Abstract
During the past three decades, evolutionary computing techniques have grown manifold in tackling all sorts of optimization problems. Genetic algorithm (GA) is one of the most popular EAs because it is easy to implement and is conducive for noisy environment. Similarly, amongst several swarm intelligence techniques, bacterial foraging optimization (BFO) is the recent popular algorithm being used for many practical applications. Depending on the complexity of the problem concerned, there is need for hybridized techniques which help in balancing exploration and exploitation capability over the search space. Many hybridized techniques have been developed recently to tackle such problems. This paper proposes a hybridization of GA and BFO to solve a real-life unconstrained electrical engineering problem. This unconstrained optimization problem is a model order reduction (MOR) problem of linear time invariant continuous single input and single output (SISO) system.
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References
Deepa, S.N., Sugumaran, G.: MPSO based model order formulation technique for SISO continuous system. World Acad. Sci. Eng. Technol. 51, 838–843 (2011)
Bansal, J.C., Sharma, H., Arya, K.V.: Model order reduction of single input and single output systems using Artificial Bee Colony optimization Algorithm. In: Nature Inspired Co-operative Strategies for Optimization (NICSO 2011), Studies in Computational intelligence, vol. 387, pp. 85–100, Springer link (2011)
Bansal, J.C., Sharma, H.: Cognitive learning in differential evolution and its application to model order reduction problem for single-input and single–output systems, Meme tic computing, doi:10.1007/s12293-012-0089-8, Springer (2012)
Bansal, J.C., Sharma H., Arya, V.K.: Fitness based Differential Evolution, Memetic Computing, doi:10.1007/s12293-012-0096-9, 2012
Kumar, V., Tiwari, J.P.: Order reducing of linear system using Clustering method factor division algorithm. Foundation of Computer science FCS, New York, USA, vol. 3, no. 5, IJAIS, ISSN: 2249–0868, www.ijais.org, 2012
Mondal, S., Tripathy, P.: Model order reduction by mixed mathematical Methods, IJCER 3(5) (2013)
Desai, S.R., Prasad, R.: A new approach to order reduction using stability equation and big bang big crunch optimization. Syst. Sci. Control Eng. Open Access J., Published online, Taylor and Francis, 1(1), 20–27, doi:10.1080/21642583. 2013. 804463, (2014)
Panda, S., Yadav., J.S., Patidar, N.P., Ardil, C.: Evolutionary techniques for model order reduction of large scale linear systems. Int. J. Electr. Comput. Energ. Electron. Commun. Eng. 6(9) (2012)
Alsmadi, O.M.K., Hammour, Z.S.A., Smadi, A.M.A.: Artificial neural network for discrete model order reduction with substructure preservation. Appl. Math. Model., Elsevier 35, 4620–4629 (2011)
Das, K.N., Mishra, R.: Chemo-inspired genetic algorithm for function optimization. Appl. Math. Comput. Elsevier, 220, 394–404 (2013)
Das, K.N., Mishra, R.: A performance study of chemo inspired genetic algorithm on benchmark functions. In: Proceedings of 7th international conference on Bio-inspired Computing: Theories and applications (BICTA-2012), Advances in Intelligent System and Computing, vol. 2, pp. 489–501, Springer (2013)
Kim, D.H., Abraham, A., Cho, J.H.: A hybrid genetic algorithm and bacterial foraging approach for global optimization. Information Sciences, 177(18), 3918–3937 (2007)
Chen, Y., Lin, W.: An improved bacterial foraging optimization. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics, Guilin, China, 19–23 Dec 2009
Lucas, T.N.: Continued- fraction expansion about two or more points: a flexible approach to linear system reduction. J. Franklin Inst. 323(1), 49–60 (1986)
Mukherjee, S., Mishra, R.N.: Order reduction of liner systems using an error minimization technique. J. Franklin Inst., Pergamon Journals Ltd, 323(1), 23–32 (1987)
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Rajashree Mishra, Das, K.N. (2016). Chemo-inspired Genetic Algorithm and Application to Model Order Reduction Problem. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_3
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