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Natural Forest Regeneration Algorithm: A New Meta-Heuristic

  • H. Moez
  • A. Kaveh
  • N. Taghizadieh
Research Paper

Abstract

A new meta-heuristic algorithm is presented for optimum design of engineering problems. This algorithm is inspired by the natural behavior of the forests against the rapidly changing environment. This phenomenon is combined with natural regeneration of forests, and a simple but efficient optimization technique is developed which is called natural forest regeneration (NFR). Some well-studied benchmark optimization problems are investigated, and the results of the NFR are compared with those of some previously published algorithms.

Keywords

Nature-inspired meta-heuristic optimization Natural forest regeneration algorithm Mathematical optimization problems Structural design problems 

References

  1. Aitken SN, Yeaman S, Holliday JA, Wang T, Curtis-McLane S (2008) Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol Appl 1(1):95–111CrossRefGoogle Scholar
  2. Arora J (2004) Introduction to optimum design. Academic Press, LondonGoogle Scholar
  3. Belegundu AD (1983) Study of mathematical programming methods for structural optimization. Diss Abstr Int Part B Sci Eng 43(12):1983Google Scholar
  4. Belegundu AD, Arora JS (1985) A study of mathematical programmingmethods for structural optimization. Part II: numerical results. Int J Numer Methods Eng 21(9):1601–1623MathSciNetCrossRefzbMATHGoogle Scholar
  5. Camp CV (2007) Design of space trusses using big bang–big crunch optimization. J Struct Eng ASCE 133(7):999–1008CrossRefGoogle Scholar
  6. Camp CV, Bichon BJ (2004) Design of space trusses using ant colony optimization. J Struct Eng 130(5):741–751CrossRefGoogle Scholar
  7. Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127CrossRefGoogle Scholar
  8. Coello CAC, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16(3):193–203CrossRefGoogle Scholar
  9. Dagum L, Enon R (1998) OpenMP: an industry standard API for shared-memory programming. Comput Sci Eng IEEE 5(1):46–55CrossRefGoogle Scholar
  10. Deb K (1991) Optimal design of a welded beam via genetic algorithms. AIAA J 29(11):2013–2015CrossRefGoogle Scholar
  11. Dorigo M (1992) Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano, ItalyGoogle Scholar
  12. Erbatur F, Hasancebi O, Tütüncü I, Kılıç H (2000) Optimal design of planar and space structures with genetic algorithms. Comput Struct 75(2):209–224CrossRefGoogle Scholar
  13. Erol OK, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37(2):106–111CrossRefGoogle Scholar
  14. Geem ZW, Kim JH, Loganathan JV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68CrossRefGoogle Scholar
  15. Gholizadeh S (2010) Optimum design of structures by an improved particle swarm algorithm. Asian J Civ Eng 11:777–793Google Scholar
  16. Gholizadeh S, Poorhoseini H (2015) Optimum design of steel frame structures by a modified Dolphin echolocation algorithm. Struct Eng Mech 55(3):535–554CrossRefGoogle Scholar
  17. Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13(5):533–549MathSciNetCrossRefzbMATHGoogle Scholar
  18. He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99CrossRefGoogle Scholar
  19. Herrera CM, Jordano P (1981) Prunus mahaleb and birds: the high-efficiency seed dispersal system of a temperate fruiting tree. Ecol Monogr 51(2):203–218CrossRefGoogle Scholar
  20. Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann ArborzbMATHGoogle Scholar
  21. Janzen DH (1975) Ecology of plants in the tropics. Edward Arnold, LondonGoogle Scholar
  22. Kannan B, Kramer SN (1994) An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Eng ASCE 116(2):405–411Google Scholar
  23. Kaveh A, Farhoudi N (2013) A new optimization method: dolphin echolocation. Adv Eng Softw 59:53–70CrossRefGoogle Scholar
  24. Kaveh A, Ilchi Ghazaan M (2015) Layout and size optimization of trusses with natural frequency constraints using improved ray optimization algorithm. Iran J Sci Technol 39(C2):395–408Google Scholar
  25. Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112:283–294CrossRefGoogle Scholar
  26. Kaveh A, Khayatazad M (2014) Optimal design of cantilever retaining walls using ray optimization method. Iran J Sci Technol 38(C1):261–274Google Scholar
  27. Kaveh A, Mahdavi VR (2014a) Colliding bodies optimization method for optimum design of truss structures with continuous variables. Adv Eng Softw 70:1–12CrossRefGoogle Scholar
  28. Kaveh A, Mahdavi VR (2014b) Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27CrossRefGoogle Scholar
  29. Kaveh A, Talatahari S (2009a) Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Comput Struct 87(5):267–283CrossRefGoogle Scholar
  30. Kaveh A, Talatahari S (2009b) Size optimization of space trusses using big bang–big crunch algorithm. Comput Struct 87(17):1129–1140CrossRefGoogle Scholar
  31. Kaveh A, Talatahari S (2010a) A novel heuristic optimization method: charged system search. Acta Mech 213(3–4):267–289CrossRefzbMATHGoogle Scholar
  32. Kaveh A, Talatahari S (2010b) Optimal design of skeletal structures via the charged system search algorithm. Struct Multidiscip Optim 41(6):893–911CrossRefGoogle Scholar
  33. Kaveh A, Motie Share MA, Moslehi M (2013) Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mech 224(1):85–107CrossRefzbMATHGoogle Scholar
  34. Kaveh A, Talatahari S, Sheikholeslami R, Keshvari M (2014) Chaotic swarming of particles: a new method for size optimization of truss structures. Adv Eng Softw 67:136–147CrossRefGoogle Scholar
  35. Kennedy J (2010) Particle swarm optimization. Encyclopedia of machine learning. Springer, Berlin, pp 760–766Google Scholar
  36. 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(36):3902–3933CrossRefzbMATHGoogle Scholar
  37. Melillo JM et al (1993) Global climate change and terrestrial net primary production. Nature 363(6426):234–240CrossRefGoogle Scholar
  38. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98CrossRefGoogle Scholar
  39. Mirjalili S, Mirjalili SM, Hatamlou A (2015) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513CrossRefGoogle Scholar
  40. Montes EM, Coello CAC (2008) An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 37(4):443–473MathSciNetCrossRefzbMATHGoogle Scholar
  41. Perez R, Behdinan K (2007) Particle swarm approach for structural design optimization. Comput Struct 85(19):1579–1588CrossRefGoogle Scholar
  42. Press WH (2007) Numerical recipes: the art of scientific computing, 3rd edn. Cambridge University Press, CambridgezbMATHGoogle Scholar
  43. Ragsdell K, Phillips D (1976) Optimal design of a class of welded structures using geometric programming. J Manuf Sci Eng 98(3):1021–1025Google Scholar
  44. Robbins H, Monro S (1951) A stochastic approximation method. Ann Math Stat 22:400–407MathSciNetCrossRefzbMATHGoogle Scholar
  45. Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Eng ASCE 112(2):223–229Google Scholar
  46. Schutte J, Groenwold A (2003) Sizing design of truss structures using particle swarms. Struct Multidiscip Optim 25(4):261–269CrossRefGoogle Scholar
  47. Tsoulos IG (2008) Modifications of real code genetic algorithm for global optimization. Appl Math Comput 203(2):598–607MathSciNetzbMATHGoogle Scholar
  48. Wu S-J, Chow P-T (1995) Integrated discrete and configuration optimization of trusses using genetic algorithms. Comput Struct 55(4):695–702CrossRefzbMATHGoogle Scholar

Copyright information

© Shiraz University 2016

Authors and Affiliations

  1. 1.The Faculty of Civil EngineeringUniversity of TabrizTabrizIran
  2. 2.Centre of Excellence for Fundamental Studies in Structural EngineeringIran University of Science and TechnologyNarmakIran

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