{"title":"A New Tool for Global Optimization Problems- Cuttlefish Algorithm","authors":"Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman","volume":93,"journal":"International Journal of Computer and Information Engineering","pagesStart":1235,"pagesEnd":1240,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9999515","abstract":"
This paper presents a new meta-heuristic bio-inspired
\r\noptimization algorithm which is called Cuttlefish Algorithm (CFA).
\r\nThe algorithm mimics the mechanism of color changing behavior of
\r\nthe cuttlefish to solve numerical global optimization problems. The
\r\ncolors and patterns of the cuttlefish are produced by reflected light
\r\nfrom three different layers of cells. The proposed algorithm considers
\r\nmainly two processes: reflection and visibility. Reflection process
\r\nsimulates light reflection mechanism used by these layers, while
\r\nvisibility process simulates visibility of matching patterns of the
\r\ncuttlefish. To show the effectiveness of the algorithm, it is tested with
\r\nsome other popular bio-inspired optimization algorithms such as
\r\nGenetic Algorithms (GA), Particle Swarm Optimization (PSO) and
\r\nBees Algorithm (BA) that have been previously proposed in the
\r\nliterature. Simulations and obtained results indicate that the proposed
\r\nCFA is superior when compared with these algorithms.<\/p>\r\n","references":"[1] M. Kalyani, C. S. Suresh, B. Poornasatyanarayana, Population based\r\nmeta-heuristic techniques for solving optimization problems: A selective\r\nsurvey, international journal of Emerging Technology and Advanced\r\nEngineering IJETAE, Vol. 2 Issue 11, 2012.\r\n[2] Dorigo, Marco, Ant colony optimization, Massachusetts Institute of\r\nTechnology, 2004.\r\n[3] J. Kennedy, and R. Eberhart, Particle Swarm Optimization, IEEE\r\nInternational Conference on Neural Networks, 1995.\r\n[4] D.T. Pham, A. Ghanbarzadeh, E. Ko\u00e7, S. Otri , S. Rahim , M. Zaidi, The\r\nBees Algorithm \u2013 A Novel Tool for Complex Optimization,\r\nManufacturing Engineering Centre, Cardiff University, 2005.\r\n[5] C. Erik, G. Mauricio, Z. Daniel, P. \u2013C. Marco, and G. Guillermo, An\r\nAlgorithm for Global Optimization Inspired by Collective Animal\r\nBehavior, Hindawi Publishing Corporation Discrete Dynamics in Nature\r\nand Society, 2012.\r\n[6] R. Esmat, N. \u2013P. Hossein, S. Saeid, GSA: A Gravitational Search\r\nAlgorithm, Elsevier, Information Sciences, 2009.\r\n[7] M. Yannis, M. Magdalene, and M. Nikolaos, A Bumble Bees Mating\r\nOptimization Algorithm for Global Unconstrained Optimization\r\nProblems, NICSO, SCI 284, 2010.\r\n[8] B. Ali, A new Approach to Global Optimization Motivated by\r\nParliamentary Political Competitions, ICIC International, 2008.\r\n[9] Y. Xin-She, A New Metaheuristic Bat-Inspired Algorithm, Springer,\r\n2010.\r\n[10] Y. Xin-She, Firefly algorithms for multimodal optimization, in:\r\nStochastic Algorithms: Foundations and Applications, SAGA 2009,\r\nLecture Notes in Computer Sciences, Vol. 5792, 2009.\r\n[11] M. M. Lydia, J. D. Eric, and T. H. Roger, N. M. Justin, Mechanisms and\r\nbehavioural functions of structural coloration in cephalopods, J. R. Soc.\r\nInterface, 2008.\r\n[12] http:\/\/www.thecephalopodpage.org\/.\r\n[13] Y. Jarred, C. L. Alexandra, G. Allyson, H. J. St. H. Debra, T. Lindsay,\r\nM. Michelle and J. T. Nathan, Principles underlying chromatophore\r\naddition during maturation in the European cuttlefish, Sepia officinalis,\r\nExperimental Biology 214, 3423-3432, 2011.\r\n[14] R. T. Hanlon, and J. B. Messenger, Cephalopod Behavior, Cambridge:\r\nCambridge University Press, 1996.\r\n[15] E. Florey, Ultrastructure and function of cephalopod chromatophores,\r\nAm. Zool. 1969.\r\n[16] R. T. Hanlon, K. M. Cooper, B. U. Budelmann. and T. C. Pappas,\r\nPhysiological color change in squid iridophores I, Behavior,\r\nmorphology and pharmacology in Lolliguncula brevis, Cell and Tissue\r\nResearch. 259, 1990.\r\n[17] K. M. Cooper, R. T. Hanlon, and B.U. Budelmann, Physiological color\r\nchange in squid iridophores II, Ultrastructural mechanisms in\r\nLolliguncula brevis, Cell and Tissue Research. 259, 1990.\r\n[18] R. A. Cloney, and S. L. Brocco, Chromatophore organs, reflector cells,\r\niridocytes and leucophores in cephalopods, Am. Zool. 1983.\r\n[19] D. Froesch,. and J. B. Messenger, On leucophores and the chromatic\r\nunit of Octopus vulgaris, J. Zool, 1978.\r\n[20] K. Eric, M. M. Lydia, T. H. Roger, B. D. Patrick, R. N. Rajesh, F. Eric\r\nand H. Jason, Biological versus electronic adaptive coloration: how can\r\none inform the other, J. R. Soc. Interface, 2012.\r\n[21] M. Marcin, S. Czes\u0142aw. Test functions for optimization needs, 2005.\r\n[22] Adel Sabry Eesa, Adnan Mohsin Abdulazeez, Zeynep Orman, Cuttlefish\r\nAlgorithm \u2013 A Novel Bio-Inspired Optimization Algorithm, International\r\nJournal of Scientific and Engineering Research, Vol. 4, Issue 9,\r\nSeptember, 2013.\r\n[23] L. H. Randy, and E. H. Sue, Practical Genetic Algorithms Second\r\nEdition, John Wiley & Sons, ISBN: 978-0-471-45565-3, Inc, 2004.\r\n[24] J. R. Nicholas, Forma analysis and random respectful recombination, In\r\nProc. 4th Int. Conf. on Genetic Algorithms, San Mateo, CA: Morgan\r\nKauffman,1991.\r\n[25] R. C. Eberhart, Y. Shi, Comparing Inertia Weights and Constriction\r\nFactors in Particle Swarm Optimization, Evolutionary Computation,\r\n2000, Proceedings of the 2000 Congress, Vol. 1, IEEE, 2000.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 93, 2014"}