Optimisation of Process Parameters using the Hybrid Algorithm of Artificial Bee Colony and Fuzzy Logic Controller

Islam, Madani; Rahman, Md. Rizwanur

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dc.contributor.author	Islam, Madani
dc.contributor.author	Rahman, Md. Rizwanur
dc.date.accessioned	2017-11-23T06:28:19Z
dc.date.available	2017-11-23T06:28:19Z
dc.date.issued	2016-11-20
dc.identifier.citation	[1] R.C. Eberhart, Y. Shi, J. Kennedy, Swarm Intelligence, Morgan Kaufmann, 2001. [2] J.H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, 1975. [3] J. Kennedy, R.C. Eberhart, in: Particle Swarm Optimization, 1995 IEEE International Conference on Neural Networks, vol. 4, 1995, pp. 1942–1948 [4] L.N. De Castro, F.J. Von Zuben, Artificial Immune Systems: Part I. Basic Theory and Applications, Technical Report Rt Dca 01/99, Feec/Unicamp, Brazil, 1999. [5] D. Karaboga, B. Basturk, On the performance of artificial bee colony (ABC) algorithm, Appl. Soft Comput. 8 (1) (2008) 687–697. [6] J.D. Knowles, D.W. Corne, Approximating the nondominated front using the Pareto archived evolution strategy, Evol. Comput. 8 (2) (2000) 149–172. [7] D.W. Corne, N.R. Jerram, J.D. Knowles, M.J. Oates, PESAII: region-based selection in evolutionary multiobjective optimization, in: Proceedings of the Genetic Evol. Comput. Conf. (GECCO), Springer, Berlin, 2001, pp. 283–290. [8] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6 (2) (2002)182–197. [9] E. Zitzler, M. Laumanns, L. Thiele, SPEA2: Improving the strength pareto evolutionary algorithm, in: Proceedings of the EUROGEN 2001: Evolutionary Methods Design Optimization Control Appl. Ind. Problems, Athens, Greece, 2002, pp. 95–100. [10] E. Zitzler, S. Künzli, Indicator-based selection in multiobjective search, in: Proceedings of the Parallel Problem Solving Nature (PPSN), Lecture Notes in Computer Science, vol. 3242, Springer-Verlag, Birmingham, UK, 2004, pp. 832–842. 38 [11] Carlos A. Coello Coello, Gregorio Toscano Pulido, Maximino Salazar Lechuga, Handling multiple objectives with particle swarm optimization, IEEE Trans. Evol. Comput. 8 (3) (2004) 256–279. [12] Q. Zhang, W. Liu, H. Li, The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances, in: Proceedings of the Congress on Evolutionary Computation (CEC 2009), Norway, 2009, pp. 203–208. [13] S.Z. Zhao, P.N. Suganthan, Two-lbests based multi-objective particle swarm optimizer, Eng. Optimiz. 43 (1) (2011) 1–17. [14] B.Y. Qu, P.N. Suganthan, Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection, Inform. Sci. 180 (17) (2010) 3170–3181. [15] D. Karaboga, C. Ozturk, A novel clustering approach: artificial bee colony (ABC) algorithm, Appl. Soft Comput. 11 (1) (2011) 652–657. [16] S.N. Omkar, J. Senthilnath, Rahul Khandelwal, G. Narayana Naik, S. Gopalakrishnan, Artificial bee colony (ABC) for multi-objective design optimization of composite structures, Appl. Soft Comput. 11 (1) (2011) 489–499. [17] R. Hedayatzadeh, B. Hasanizadeh, R. Akbari, K. Ziarati, A multi-objective artificial bee colony for optimizing multi-objective problems, in: 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE). Chengdu, China, August 2010, pp. 277–281. [18] A.M. ABIDO, Multiobjective evolutionary algorithms for electric power dispatch problem, IEEE Trans. Evol. Comput. 10 (3) (2006) 315–329. [19] D. Karaboga, An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report TR06, Computer Engineering Department, Erciyes University, Turkey, 2005. [20] D. Karaboga, B. Gorkemli, C. Ozturk, N. Karaboga, A comprehensive survey: artificial bee colony (ABC) algorithm and applications, Artificial Intelligence Review (in press)http://dx.doi.org/10.1007/S10462-012-9328-0. 39 [21] A.R. Yildiz, Comparison of evolutionary-based optimization algorithms for structural design optimization, Engineering Applications of Artificial Intelligence (2013), in press, http://dx.doi.org/10.1016/j.engappai.2012.05.014 [22] Use of an Artificial Neural Network for Data Analysis in Clinical Decision-Making: The Diagnosis of Acute Coronary Occlusion. Baxt, William G. 1990, Neural Computation, Vol. 2, pp. 480-489. [23] The integrated methodology of rough set theory and artificial neuralnetwork for business failure prediction. B.S. Ahn,S.S. Cho,C.Y. Kim. 2, february 2000, Expert Systems with Applications, Vol. 18, pp. 65-74. [24] A multi-sensor fusion model based on artificial neural network to predict tool wear during hard turning. P Sam Paul1,AS Varadarajan. November 9, 2011. [25] Abachizadeh M, Yazdi M, Yousefi-Koma A (2010a) Optimal tuning of pid controllers using artificial beecolony algorithm. In: 2010 IEEE/ASME international conference on advanced intelligent mechatronics (AIM), pp 379–384 [26] Abachizadeh M, Yousefi-Koma A, Shariatpanahi M (2010b) Optimization of a beam-type ipmc actuator using insects swarm intelligence methods. In: Proceedings of the ASME 10th biennial conference onengineering systems design and analysis, 2010, vol 1, ASME, Petroleum Div, pp 559–566	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/159
dc.description	Supervised by Dr. Mohammad Ahsan Habib Assistant Professor Department of Mechanical & Chemical Engineering Islamic University of Technology (IUT), OIC Board Bazar, Gazipur	en_US
dc.description.abstract	A hybrid Artificial Bee Colony (HABC) algorithm is proposed based on Fuzzy Inference System for solving fuzzy flexible real life optimisation problem. First, the Fuzzy Logic Control provides multiple parameters to an output of single parameter using the fuzzy logic membership function dependent on the input parameter characteristic and the fuzzy logic rules. Second, this single output from the fuzzy logic is optimised by ABC which utilises multiple strategies in a combined way to generate the initial solutions with certain quality. Third, the algorithm is verified in established set of data which demonstrated the effectiveness of the hybrid Algorithm	en_US
dc.language.iso	en	en_US
dc.publisher	IUT, MCE	en_US
dc.title	Optimisation of Process Parameters using the Hybrid Algorithm of Artificial Bee Colony and Fuzzy Logic Controller	en_US
dc.type	Thesis	en_US