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Colliding Bodies Optimization

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Advances in Metaheuristic Algorithms for Optimal Design of Structures

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Abstract

This chapter presents a novel efficient metaheuristic optimization algorithm called colliding bodies optimization (CBO) for optimization. This algorithm is based on one-dimensional collisions between bodies, with each agent solution being considered as the massed object or body. After a collision of two moving bodies having specified masses and velocities, these bodies are separated with new velocities. This collision causes the agents to move toward better positions in the search space. CBO utilizes a simple formulation to find minimum or maximum of functions; also it is independent of parameters [1].

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References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Tolman RC (1979) The principles of statistical mechanics. Clarendon Press, Oxford (Reissued)

    MATH  Google Scholar 

  4. Tsoulos IG (2008) Modifications of real code genetic algorithm for global optimization. Appl Math Comput 203:598–607

    MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Montes EM, 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 

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

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Coello CAC, Montes EM (1992) Constraint-handling in genetic algorithms through the use of dominance-based tournament. IEEE Trans Reliab 41(4):576–582

    Article  Google Scholar 

  12. 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 

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

    Article  Google Scholar 

  14. Deb K, Gene AS (1997) A robust optimal design technique for mechanical component design. In: Dasgupta D, Michalewicz Z (eds) Evolutionary algorithms in engineering applications. Springer, Berlin, pp 497–514

    Chapter  Google Scholar 

  15. Belegundu AD (1982) A study of mathematical programming methods for structural optimization. Ph.D. thesis, Department of Civil and Environmental Engineering, University of Iowa, Iowa, USA

    Google Scholar 

  16. Arora JS (1989) Introduction to optimum design. McGraw-Hill, New York

    Google Scholar 

  17. Soh CK, Yang J (1996) Fuzzy controlled genetic algorithm search for shape optimization. J Comput Civil Eng ASCE 10:143–150

    Article  Google Scholar 

  18. Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798

    Article  Google Scholar 

  19. Kaveh A, Khayatazad M (2012) A novel meta-heuristic method: ray optimization. Comput Struct 112–113:283–294

    Article  Google Scholar 

  20. American Institute of Steel Construction (AISC) (1989) Manual of steel construction—allowable stress design, 9th edn. AISC, Chicago, IL

    Google Scholar 

  21. Rajeev S, Krishnamoorthy CS (1992) Discrete optimization of structures using genetic algorithms. Struct Eng ASCE 118:1233–1250

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Erbatur F, Hasançebi O, Tütüncü I, Kiliç H (2000) Optimal design of planar and space structures with genetic algorithms. Comput Struct 75:209–224

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Perez RE, Behdinan K (2007) Particle swarm approach for structural design optimization. Comput Struct 85:1579–1588

    Article  Google Scholar 

  26. Camp CV (2007) Design of space trusses using Big Bang–Big Crunch optimization. J Struct Eng ASCE 133:999–1008

    Article  Google Scholar 

  27. Kaveh A, Talatahari S (2009) A particle swarm ant colony optimization for truss structures with discrete variables. J Constr Steel Res 65:1558–1568

    Article  Google Scholar 

  28. Hasançebi O, Çarbas S, Dogan E, Erdal F, Saka MP (2009) Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures. Comput Struct 87:284–302

    Article  Google Scholar 

  29. Lingyun W, Mei Z, Guangming W, Guang M (2005) Truss optimization on shape and sizing with frequency constraints based on genetic algorithm. J Comput Mech 25:361–368

    Article  MATH  Google Scholar 

  30. Gomes MH (2011) Truss optimization with dynamic constraints using a particle swarm algorithm. Expert Syst Appl 38:957–968

    Article  Google Scholar 

  31. Kaveh A, Zolghadr A (2011) Shape and size optimization of truss structures with frequency constraints using enhanced charged system search algorithm. Asian J Civil Eng 12:487–509

    Google Scholar 

  32. Makiabadi MH, Baghlani A, Rahnema H, Hadianfard MA (2013) Optimal design of truss bridges using teaching–learning-base optimization algorithm. Int J Optim Civil Eng 3(3):499–510

    Google Scholar 

  33. AustRoads. 92 (1992) Austroads bridge design code. Australasian Railway Association, NSW

    Google Scholar 

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Authors and Affiliations

  1. School of Civil Engineering, Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Tehran, Iran

    A. Kaveh

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  1. A. Kaveh
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Kaveh, A. (2017). Colliding Bodies Optimization. In: Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer, Cham. https://doi.org/10.1007/978-3-319-46173-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-46173-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46172-4

  • Online ISBN: 978-3-319-46173-1

  • eBook Packages: EngineeringEngineering (R0)

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