A New Tool for Global Optimization Problems- Cuttlefish Algorithm

Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096403

References:

[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.