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Recent Advances in Electrical & Electronic Engineering

Author(s): Iman E.P. Afrakoti and Vahdat Nazerian*

DOI: 10.2174/2352096512666191127122752

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Performance Analysis of Optimization Process on Adaptive Group of Ink Drop Spread Algorithm Proficiency

Page: [918 - 924] Pages: 7

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Abstract

Aims: Two evolutionary algorithms consist of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are being used for finding the best value of critical parameters in AGIDS which will affect the accuracy and efficiency of the algorithm.

Background: Adaptive Group of Ink Drop Spread (AGIDS) is a powerful algorithm which was proposed in fuzzy domain based on Active Learning Method (ALM) algorithm.

Objective: The effectiveness of AGIDS vs. artificial neural network and other soft-computing algorithms has been shown in classification, system modeling and regression problems.

Methods: For solving a real-world problem a tradeoff should be taken between the needed accuracy and the available time and processing resources.

Results: The simulation result shows that optimization approach will affect the accuracy of modelling being better, but its computation time is rather high.

Conclusion: The simulation shows that AGIDS algorithm has a suitable efficacy in solving complex problems without using complex optimization algorithms.

Other: The simulation shows that AGIDS algorithm has a suitable efficacy in solving complex problems without using complex optimization algorithms.

Keywords: AGIDS, evolutionary algorithm, genetic algorithm, fuzzy inference, particle swarm optimization, linguistic fuzzy.

Graphical Abstract

[1]
L.A. Zadeh, "Outline of a new approach to the analysis of complex systems and decision processes", IEEE Trans. Syst. Man Cybern., no. 1, pp. 28-44, 1973.
[http://dx.doi.org/10.1109/TSMC.1973.5408575]
[2]
E.H. Mamdani, "Application of fuzzy logic to approximate reasoning using linguistic synthesis", In Proceedings of the Sixth International Symposium On Multiple-Valued Logic, 1976pp. 196-202
[3]
T. Takagi, and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control", IEEE Trans. Syst. Man Cybern., no. 1, pp. 116-132, 1985.
[http://dx.doi.org/10.1109/TSMC.1985.6313399]
[4]
S.B. Shouraki, and N. Honda, "Recursive fuzzy modeling based on fuzzy interpolation", JACIII, vol. 3, no. 2, pp. 114-125, 1999.
[http://dx.doi.org/10.20965/jaciii.1999.p0114]
[5]
M. Murakami, and N. Honda, "A study on the modeling ability of the IDS method: A soft computing technique using pattern-based information processing", Int. J. Approx. Reason., vol. 45, no. 3, pp. 470-487, 2007.
[http://dx.doi.org/10.1016/j.ijar.2006.06.022]
[6]
M. Firouzi, S.B. Shouraki, and I.E.P. Afrakoti, "Pattern analysis by active learning method classifier", J. Intell. Fuzzy Syst., vol. 26, no. 1, pp. 49-62, 2014.
[http://dx.doi.org/10.3233/IFS-120714]
[7]
M. Murakami, and N. Honda, "Classification performance of the IDS method based on the two-spiral benchmark", In: IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, HI, USA, vol. 4, , 2005, pp. 3797-3803,
[http://dx.doi.org/0.1109/ICSMC.2005.1571738]
[8]
S.A. Shahdi, and S.B. Shouraki, Supervised active learning method as an intelligent linguistic controller and its hardware implementation ", In: IASTEAD Artificial Intelligence Soft Computing Conference (IASTEAD-ASC'03), Banff, Canada, 2003.
[9]
Y. Sakurai, A study of the learning control method using PBALM-a nonlinear modeling method., PhD, The University of Electro-Communications: Tokyo, 2005.
[10]
M. Murakami, and N. Honda, A basic constructive algorithm for the IDS method. SCIS & ISIS SCIS & ISIS 2006., Japan Society for Fuzzy Theory and Intelligent Informatics, 2006, pp. 355-360.
[11]
I.E.P. Afrakoti, A. Ghaffari, and S.B. Shouraki, "Effective partitioning of input domains for ALM algorithm", In First Iranian Conference on Pattern Recognition and Image Analysis (PRIA), 2013pp. 1-5
[http://dx.doi.org/10.1109/PRIA.2013.6528437]
[12]
H. Sagha, S.B. Shouraki, H. Beigy, H. Khasteh, and E. Enayati, Genetic ink drop spread.
[http://dx.doi.org//10.1109/IITA.2008.588]
[13]
S.H. Klidbary, S.B. Shouraki, and I.E.P. Afrakoti, "An adaptive efficient memristive ink drop spread (IDS) computing system", Neural Comput. Appl., pp. 1-22, 2018.
[14]
I.E.P. Afrakoti, S.B. Shouraki, and B. Haghighat, "An Optimal Hardware Implementation for Active Learning Method Based on Memristor Crossbar Structures", IEEE Syst. J., vol. 8, no. 4, pp. 1190-1199, 2014.
[http://dx.doi.org/10.1109/JSYST.2013.2295963]
[15]
I.E.P. Afrakoti, S.B. Shouraki, F.M. Bayat, and M. Gholami, "Using a memristor crossbar structure to implement a novel adaptive real-time fuzzy modeling algorithm", Fuzzy Sets Syst., vol. 307, pp. 115-128, 2017.
[http://dx.doi.org/10.1016/j.fss.2016.02.016]
[16]
S.A. Hosseini, and I.E.P. Afrakoti, "Adaptive group of ink drop spread: A computer code to unfold neutron noise sources in reactor cores", Nucl. Eng. Technol., vol. 49, no. 7, pp. 1369-1378, 2017.
[http://dx.doi.org/10.1016/j.net.2017.05.009]
[17]
M. Dorigo, and M. Birattari, Ant colony optimization., Springer: US, 2010, pp. 36-39.
[18]
D.T. Pham, A. Ghanbarzadeh, E. Koç, S. Otri, S. Rahim, and M. Zaidi, The bees algorithm-a novel tool for complex optimisation problems”, Intell. Product. Mach. Syst., Elsevier Science Ltd., 2006, pp. 454-459..
[http://dx.doi.org/10.1016/B978-008045157-2/50081-X]
[19]
G. Dhiman, and A. Kaur, "Spotted hyena optimizer for solving engineering design problems. In: International Conference on Machine Learning and Data Science (MLDS) IEEE, pp. 114-119, 2017.,
[http://dx.doi.org/10.1109/MLDS.2017.5]
[20]
J.D. Knowles, and D.W. Corne, "M-PAES: A memetic algorithm for multiobjective optimization".
[http://dx.doi.org/10.1109/CEC.2000.870313]
[21]
G. Dhiman, "MOSHEPO: A hybrid multi-objective approach to solve economic load dispatch and micro grid problems", Appl. Intell., pp. 1-19, 2019.
[22]
J.H. Holland, Adaptation in natural and artificial systems. 1975., University of Michigan Press and: Ann Arbor, MI, 1992.
[http://dx.doi.org/10.7551/mitpress/1090.001.0001]
[23]
K.C. Wong, C. Peng, M.H. Wong, and K.S. Leung, "Generalizing and learning protein-DNA binding sequence representations by an evolutionary algorithm", Soft Comput., vol. 15, no. 8, pp. 1631-1642, 2011.
[http://dx.doi.org/10.1007/s00500-011-0692-5]
[24]
A.C. dos-Santos-Paulino, J.C. Nebel, and F. Flórez-Revuelta, "Evolutionary algorithm for dense pixel matching in presence of distortions", In European Conference on the Applications of Evolutionary Computation, 2014pp. 439-450
[http://dx.doi.org/10.1007/978-3-662-45523-4_36]
[25]
K. Krawiec, and M. Pawlak, "Genetic programming with alternative search drivers for detection of retinal blood vessels", In European Conference on the Applications of Evolutionary Computation, 2015pp. 554-566
[http://dx.doi.org/10.1007/978-3-319-16549-3_45]
[26]
N.D. Pandey, and D.P. Tiwari, "Comparison between Speed Control DC motor using genetic algorithm and PSO-PID algorithm"In: Int. J. Electric. Eng. Technol., vol. 8, no. 1, 2017..
[27]
R. Eberhart, and J. Kennedy, "A new optimizer using particle swarm theory", In: MHS'95.Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995pp. 39-43
[http://dx.doi.org/10.1109/MHS.1995.494215]
[28]
A.P. Engelbrecht, Fundamentals of Computational Swarm Intelligence., John Wiley & Sons, 2006.
[29]
R. Poli, "Analysis of the publications on the applications of particle swarm optimisation", J. Artif. Evol. Appl., p. 2008, 2008.
[http://dx.doi.org/10.1155/2008/685175]
[30]
T.L. Dang, and Y. Hoshino, An-FPGA based classification system by using a neural network and an improved particle swarm optimization algorithm.
[http://dx.doi.org/10.1109/SCIS-ISIS.2016.0033]
[31]
Z. Izakian, M.S. Mesgari, and A. Abraham, "Automated clustering of trajectory data using a particle swarm optimization", Comput. Environ. Urban Syst., vol. 55, pp. 55-65, 2016.
[http://dx.doi.org/10.1016/j.compenvurbsys.2015.10.009]
[32]
J.N. Hwang, S.R. Lay, M. Maechler, R.D. Martin, and J. Schimert, "Regression modeling in back-propagation and projection pursuit learning", IEEE Trans. Neural Netw., vol. 5, no. 3, pp. 342-353, 1994.
[http://dx.doi.org/10.1109/72.286906 ] [PMID: 18267802]