A New Tool for Global Optimization Problems- Cuttlefish Algorithm
This paper presents a new meta-heuristic bio-inspired
optimization algorithm which is called Cuttlefish Algorithm (CFA).
The algorithm mimics the mechanism of color changing behavior of
the cuttlefish to solve numerical global optimization problems. The
colors and patterns of the cuttlefish are produced by reflected light
from three different layers of cells. The proposed algorithm considers
mainly two processes: reflection and visibility. Reflection process
simulates light reflection mechanism used by these layers, while
visibility process simulates visibility of matching patterns of the
cuttlefish. To show the effectiveness of the algorithm, it is tested with
some other popular bio-inspired optimization algorithms such as
Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and
Bees Algorithm (BA) that have been previously proposed in the
literature. Simulations and obtained results indicate that the proposed
CFA is superior when compared with these algorithms.
[1] M. Kalyani, C. S. Suresh, B. Poornasatyanarayana, Population based
meta-heuristic techniques for solving optimization problems: A selective
survey, international journal of Emerging Technology and Advanced
Engineering IJETAE, Vol. 2 Issue 11, 2012.
[2] Dorigo, Marco, Ant colony optimization, Massachusetts Institute of
Technology, 2004.
[3] J. Kennedy, and R. Eberhart, Particle Swarm Optimization, IEEE
International Conference on Neural Networks, 1995.
[4] D.T. Pham, A. Ghanbarzadeh, E. Koç, S. Otri , S. Rahim , M. Zaidi, The
Bees Algorithm – A Novel Tool for Complex Optimization,
Manufacturing Engineering Centre, Cardiff University, 2005.
[5] C. Erik, G. Mauricio, Z. Daniel, P. –C. Marco, and G. Guillermo, An
Algorithm for Global Optimization Inspired by Collective Animal
Behavior, Hindawi Publishing Corporation Discrete Dynamics in Nature
and Society, 2012.
[6] R. Esmat, N. –P. Hossein, S. Saeid, GSA: A Gravitational Search
Algorithm, Elsevier, Information Sciences, 2009.
[7] M. Yannis, M. Magdalene, and M. Nikolaos, A Bumble Bees Mating
Optimization Algorithm for Global Unconstrained Optimization
Problems, NICSO, SCI 284, 2010.
[8] B. Ali, A new Approach to Global Optimization Motivated by
Parliamentary Political Competitions, ICIC International, 2008.
[9] Y. Xin-She, A New Metaheuristic Bat-Inspired Algorithm, Springer,
2010.
[10] Y. Xin-She, Firefly algorithms for multimodal optimization, in:
Stochastic Algorithms: Foundations and Applications, SAGA 2009,
Lecture Notes in Computer Sciences, Vol. 5792, 2009.
[11] M. M. Lydia, J. D. Eric, and T. H. Roger, N. M. Justin, Mechanisms and
behavioural functions of structural coloration in cephalopods, J. R. Soc.
Interface, 2008.
[12] http://www.thecephalopodpage.org/.
[13] Y. Jarred, C. L. Alexandra, G. Allyson, H. J. St. H. Debra, T. Lindsay,
M. Michelle and J. T. Nathan, Principles underlying chromatophore
addition during maturation in the European cuttlefish, Sepia officinalis,
Experimental Biology 214, 3423-3432, 2011.
[14] R. T. Hanlon, and J. B. Messenger, Cephalopod Behavior, Cambridge:
Cambridge University Press, 1996.
[15] E. Florey, Ultrastructure and function of cephalopod chromatophores,
Am. Zool. 1969.
[16] R. T. Hanlon, K. M. Cooper, B. U. Budelmann. and T. C. Pappas,
Physiological color change in squid iridophores I, Behavior,
morphology and pharmacology in Lolliguncula brevis, Cell and Tissue
Research. 259, 1990.
[17] K. M. Cooper, R. T. Hanlon, and B.U. Budelmann, Physiological color
change in squid iridophores II, Ultrastructural mechanisms in
Lolliguncula brevis, Cell and Tissue Research. 259, 1990.
[18] R. A. Cloney, and S. L. Brocco, Chromatophore organs, reflector cells,
iridocytes and leucophores in cephalopods, Am. Zool. 1983.
[19] D. Froesch,. and J. B. Messenger, On leucophores and the chromatic
unit of Octopus vulgaris, J. Zool, 1978.
[20] K. Eric, M. M. Lydia, T. H. Roger, B. D. Patrick, R. N. Rajesh, F. Eric
and H. Jason, Biological versus electronic adaptive coloration: how can
one inform the other, J. R. Soc. Interface, 2012.
[21] M. Marcin, S. Czesław. Test functions for optimization needs, 2005.
[22] Adel Sabry Eesa, Adnan Mohsin Abdulazeez, Zeynep Orman, Cuttlefish
Algorithm – A Novel Bio-Inspired Optimization Algorithm, International
Journal of Scientific and Engineering Research, Vol. 4, Issue 9,
September, 2013.
[23] L. H. Randy, and E. H. Sue, Practical Genetic Algorithms Second
Edition, John Wiley & Sons, ISBN: 978-0-471-45565-3, Inc, 2004.
[24] J. R. Nicholas, Forma analysis and random respectful recombination, In
Proc. 4th Int. Conf. on Genetic Algorithms, San Mateo, CA: Morgan
Kauffman,1991.
[25] R. C. Eberhart, Y. Shi, Comparing Inertia Weights and Constriction
Factors in Particle Swarm Optimization, Evolutionary Computation,
2000, Proceedings of the 2000 Congress, Vol. 1, IEEE, 2000.
[1] M. Kalyani, C. S. Suresh, B. Poornasatyanarayana, Population based
meta-heuristic techniques for solving optimization problems: A selective
survey, international journal of Emerging Technology and Advanced
Engineering IJETAE, Vol. 2 Issue 11, 2012.
[2] Dorigo, Marco, Ant colony optimization, Massachusetts Institute of
Technology, 2004.
[3] J. Kennedy, and R. Eberhart, Particle Swarm Optimization, IEEE
International Conference on Neural Networks, 1995.
[4] D.T. Pham, A. Ghanbarzadeh, E. Koç, S. Otri , S. Rahim , M. Zaidi, The
Bees Algorithm – A Novel Tool for Complex Optimization,
Manufacturing Engineering Centre, Cardiff University, 2005.
[5] C. Erik, G. Mauricio, Z. Daniel, P. –C. Marco, and G. Guillermo, An
Algorithm for Global Optimization Inspired by Collective Animal
Behavior, Hindawi Publishing Corporation Discrete Dynamics in Nature
and Society, 2012.
[6] R. Esmat, N. –P. Hossein, S. Saeid, GSA: A Gravitational Search
Algorithm, Elsevier, Information Sciences, 2009.
[7] M. Yannis, M. Magdalene, and M. Nikolaos, A Bumble Bees Mating
Optimization Algorithm for Global Unconstrained Optimization
Problems, NICSO, SCI 284, 2010.
[8] B. Ali, A new Approach to Global Optimization Motivated by
Parliamentary Political Competitions, ICIC International, 2008.
[9] Y. Xin-She, A New Metaheuristic Bat-Inspired Algorithm, Springer,
2010.
[10] Y. Xin-She, Firefly algorithms for multimodal optimization, in:
Stochastic Algorithms: Foundations and Applications, SAGA 2009,
Lecture Notes in Computer Sciences, Vol. 5792, 2009.
[11] M. M. Lydia, J. D. Eric, and T. H. Roger, N. M. Justin, Mechanisms and
behavioural functions of structural coloration in cephalopods, J. R. Soc.
Interface, 2008.
[12] http://www.thecephalopodpage.org/.
[13] Y. Jarred, C. L. Alexandra, G. Allyson, H. J. St. H. Debra, T. Lindsay,
M. Michelle and J. T. Nathan, Principles underlying chromatophore
addition during maturation in the European cuttlefish, Sepia officinalis,
Experimental Biology 214, 3423-3432, 2011.
[14] R. T. Hanlon, and J. B. Messenger, Cephalopod Behavior, Cambridge:
Cambridge University Press, 1996.
[15] E. Florey, Ultrastructure and function of cephalopod chromatophores,
Am. Zool. 1969.
[16] R. T. Hanlon, K. M. Cooper, B. U. Budelmann. and T. C. Pappas,
Physiological color change in squid iridophores I, Behavior,
morphology and pharmacology in Lolliguncula brevis, Cell and Tissue
Research. 259, 1990.
[17] K. M. Cooper, R. T. Hanlon, and B.U. Budelmann, Physiological color
change in squid iridophores II, Ultrastructural mechanisms in
Lolliguncula brevis, Cell and Tissue Research. 259, 1990.
[18] R. A. Cloney, and S. L. Brocco, Chromatophore organs, reflector cells,
iridocytes and leucophores in cephalopods, Am. Zool. 1983.
[19] D. Froesch,. and J. B. Messenger, On leucophores and the chromatic
unit of Octopus vulgaris, J. Zool, 1978.
[20] K. Eric, M. M. Lydia, T. H. Roger, B. D. Patrick, R. N. Rajesh, F. Eric
and H. Jason, Biological versus electronic adaptive coloration: how can
one inform the other, J. R. Soc. Interface, 2012.
[21] M. Marcin, S. Czesław. Test functions for optimization needs, 2005.
[22] Adel Sabry Eesa, Adnan Mohsin Abdulazeez, Zeynep Orman, Cuttlefish
Algorithm – A Novel Bio-Inspired Optimization Algorithm, International
Journal of Scientific and Engineering Research, Vol. 4, Issue 9,
September, 2013.
[23] L. H. Randy, and E. H. Sue, Practical Genetic Algorithms Second
Edition, John Wiley & Sons, ISBN: 978-0-471-45565-3, Inc, 2004.
[24] J. R. Nicholas, Forma analysis and random respectful recombination, In
Proc. 4th Int. Conf. on Genetic Algorithms, San Mateo, CA: Morgan
Kauffman,1991.
[25] R. C. Eberhart, Y. Shi, Comparing Inertia Weights and Constriction
Factors in Particle Swarm Optimization, Evolutionary Computation,
2000, Proceedings of the 2000 Congress, Vol. 1, IEEE, 2000.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:68102", author = "Adel Sabry Eesa and Adnan Mohsin Abdulazeez Brifcani and Zeynep Orman", title = "A New Tool for Global Optimization Problems- Cuttlefish Algorithm", abstract = "This paper presents a new meta-heuristic bio-inspired
optimization algorithm which is called Cuttlefish Algorithm (CFA).
The algorithm mimics the mechanism of color changing behavior of
the cuttlefish to solve numerical global optimization problems. The
colors and patterns of the cuttlefish are produced by reflected light
from three different layers of cells. The proposed algorithm considers
mainly two processes: reflection and visibility. Reflection process
simulates light reflection mechanism used by these layers, while
visibility process simulates visibility of matching patterns of the
cuttlefish. To show the effectiveness of the algorithm, it is tested with
some other popular bio-inspired optimization algorithms such as
Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and
Bees Algorithm (BA) that have been previously proposed in the
literature. Simulations and obtained results indicate that the proposed
CFA is superior when compared with these algorithms.
", keywords = "Cuttlefish Algorithm, bio-inspired algorithms,
optimization.", volume = "8", number = "9", pages = "1239-5", }
