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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.6, No.11, Oct. 2014

Predator and Prey Modified Biogeography Based Optimization Approach (PMBBO) in Tuning a PID Controller for Nonlinear Systems

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Author(s)

Mohammed Salem, Mohamed. F. Khelfi

Index Terms

Biogeography Based Optimization; Predator And Prey; Modified Migration; PID Control; Nonlinear System; Genetic Algorithms; Inverted Pendulum

Abstract

In this paper an enhanced approach based on a modified biogeography optimization with predator and prey behavior (PMBBO) is presented. The approach uses several predators with new proposed prey’s movement formula. The potential of using a modified predator and prey model is to increase the diversification along the optimization process so to avoid local optima and reach the optimal solution quickly. The proposed approach is used in tuning the gains of PID controller for nonlinear systems (Mass spring damper and an inverted pendulum) and has given remarkable results when compared to genetic algorithm and classical BBO.

Cite This Paper

Mohammed Salem, Mohamed. F. Khelfi,"Predator and Prey Modified Biogeography Based Optimization Approach (PMBBO) in Tuning a PID Controller for Nonlinear Systems", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.11, pp.12-20, 2014. DOI: 10.5815/ijisa.2014.11.02

Reference

[1]D. Simon, Biogeography-based optimization, IEEE Transactions on Evolutionary Computation, 12(6), 702-713, 2008.

[2]M.R. Lohokare, S.S. Pattnaik, B.K. Panigrahi, and S Das, Accelerated biogeography-based optimization with neighborhood search for optimization, Applied Soft Computing, 13 (5), 2318-2342, 2013.

[3]H. Ma, and D. Simon, Blended biogeography-based optimization for constrained optimization. Engineering Applications of Artificial Intelligence, 24 (3), 517--525 , 2011

[4]H. Ma, D. Simon, M. Fei and Z. Xie, Variations of biogeography-based optimization and Markov analysis, 2013. Information Sciences, 220, 492-506, 2013.

[5]M.M. Sayed, M.S. Saad, H.M. Emara, and A.E.E. El-Zahab, A Novel Method for PID Tuning Using a Modified Biogeography-Based Optimization Algorithm, In: 24th Chinese Control and Decision Conference (CCDC),23-25 May 2012, Taiyuan, China, 2012

[6]M.A.C. Silva, L.dos S Coelho and R.Z. Freire, Biogeography-based Optimization approach based on Predator-Prey concepts applied to path planning of 3- Technologies and Factory Automation (ETFA),13-16 Sept. 2010, Cracow, Poland ,1-8 , 2010

[7]M.Higashitani, A. Ishigame, K.Yasuda, Particle Swarm Optimization Considering the Concept of Predator-Prey Behavior, IEEE Congress on Evolutionary Computation (CEC), 16-21 Jul 2006,Vancouver, Canada,434-437, 2006. 

[8]M.A. Johnson and M.H. Moradi, PID Controllers: New Identification and Design Methods. Springer, 2005.

[9]T.K. Teng, J.S. Shieh and C.S. Chen, Genetic algorithms applied in online auto tuning PID parameters of a liquid-level control system, “Transaction of the Institute of Measurement and control, 25(5), 433-450, 2003.

[10]L. Fan, and E.M. Joo, Design for Auto-tuning PID Controller Based on Genetic Algorithms, In: 4th IEEE Conference on Industrial Electronics and Applications (ICIEA 2009), 25-27 May 2009, Xian, China, 2009.

[11]I. Chiha, N. Liouane, and P. Borne, Tuning PID Controller Using Multiobjective Ant Colony Optimization, Applied Computational Intelligence and Soft Computing, 2012. 

[12]Y.B. Wang, X. Peng and B.Z. We, A new particle swarm optimization based auto-tuning of PID controller, Seventh International Conference on Machine Learning and Cybernetics, 12-15 Jul. 2008, Kunming, China, 2008.

[13]M.I. Solihin, L.F. Tack and M.L. Kean, Tuning of PID Controller Using Particle Swarm Optimization (PSO), International Conference on Advanced Science, Engineering and Information Technology, Selangor, 14-15 Jan. 2011, Kuala Lumpur, Malaysia, 2011.

[14]M. Salem and M.F. Khelfi, Application of Biogeography based optimization in tuning a PID controller for nonlinear systems, IEEE International Conference on Complex Systems (ICCS), 5-6 Nov 2012, Agadir, Morocco, 2012.

[15]A. Bagis, Determination of the PID Controller Parameters by Modified Genetic Algorithm for Improved Performance, journal of information science and engineering, (23), 1469-1480, 2007.

[16]H. Kundra, A. Kaur and V. Panchal, An integrated approach to biogeography based optimization with case based reasoning for retrieving groundwater possibility, in: 8th Annual Asian Conference and Exhibition on Geospatial Information, Technology and Applications, 18-20 Aug. 2009, Singapore, 2009.

[17]X. Li, A real-coded predator-prey genetic algorithm for multiobjective optimization, Evolutionary Multi-Criterion Optimization Lecture Notes in Computer Science, 2632, 207-221, 2003.

[18]M. Ovreiu, and D. Simon, Biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease, In: Genetic and Evolutionary Computation Conference, Portland, Oregon, USA, 1235-1242, 2010.

[19]V. Panchal, P. Singh, N. Kaur, and H. Kundra, Biogeography based satellite image classification. Int. J. of Comp. Sci. and Info. Security, 6, 269—274, 2009.

[20]D. Simon, A dynamic system model of biogeography-based optimization, Appl. Soft Comput. 11(8), 5652-5661, 2011.

[21]A.A. Aly, Model Reference PID Control of an Electro-hydraulic Drive, International Journal of Intelligent Systems and Applications (IJISA), Vol.4 No.11, October 2012.

[22]S. Sharma, V. Kumar, R. Kumar, Supervised Online Adaptive Control of Inverted Pendulum System Using ADALINE Artificial Neural Network with Varying System Parameters and External Disturbance, International Journal of Intelligent Systems and Applications(IJISA),Vol.4, No.8, July 2012.