Improved Hill Climbing and Simulated Annealing Algorithms for Size Optimization of Trusses
Truss optimization problem has been vastly studied
during the past 30 years and many different methods have been
proposed for this problem. Even though most of these methods
assume that the design variables are continuously valued, in reality,
the design variables of optimization problems such as cross-sectional
areas are discretely valued. In this paper, an improved hill climbing
and an improved simulated annealing algorithm have been proposed
to solve the truss optimization problem with discrete values for crosssectional
areas. Obtained results have been compared to other
methods in the literature and the comparison represents that the
proposed methods can be used more efficiently than other proposed
methods
[1] C. A. Coello, "Theoretical and numerical constraint-handling techniques
used with evolutionary algorithms: A survey of the state of the art",
Computer Methods in Applied Mechanics and Engineering, vol. 191,
2002, pp. 1245-87.
[2] L. J. Le, Z. B. Huang, and F. Liu, "A heuristic particle swarm
optimization method for truss structures with discrete variables",
Journal of Computers and Structures, vol. 87, 2009, pp. 435-443.
[3] S. Rajeev, and C. S. Krishnamoorthy, "Discrete optimization of
structures using genetic algorithms", Journal of Structural Engineering,
ASCE, vol. 118, 1992, pp. 1233-1250.
[4] S. J. Wu, and P. T. Chow, "Steady-state genetic algorithms for discrete
optimization of trusses", Journal of Computer Structures, vol. 56, 1995,
pp. 979-991.
[5] J. Cheng, "Optimum design of steel truss arch bridges using a hybrid
genetic algorithm", Journal of Constructional Steel Research, vol. 66,
2010, pp. 1011-1017.
[6] L. J. Li, F. M. Ren, F. Liu, and Q. H. Wu. "An improved particle swarm
optimization method and its application in civil engineering.", The
eighth international conference on computation and structures
technology, 2006.
[7] M. P. Saka, "Optimum design of steel frames using stochastic search
techniques based on natural phenomena: a review.", Topping BHV,
editor. Civil engineering computations: tools and techniques, UK: Saxe-
Coburg Publications, 2007, pp. 105-147.
[8] L. Lamberty, "An efficient simulated annealing algorithm for design
optimization of truss structures", Journal of Computers and Structures,
vol. 86, 2008, pp. 1936-1953.
[9] M. Assari, B. Hassani, and M. Kazemi Torbaghan, "An improved big
bang - big crunch algorithm for size optimization of trusses", 9th
International Congress on Civil Engineering, Isfahan University of
Technology (IUT), Isfahan, Iran, 2012.
[10] http://en.wikipedia.org/wiki/Hill_climbing
[11] http://en.wikipedia.org/wiki/Simulated_annealing
[12] S. Jang, W. Jung, B. Kwon, and Y. Choi, "A Study on Structural Design
Optimization using TMSA in Discrete Searching Space", World
Academy of Science, Engineering and Technology, vol. 56, 2009, pp.
626-630.
[1] C. A. Coello, "Theoretical and numerical constraint-handling techniques
used with evolutionary algorithms: A survey of the state of the art",
Computer Methods in Applied Mechanics and Engineering, vol. 191,
2002, pp. 1245-87.
[2] L. J. Le, Z. B. Huang, and F. Liu, "A heuristic particle swarm
optimization method for truss structures with discrete variables",
Journal of Computers and Structures, vol. 87, 2009, pp. 435-443.
[3] S. Rajeev, and C. S. Krishnamoorthy, "Discrete optimization of
structures using genetic algorithms", Journal of Structural Engineering,
ASCE, vol. 118, 1992, pp. 1233-1250.
[4] S. J. Wu, and P. T. Chow, "Steady-state genetic algorithms for discrete
optimization of trusses", Journal of Computer Structures, vol. 56, 1995,
pp. 979-991.
[5] J. Cheng, "Optimum design of steel truss arch bridges using a hybrid
genetic algorithm", Journal of Constructional Steel Research, vol. 66,
2010, pp. 1011-1017.
[6] L. J. Li, F. M. Ren, F. Liu, and Q. H. Wu. "An improved particle swarm
optimization method and its application in civil engineering.", The
eighth international conference on computation and structures
technology, 2006.
[7] M. P. Saka, "Optimum design of steel frames using stochastic search
techniques based on natural phenomena: a review.", Topping BHV,
editor. Civil engineering computations: tools and techniques, UK: Saxe-
Coburg Publications, 2007, pp. 105-147.
[8] L. Lamberty, "An efficient simulated annealing algorithm for design
optimization of truss structures", Journal of Computers and Structures,
vol. 86, 2008, pp. 1936-1953.
[9] M. Assari, B. Hassani, and M. Kazemi Torbaghan, "An improved big
bang - big crunch algorithm for size optimization of trusses", 9th
International Congress on Civil Engineering, Isfahan University of
Technology (IUT), Isfahan, Iran, 2012.
[10] http://en.wikipedia.org/wiki/Hill_climbing
[11] http://en.wikipedia.org/wiki/Simulated_annealing
[12] S. Jang, W. Jung, B. Kwon, and Y. Choi, "A Study on Structural Design
Optimization using TMSA in Discrete Searching Space", World
Academy of Science, Engineering and Technology, vol. 56, 2009, pp.
626-630.
@article{"International Journal of Architectural, Civil and Construction Sciences:56436", author = "Morteza Kazemi Torbaghan and Seyed Mehran Kazemi and Rahele Zhiani and Fakhriye Hamed", title = "Improved Hill Climbing and Simulated Annealing Algorithms for Size Optimization of Trusses", abstract = "Truss optimization problem has been vastly studied
during the past 30 years and many different methods have been
proposed for this problem. Even though most of these methods
assume that the design variables are continuously valued, in reality,
the design variables of optimization problems such as cross-sectional
areas are discretely valued. In this paper, an improved hill climbing
and an improved simulated annealing algorithm have been proposed
to solve the truss optimization problem with discrete values for crosssectional
areas. Obtained results have been compared to other
methods in the literature and the comparison represents that the
proposed methods can be used more efficiently than other proposed
methods", keywords = "Size Optimization of Trusses, Hill Climbing,
Simulated Annealing.", volume = "7", number = "2", pages = "131-4", }