Grass Fibrous Root Optimization Algorithm

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Hanan A. R. Akkar 1,* Firas R. Mahdi 1

1. University Of Technology/ Electrical Engineering Department, Baghdad, Iraq

* Corresponding author.


Received: 14 Nov. 2016 / Revised: 5 Feb. 2017 / Accepted: 10 Apr. 2017 / Published: 8 Jun. 2017

Index Terms

Grass development, Fibrous root system, Meta-heuristic algorithms, Grass Root Algorithm, Optimization


This paper proposes a novel meta-heuristic optimization algorithm inspired by general grass plants fibrous root system, asexual reproduction, and plant development. Grasses search for water and minerals randomly by developing its location, length, primary root, regenerated secondary roots, and small branches of roots called hair roots. The proposed algorithm explore the bounded solution domain globally and locally. Globally using the best grasses survived by the last iteration, and the root system of the best grass obtained so far by the iteration process and locally uses the primary roots, regenerated secondary roots and hair roots of the best global grass. Each grass represents a global candidate solution, while regenerated secondary roots stand for the locally obtained solution. Secondary generated hair roots are equal to the problem dimensions. The performance of the proposed algorithm is tested using seven standard benchmark test functions, comparing it with other meta-heuristic well-known and recently proposed algorithms.

Cite This Paper

Hanan A. R. Akkar, Firas R. Mahdi,"Grass Fibrous Root Optimization Algorithm", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.6, pp.15-23, 2017. DOI:10.5815/ijisa.2017.06.02


[1]Koffka Khan,Ashok Sahai,"A Comparison of BA, GA, PSO, BP and LM for Training Feed forward Neural Networks in e-Learning Context", International Journal of Intelligent Systems and Applications IJISA, vol.4, no.7, pp.23-29, 2012.
[2]Harpreet Singh, Parminder Kaur,"Website Structure Optimization Model Based on Ant Colony System and Local Search", IJITCS, vol.6, no.11, pp.48-53, 2014.
[3]A. J. Umbarkar, N. M. Rothe, A.S. Sathe,"OpenMP Teaching-Learning Based Optimization Algorithm over Multi-Core System", International Journal of Intelligent Systems and Applications (IJISA), vol.7, no.7, pp.57-65, 2015.
[4]Alazzam, H. W. Lewis, “A New Optimization Algorithm For Combinatorial Problems,” Int. J. of Advanced Research in Artificial Intelligence, Vol.2, Issue 5, pp.63-68, 2013.
[5]J. Kennedy, R. Eberhart, “ Particle Swarm Optimization,” IEEE, Int. Conf., Neural Networks Proc., Vol.4, 1942 – 1948, 1995.
[6]F. Xue, Sanderson, A.C., Graves, R.J., “Multi-Objective Differential Evolution Algorithm Convergence Analysis and Applications,” IEEE conf., Evolutionary Computation, vol. 1, Edinburgh, Scotland, pp. 743–750, 2005.
[7]D. Karaboga, B. Basturk, “A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm,” Journal of Global Optimization, Vol.39, Issue 3, pp. 459-471, 2007.
[8]X.S. Yang, S. Deb, “Cuckoo Search via Levy Flights”, IEEE conf. , Proc. of World Congress on Nature & Biologically Inspired Computing, (NABIC), pp. 210-214, 2009.
[9]Bayraktar, Z., Komurcu, M. , Werner, D.H., “Wind Driven Optimization (WDO): A Novel Nature-Inspired Optimization Algorithm and its Application to Electromagnetics,” IEEE Int. conf., Antennas and Propagation Society International Symposium (APSURSI), Toronto, pp. 1-4, 2010.
[10]H. Salimi, “Stochastic Fractal Search: A Powerful Meta-heuristic Algorithm,” Knowledge-Based Systems, Vol. 75, 1-18, 2015.
[11]M. Cheng, D. Prayogo, “Symbiotic Organisms Search: A New Metaheuristic Optimization Algorithm,” Computers & Structures, Vol. 139, pp. 98–112, 2014.
[12]S. Mirjalilia, S. M. Mirjalilib, A. Lewisa, “Grey Wolf Optimizer”, Advances in Engineering Software, Vol. 69, pp. 46-61, 2014.
[13]Menga, X.Z. Gaob, Y. Liuc, H. Zhanga, “A Novel Bat Algorithm with Habitat Selection and Doppler Effect in Echoes for Optimization,” Expert Systems with Applications, Vol. 42, no. 17–18, 6350–6364, 2015.
[14]C. Stichler, Grass Growth and Development, Texas Cooperative Extension, Texas A&M University.
[15]M. Jamil, and X.Yang, “A Literature Survey of Benchmark Functions for Global Optimization Problems”, Int. Journal of Mathematical Modelling and Numerical Optimisation, vol. 4, no. 2, pp. 150–194, Aug., 2013.
[16]X.S. Yang, “Appendix A Test Problems in Optimization,” in Engineering Optimization: An Introduction with Metaheuristic Applications, 1st ed., New Jersey, USA, John Wiely & Sons, Inc., pp. 261-166, 2010.