Natural Forest Regeneration Algorithm: A New Meta-Heuristic | Iranian Journal of Science and Technology, Transactions of Civil Engineering
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Natural Forest Regeneration Algorithm: A New Meta-Heuristic

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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.

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References

  • 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):95–111

    Article  Google Scholar 

  • Arora J (2004) Introduction to optimum design. Academic Press, London

    Google Scholar 

  • Belegundu AD (1983) Study of mathematical programming methods for structural optimization. Diss Abstr Int Part B Sci Eng 43(12):1983

    Google Scholar 

  • Belegundu AD, Arora JS (1985) A study of mathematical programmingmethods for structural optimization. Part II: numerical results. Int J Numer Methods Eng 21(9):1601–1623

    Article  MathSciNet  MATH  Google Scholar 

  • Camp CV (2007) Design of space trusses using big bang–big crunch optimization. J Struct Eng ASCE 133(7):999–1008

    Article  Google Scholar 

  • Camp CV, Bichon BJ (2004) Design of space trusses using ant colony optimization. J Struct Eng 130(5):741–751

    Article  Google Scholar 

  • Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127

    Article  Google Scholar 

  • Coello CAC, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16(3):193–203

    Article  Google Scholar 

  • Dagum L, Enon R (1998) OpenMP: an industry standard API for shared-memory programming. Comput Sci Eng IEEE 5(1):46–55

    Article  Google Scholar 

  • Deb K (1991) Optimal design of a welded beam via genetic algorithms. AIAA J 29(11):2013–2015

    Article  Google Scholar 

  • Dorigo M (1992) Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano, Italy

  • Erbatur F, Hasancebi O, Tütüncü I, Kılıç H (2000) Optimal design of planar and space structures with genetic algorithms. Comput Struct 75(2):209–224

    Article  Google Scholar 

  • Erol OK, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37(2):106–111

    Article  Google Scholar 

  • Geem ZW, Kim JH, Loganathan JV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  • Gholizadeh S (2010) Optimum design of structures by an improved particle swarm algorithm. Asian J Civ Eng 11:777–793

    Google Scholar 

  • Gholizadeh S, Poorhoseini H (2015) Optimum design of steel frame structures by a modified Dolphin echolocation algorithm. Struct Eng Mech 55(3):535–554

    Article  Google Scholar 

  • Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13(5):533–549

    Article  MathSciNet  MATH  Google Scholar 

  • He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99

    Article  Google Scholar 

  • Herrera CM, Jordano P (1981) Prunus mahaleb and birds: the high-efficiency seed dispersal system of a temperate fruiting tree. Ecol Monogr 51(2):203–218

    Article  Google Scholar 

  • 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 Arbor

    MATH  Google Scholar 

  • Janzen DH (1975) Ecology of plants in the tropics. Edward Arnold, London

    Google Scholar 

  • 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):405–411

    Google Scholar 

  • Kaveh A, Farhoudi N (2013) A new optimization method: dolphin echolocation. Adv Eng Softw 59:53–70

    Article  Google Scholar 

  • 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):395–408

    Google Scholar 

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

    Article  Google Scholar 

  • Kaveh A, Khayatazad M (2014) Optimal design of cantilever retaining walls using ray optimization method. Iran J Sci Technol 38(C1):261–274

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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):267–283

    Article  Google Scholar 

  • Kaveh A, Talatahari S (2009b) Size optimization of space trusses using big bang–big crunch algorithm. Comput Struct 87(17):1129–1140

    Article  Google Scholar 

  • Kaveh A, Talatahari S (2010a) A novel heuristic optimization method: charged system search. Acta Mech 213(3–4):267–289

    Article  MATH  Google Scholar 

  • Kaveh A, Talatahari S (2010b) Optimal design of skeletal structures via the charged system search algorithm. Struct Multidiscip Optim 41(6):893–911

    Article  Google Scholar 

  • Kaveh A, Motie Share MA, Moslehi M (2013) Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mech 224(1):85–107

    Article  MATH  Google Scholar 

  • 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 67:136–147

    Article  Google Scholar 

  • Kennedy J (2010) Particle swarm optimization. Encyclopedia of machine learning. Springer, Berlin, pp 760–766

    Google Scholar 

  • 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):3902–3933

    Article  MATH  Google Scholar 

  • Melillo JM et al (1993) Global climate change and terrestrial net primary production. Nature 363(6426):234–240

    Article  Google Scholar 

  • Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Hatamlou A (2015) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Article  Google Scholar 

  • 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):443–473

    Article  MathSciNet  MATH  Google Scholar 

  • Perez R, Behdinan K (2007) Particle swarm approach for structural design optimization. Comput Struct 85(19):1579–1588

    Article  Google Scholar 

  • Press WH (2007) Numerical recipes: the art of scientific computing, 3rd edn. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  • Ragsdell K, Phillips D (1976) Optimal design of a class of welded structures using geometric programming. J Manuf Sci Eng 98(3):1021–1025

    Google Scholar 

  • Robbins H, Monro S (1951) A stochastic approximation method. Ann Math Stat 22:400–407

    Article  MathSciNet  MATH  Google Scholar 

  • Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Eng ASCE 112(2):223–229

    Google Scholar 

  • Schutte J, Groenwold A (2003) Sizing design of truss structures using particle swarms. Struct Multidiscip Optim 25(4):261–269

    Article  Google Scholar 

  • Tsoulos IG (2008) Modifications of real code genetic algorithm for global optimization. Appl Math Comput 203(2):598–607

    MathSciNet  MATH  Google Scholar 

  • Wu S-J, Chow P-T (1995) Integrated discrete and configuration optimization of trusses using genetic algorithms. Comput Struct 55(4):695–702

    Article  MATH  Google Scholar 

Download references

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Moez, H., Kaveh, A. & Taghizadieh, N. Natural Forest Regeneration Algorithm: A New Meta-Heuristic. Iran J Sci Technol Trans Civ Eng 40, 311–326 (2016). https://doi.org/10.1007/s40996-016-0042-z

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  • DOI: https://doi.org/10.1007/s40996-016-0042-z

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