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Ray Optimization Algorithm

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Advances in Metaheuristic Algorithms for Optimal Design of Structures

Abstract

In this chapter a newly developed metaheuristic method, so-called Ray Optimization, is presented. Similar to other multi-agent methods, Ray Optimization has a number of particles consisting of the variables of the problem. These agents are considered as rays of light. Based on the Snell’s light refraction law when light travels from a lighter medium to a darker medium, it refracts and its direction changes. This behavior helps the agents to explore the search space in early stages of the optimization process and to make them converge in the final stages. This law is the main tool of the Ray Optimization algorithm. This chapter consists of three parts.

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References

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

    Article  Google Scholar 

  2. Kaveh A, Khayatazad M (2013) Ray optimization for size and shape optimization of truss structures. Comput Struct 117:82–94

    Article  Google Scholar 

  3. Kaveh A, Ilchi Ghazaan M, Bakhshpoori T (2013) An improved ray optimization algorithm for design of truss structures. Period Polytech 57(2):97–112

    Google Scholar 

  4. Laval PB (2003) Mathematics for computer graphics-ray tracing III. Kennesaw-MATH–4490, Kennesaw State University, Kennesaw, GA

    Google Scholar 

  5. 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

    Article  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  7. 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, IA

    Google Scholar 

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

    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 98:1021–1925

    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. Gil L, Andreu A (2003) Shape and cross-section optimization of a truss structure. Comput Struct 79:681–689

    Article  Google Scholar 

  12. Wang D, Zhang WH, Jiang JS (2004) Truss optimization on shape and sizing with frequency constraints. AIAA J 42:1452–1456

    Google Scholar 

  13. 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  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  17. 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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Kaveh A, Talatahari S (2009) Size optimization of space trusses using Big Bang-Big Crunch algorithm. Comput Struct 87:1129–1140

    Article  Google Scholar 

  21. Kaveh A, Talatahari S (2010) Optimum design of skeletal structures using imperialist competitive algorithm. Comput Struct 88:1220–1229

    Article  Google Scholar 

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

    Article  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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© 2014 Springer International Publishing Switzerland

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Kaveh, A. (2014). Ray Optimization Algorithm. In: Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer, Cham. https://doi.org/10.1007/978-3-319-05549-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-05549-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05548-0

  • Online ISBN: 978-3-319-05549-7

  • eBook Packages: EngineeringEngineering (R0)

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