approach for nonlinear mixed discrete programming in design optimization. Eng Optim 22:109–122

    Article  Google Scholar 

  78. Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput J 13:2592–2612. doi:10.1016/j.asoc.2012.11.026

    Article  Google Scholar 

  79. Cai J, Thierauf G (1997) Evolution strategies in engineering optimization. Eng Optim 29:177–199

    Article  Google Scholar 

  80. He S, Prempain E, Wu QH (2004) An improved particle swarm optimizer for mechanical design optimization problems. Eng Optim 36:585–605

    Article  MathSciNet  Google Scholar 

  81. Cao YJ, Wu QH (1999) A mixed variable evolutionary programming for optimisation of mechanical design. Eng Intell Syst Electr Eng Commun 7:77–82

    Google Scholar 

  82. Hu X, Eberhart RC, Shi Y (2003) Engineering optimization with particle swarm. In: Swarm Intell. Symp. 2003. SIS’03. Proc. 2003 IEEE, pp 53–57

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

    MathSciNet  MATH  Google Scholar 

  84. Litinetski VV, Abramzon BM (1998) MARS-A multistart adaptive random search method for global constrained optimization in engineering applications. Eng Optim 30:125–154

    Article  Google Scholar 

  85. Wu S-J, Chow P-T (1995) Genetic algorithms for nonlinear mixed discrete-integer optimization problems via meta-genetic parameter optimization. Eng Optim A35(24):137–159

    Article  Google Scholar 

  86. Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194:3902–3933. doi:10.1016/j.cma.2004.09.007

    Article  MATH  Google Scholar 

  87. Cagnina LC, Esquivel SC, Coello CAC (2008) Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica 32:319–326

    MATH  Google Scholar 

  88. Hossein A, Yang GX, Gandomi AH et al (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35. doi:10.1007/s00366-011-0241-y

    Article  Google Scholar 

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

    Article  Google Scholar 

  90. Mezura-Montes E, Coello CAC, Velázquez-Reyes J, Muñoz-Dávila L (2007) Multiple trial vectors in differential evolution for engineering design. Eng Optim 39:567–589

    Article  MathSciNet  Google Scholar 

  91. Parsopoulos KE, Vrahatis MN (2005) Unified particle swarm optimization for solving constrained engineering optimization problems. In: Adv. Nat. Comput. Springer, Berlin, pp 582–591

  92. Shih CJ, Lai TK (1995) Mixed-discrete fuzzy programming for nonlinear engineering optimization. Eng Optim 23:187–199

    Article  Google Scholar 

  93. Li H-L, Chang C-T (1998) An approximate approach of global optimization for polynomial programming problems. Eur J Oper Res 107:625–632

    Article  MATH  Google Scholar 

  94. Belegundu AD, Arora JS (1985) A study of mathematical programming methods for structural optimization. Part I: theory. Int J Numer Methods Eng 21:1583–1599

    Article  MathSciNet  MATH  Google Scholar 

  95. Arora J (2004) Introduction to optimum design. Academic Press, Cambridge

    Google Scholar 

  96. Krohling R, dos Santos Coelho LDS et al (2006) Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems. Syst Man Cybern Part B Cybern IEEE Trans 36:1407–1416

    Article  Google Scholar 

  97. Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188:1567–1579. doi:10.1016/j.amc.2006.11.033

    MathSciNet  MATH  Google Scholar 

  98. Kaveh A, Mahdavi VRR (2014) Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27. doi:10.1016/j.compstruc.2014.04.005

    Article  Google Scholar 

  99. He Q, Wang L (2007) A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput 186:1407–1422

    MathSciNet  MATH  Google Scholar 

  100. Wang L, Li L (2010) An effective differential evolution with level comparison for constrained engineering design. Struct Multidiscip Optim 41:947–963

    Article  Google Scholar 

  101. Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci (NY) 178:3043–3074

    Article  Google Scholar 

  102. Wang Y, Cai Z, Zhou Y, Fan Z (2009) Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique. Struct Multidiscip Optim 37:395–413

    Article  Google Scholar 

  103. Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization. In: Probl. LNCS Adv. Soft Comput. Found. Fuzzy Log. Soft Comput. Springer, IFSA (2007). Citeseer, pp 789–798

  104. Mezura-Montes E, Coello CAC (2005) Useful infeasible solutions in engineering optimization with evolutionary algorithms. In: MICAI 2005 Adv. Artif. Intell. Springer, Berlin, pp 652–662

  105. Chickermane H, Gea HC (1996) Structural optimization using a new local approximation method. Int J Numer Methods Eng 39:829–846

    Article  MathSciNet  MATH  Google Scholar 

  106. Svanberg K (1987) The method of moving asymptotes- a new method for structural optimization. Int J Numer Methods Eng 24:359–373

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  108. Deb K, Goyal M (1996) A combined genetic adaptive search (GeneAS) for engineering design. Comput Sci Inform 26:30–45

    Google Scholar 

  109. Sharma TK, Pant M, Singh VP (2012) Improved local search in artificial bee colony using golden section search. arXiv:1210.6128 (arXiv Prepr)

  110. Schmit LA, Farshi B (1974) Some approximation concepts for structural synthesis. AIAA J 12:692–699

    Article  Google Scholar 

  111. Schmit LA, Miura H (1976) Approximation concepts for efficient structural synthesis. US National Aeronautics and Space Administration

  112. Li LJ, Huang ZB, Liu F (2009) A heuristic particle swarm optimization method for truss structures with discrete variables. Comput Struct 87:435–443. doi:10.1016/j.compstruc.2009.01.004

    Article  Google Scholar 

  113. Kaveh A, Rahami H (2006) Analysis, design and optimization of structures using force method and genetic algorithm. Int J Numer Methods Eng 65:1570–1584

    Article  MathSciNet  MATH  Google Scholar 

  114. Camp C, Pezeshk S, Cao G (1998) Optimized design of two-dimensional structures using a genetic algorithm. J Struct Eng 124:551–559. doi:10.1061/(ASCE)0733-9445(1998)124:5(551)

    Article  Google Scholar 

  115. Farshi B, Alinia-Ziazi A (2010) Sizing optimization of truss structures by method of centers and force formulation. Int J Solids Struct 47:2508–2524. doi:10.1016/j.ijsolstr.2010.05.009

    Article  MATH  Google Scholar 

  116. Rizzi P (1976) Optimization of multi-constrained structures based on optimality criteria. Conf. AIAA/ASME/SAE 17th Struct. Struct. Dyn. Mater., King Prussia, PA

  117. John KV, Ramakrishnan CV, Sharma KG (1987) Minimum weight design of trusses using improved move limit method of sequential linear programming. Comput Struct 27:583–591

    Article  MATH  Google Scholar 

  118. Kaveh A, Talatahari S (2009) Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Comput Struct 87:267–283. doi:10.1016/j.compstruc.2009.01.003

    Article  Google Scholar 

  119. Camp CV, Bichon BJ (2004) Design of space trusses using ant colony optimization. J Struct Eng 130:741–751

    Article  Google Scholar 

  120. Kaveh A, Talatahari S (2009) Size optimization of space trusses using Big Bang-Big Crunch algorithm. Comput Struct 87:1129–1140. doi:10.1016/j.compstruc.2009.04.011

    Article  Google Scholar 

  121. Schutte JF, Groenwold AA (2003) Sizing design of truss structures using particle swarms. Struct Multidiscip Optim 25:261–269

    Article  Google Scholar 

  122. Kaveh A, Talatahari S (2010) Optimal design of skeletal structures via the charged system search algorithm. Struct Multidiscip Optim 41:893–911

    Article  Google Scholar 

  123. Kaveh A, Sheikholeslami R, Talatahari S, Keshvari-Ilkhichi M (2014) Chaotic swarming of particles: a new method for size optimization of truss structures. Adv Eng Softw 67:136–147. doi:10.1016/j.advengsoft.2013.09.006

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran

    Hesam Varaee & Mohammad Reza Ghasemi

Authors
  1. Hesam Varaee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Reza Ghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Reza Ghasemi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Varaee, H., Ghasemi, M.R. Engineering optimization based on ideal gas molecular movement algorithm. Engineering with Computers 33, 71–93 (2017). https://doi.org/10.1007/s00366-016-0457-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-016-0457-y

Keywords

Search

Navigation