For solving nonlinear continuous problems of optimization numerous nature inspired optimization techniques are being proposed in literature which can be implemented to solve real life problems wherein the conventional techniques cannot be applied. Grey Wolf Optimizer is one of such technique which is gaining popularity since the last two years. The objective of this paper is to investigate the performance of Grey Wolf Optimization Algorithm on large scale optimization problems. The Algorithm is implemented on 5 common scalable problems appearing in literature namely Sphere, Rosenbrock, Rastrigin, Ackley and Griewank Functions. The dimensions of these problems are varied from 50 to 1000. The results indicate that Grey Wolf Optimizer is a powerful nature inspired Optimization Algorithm for large scale problems, except Rosenbrock which is a unimodal function.
REFERENCES
1.
Arora
, J.
(2017
). Introduction to Optimum Design. Elsevier
, fourth edition, pp 739
–769
.2.
Bansal
, J. C.
, Sharma
, H.
and Clerc
., M.
(2014
). Spider Monkey Optimization Algorithm for Numerical Optimization
. Memetic Computing
, 6
(1
), 31
–47
.3.
Dorigo
, M.
, Maniezzo
, V.
and Colorni
, A.
(1991
). The Ant System: An Autocatalytic Optimizing Process
. Technical Report TR91-016, Politecnico di Milano.
4.
Formato
, R
(2008
). Central Force Optimization: A New Nature Inspired Computational Framework for Multidimensional Search and Optimization
. Nature Inspired Cooperative Strategies for Optimization (NICSO 2007
), Italy
, Series: Studies in Computational Intelligence, Springer
, 129
, 221
–238
.5.
Formato
, R
(2009
). Central force optimization: A New Deterministic Gradient-Like Optimization Metaheuristic
. OPSEARCH
, 46
(1
), 25
–51
.6.
Geem
, Z. W.
, Kim
, J. H.
and Loganathan
, G. V.
(2001
). A new heuristic optimization algorithm: Harmony search
. Simulation
, 76
, 60
–68
.7.
Holland
, J.
,H. (1975
). Adaptation in Natural and Artificial System
. Ann Arbor
, University of Michigan Press
.8.
Jaddi
, N. S.
, Alvankarian
, J.
, and Abdullah
, S.
(2016
). Kidney-inspired Algorithm for Optimization Problems
. Communications in Nonlinear Science and Numerical Simulation
, 42
, 358
–369
.9.
Kar
, A.
, K. (2016
).Bio inspired Computing – A Review of Algorithms and Scope of Applications
, Expert Systems with Applications
, 59
, 20
–32
.10.
Karaboga
, D.
(2005
). An Idea Based On Honey Bee Swarm for Numerical Optimization’, Technical Report-TR06
, Erciyes University, Engineering Faculty, Computer Engineering Department
.11.
Kennedy
, J.
and Eberhart
, R.C.
(1995
). Particle swarm optimization
. In Proceedings of IEEE International Conference Neural Networks
, 4
, 1942
–1948
.12.
Krishnanand
, K. N.
and Ghose
, D.
(2006
). Glowworm Swarm Based Optimization Algorithm for Multimodal Functions with Collective Robotics Applications
. Multiagent and Grid Systems
, 2
(3
), 209
–222
.13.
Krishnanand
, K. N.
and Ghose
, D.
(2009
). Glowworm Swarm Optimisation: A New Method for Optimising Multi-Modal Functions
. International Journal of Computational Intelligence Studies
, 1
(1
), 93
–119
.14.
Mehrabian
, A. R.
, and Lucas
, C.
(2006
). A Novel Numerical Optimization Algorithm Inspired from Weed Colonization
. Ecological Informatics
, 1
(4
), 355
–366
.15.
Mirjalili
, S.
(2015
). Moth-Flame Optimization Algorithm: A Novel Nature-Inspired Heuristic Paradigm
. Knowledge-Based Systems
, 89
, 228
–249
.16.
Mirjalili
, S.
(2015
). The Ant Lion Optimizer
. Advances in Engineering Software
, 83
, 80
–98
.17.
Mirjalili
, S.
, Mirjalili
, S., M.
, and Lewis
, A.
, (2014
). Grey Wolf Optimizer
. Advances in Engineering Software
, 69
, 46
–61
.18.
Mirjalili
, S.
, S. M.
Mirjalili
, A.
Lewis
, Grey Wolf Optimizer
, Advances in Engineering Software
, vol. 69
, pp. 46
–61
, 2014
.19.
Passino
, K. M.
(2002
). Biomimicry of Bacterial Foraging for Distributed Optimization and Control
. IEEE Control Systems Magazine
, 52
–67
.20.
Rao
, R. V.
, Savsani
, V. J.
, and Vakharia
, D. P.
(2011
). Teaching–Learning-Based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems
. Computer-Aided Design
, 43
(3
), 303
–315
.21.
Rashedi
, E.
, Nezamabadi-Pour
, H.
and Saryazdi
,S.
(2009
). GSA: A Gravitational Search Algorithm
, Information Sciences
, 179
(13
), 2232
–2248
.22.
Shah-Hosseini
, H.
(2008
).Optimization with the Nature-Inspired Intelligent Water Drops Algorithm
. International Journal of Intelligent Computing and Cybernetics
, 1
(2
), 193
–212
.23.
Storn
, R.
and Price
, K.
(1995
). Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical Report TR-95-012
, Berkeley, CA
.24.
Tan
, Y.
, and Zhu
, Y.
(2010
, June). Fireworks Algorithm for Optimization
. In International Conference in Swarm Intelligence, Springer Berlin Heidelberg
, 355
–364
.25.
Yang
, X.
, S., (2014
). Nature-Inspired Optimization Algorithms. Elsevier
, first edition, p 263
.26.
Yang
, X.,S.
, Chien
, S.,F.
, and Ting
,T.O.
(2015
).Bio-Inspired Computation in Telecommunications. Morgan Kauffman
, pp 1
–21
.27.
Yadav
, Anupam
, Kusum
Deep
, JoongHoon
Kim
, and Atulya K.
Nagar
. “Gravitational swarm optimizer for global optimization
.” Swarm and Evolutionary Computation
(2016
).
This content is only available via PDF.
© 2017 Author(s).
2017
Author(s)
You do not currently have access to this content.
