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Soft Computing

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01.03.2014 | Methodologies and Application

Root growth model: a novel approach to numerical function optimization and simulation of plant root system

verfasst von: Hao Zhang, Yunlong Zhu, Hanning Chen

Erschienen in: Soft Computing | Ausgabe 3/2014

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Abstract

This paper presents a general optimization model gleaned ideas from root growth behaviours in the soil. The purpose of the study is to investigate a novel biologically inspired methodology for complex system modelling and computation, particularly for optimization of higher-dimensional numerical function. For this study, a mathematical framework and architecture are designed to model root growth patterns of plant. Under this architecture, the interactions between the soil and root growth are investigated. A novel approach called “root growth algorithm” (RGA) is derived in the framework and simulation studies are undertaken to evaluate this algorithm. The simulation results show that the proposed model can reflect the root growth behaviours of plant in the soil and the numerical results also demonstrate RGA is a powerful search and optimization technique for higher-dimensional numerical function optimization.

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Cai W, Yang W, Chen X (2008) A global optimization algorithm based on plant growth theory: plant growth optimization. Int Conf Intell Comput Technol Autom (ICICTA) 1:1194–1199

Corne D, Dorigo M, Glover F (1999) New ideas in optimization. McGraw-Hill, New York

De Castro LN, Von Zuben FJ (1999) Artificial immune systems, Part I. Basic theory and applications, Technical Report Rt Dca 01/99, Feec/Unicamp

Dorigo M, Maniezzo V, Colorni A (1991) Positive feedback as a search strategy, Technical Report 91-016. Dipartimento di Elettronica, Politecnico di Milano

Eberhart RC, Shi Y, Kennedy J (2001) Swarm intelligence. Morgan Kaufmann, Massachusetts

Fogel LJ, Owens AJ, Walsh MJ (1965) Artificial intelligence through a simulation of evolution. In: Maxfield M, Callahan A, Fogel LJ (eds) Biophysics and cybernetic systems. Proceedings of the 2nd cybernetic sciences symposium. Spartan Books, pp 131–155

Gerwitz A, Page ER (1974) An empirical mathematical model to describe plant root systems. J Appl Ecol 11(2):773–781CrossRef

Hodge A (2009) Root decisions. Plant Cell Environ 32(6):628–640CrossRef

Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697CrossRef

Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132CrossRefMATHMathSciNet

Kennedy J, Eberhart RC (1995) IEEE international conference on neural networks, vol 4, pp 1942–1948

Krink T, Vestertroem JS, Riget J (2002) Particle swarm optimization with spatial particle extension. In: Proceedings of the IEEE congress on evolutionary computation, Honolulu, pp 1474–1479

Leitner D, Klepsch S, Bodner G, Schnepf A (2010) A dynamic root system growth model based on L-Systems. Plant Soil 332:177–192CrossRef

Lynch J (2007) Roots of the second green revolution. Aust J Bot 55(5):493–512CrossRef

Mainzer K (1997) Thinking in complexity: the complex dynamics of matter, mind and mankind, 3rd edn. Springer, New YorkCrossRef

Pages L, Jordan MO, Picard D (1989) A simulation model of the three-dimensional architecture of the maize root system. Plant Soil 119:147–154

Price K, Storn R (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report. International Computer Science Institute, Berkley

Sheng WD, Lei C (1995) Fractal and their applications. Publishing Company of University of Science and Technology of China, Beijing

Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation, Washington, DC, pp 1945–1950

Shi Y, Ebrehart RC (1998) A modified particle swarm optimizer. In: Proceeding of the IEEE international conference on computational intelligence, Anchorage, pp 69–73

Tong L, Feng WC, Bo WW, Ling SW (2005) A global optimization bionics algorithm for solving integer programming—plant growth simulation algorithm. Syst Eng 25(1):76–85

Valdez F, Melin P, Castillo O (2011) An improved evolutionary method with fuzzy logic for combining Particle swarm optimization and genetic algorithms. Appl Soft Comput 11(2):2625–2632CrossRef

Vesterstrom J, Thomsen R (2004) A comparative study of differential evolution particle swarm optimization and evolutionary algorithms on numerical benchmark problems. In: IEEE congress on evolutionary computation (CEC’2004), vol 3, Piscataway, pp 1980–1987

White PJ, Broadley MR, Greenwood DJ, Hammond JP (2005) Genetic modifications to improve phosphorus acquisition by roots. IFS, Proceedings of the International Fertiliser Society, York

Titel: Root growth model: a novel approach to numerical function optimization and simulation of plant root system
verfasst von: Hao Zhang
Yunlong Zhu
Hanning Chen
Publikationsdatum: 01.03.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 3/2014
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-013-1073-z

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