Optimal design of skeletal structures via the charged system search algorithm

Research Paper


A new meta-heuristic optimization algorithm is presented for design of skeletal structures. The algorithm is inspired by the Coulomb and Gauss’s laws of electrostatics in physics, and it is called charged system search (CSS). CSS utilizes a number of charged particle (CP) which affects each other based on their fitness values and separation distances considering the governing laws of Coulomb and Gauss from electrical physics and the governing laws of motion from the Newtonian mechanics. Some truss and frame structures are optimized with the CSS algorithm. Comparison of the results of the CSS with those of other meta-heuristic algorithms shows the robustness of the new algorithm.


Heuristic optimization algorithm Charged system search Coulomb’s law Gauss’s law Newtonian mechanics Optimal design of skeletal structures 


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  1. American Institute of Steel Construction (AISC) (1989) Manual of steel construction-allowable stress design, 9th edn. American Institute of Steel Construction, ChicagoGoogle Scholar
  2. ASCE 7-05 (2005) Minimum design loads for building and other structuresGoogle Scholar
  3. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Sys Man Cybern, B 26(1):29–41CrossRefGoogle Scholar
  4. Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, Nagoya, JapanGoogle Scholar
  5. Erol OK, Eksin I (2006) New optimization method: Big Bang–Big Crunch. Adv Eng Softw 37:106–111CrossRefGoogle Scholar
  6. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, ChichesterMATHGoogle Scholar
  7. Geem ZW (2000) Optimal design of water distribution networks using harmony search. PhD thesis, Korea University, South KoreaGoogle Scholar
  8. Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, BostonMATHGoogle Scholar
  9. Glover F (1977) Heuristic for integer programming using surrogate constraints. Decis Sci 8(1):156–166CrossRefGoogle Scholar
  10. Halliday D, Resnick R, Walker J (2008) Fundamentals of physics, 8th edn. Wiley, New YorkGoogle Scholar
  11. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  12. Kaveh A, Farahmand Azar B, Talatahari S (2008) Ant colony optimization for design of space trusses. Int J Space Struct 23(3):167–181CrossRefGoogle Scholar
  13. Kaveh A, Talatahari S (2009a) An improved ant colony optimization for constrained engineering design problems. Eng Comput (in press)Google Scholar
  14. Kaveh A, Talatahari S (2009b) A novel heuristic optimization method: charged system search. Acta Mech. doi:10.1007/s00707-009-0270-4 Google Scholar
  15. Kaveh A, Talatahari S (2009c) Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Comput Struct 87(5–6):267–283CrossRefGoogle Scholar
  16. Kaveh A, Talatahari S (2009d) A particle swarm ant colony optimization for truss structures with discrete variables. J Constr Steel Res 65(8–9):1558–1568CrossRefGoogle Scholar
  17. Kaveh A, Talatahari S (2009e) Size optimization of space trusses using Big Bang–Big Crunch algorithm. Comput Struct 87:1129–1140CrossRefGoogle Scholar
  18. Kirkpatrick S, Gelatt C, Vecchi M (1983) Optimization by simulated annealing. Science 220(4598):671–680CrossRefMathSciNetGoogle Scholar
  19. Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798CrossRefGoogle Scholar
  20. Rajeev S, Krishnamoorthy CS (1992) Discrete optimization of structures using genetic algorithms. J Struct Eng ASCE 118(5):1233–1250CrossRefGoogle Scholar
  21. Saka MP, Hasançebi O (2009) Design code optimization of steel structures using adaptive harmony search algorithm, chapter 3. In: Geem ZW (ed) Harmony search algorithms for structural design. Springer, BerlinGoogle Scholar
  22. Schutte JJ, Groenwold AA (2003) Sizing design of truss structures using particle swarms. Struct Multidisc Optim 25:261–269CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Centre of Excellence for Fundamental Studies in Structural EngineeringIran University of Science and TechnologyTehran-16Iran
  2. 2.Department of Civil EngineeringUniversity of TabrizTabrizIran

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