Parallel Pbil Applied to Power System Controller Design

[1] S. Baluja, Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning, CMU-CS-94-163, Carnegie Mellon University, 1994.Search in Google Scholar

[2] F. G. Lobo, and D.E. Golberg, The parameter-less genetic algorithm in practice, International Journal of Information Sciences 2004; 167, pp.217-32.10.1016/j.ins.2003.03.029Search in Google Scholar

[3] S. Baluja, and R. Caruana, Removing the Genetics from the Standard Genetic Algorithm, Tech. Rep. CMU-CS-95-141), Carnegie Mellon University, 1995.10.1016/B978-1-55860-377-6.50014-1Search in Google Scholar

[4] J. H. Holland, Adaptation in nature and artificial systems. The University of Michigan Press, 1975.Search in Google Scholar

[5] L. Davis, Handbook of genetic algorithms, International Thomson Computer Press, 1996.Search in Google Scholar

[6] D. E. Goldberg, Genetic algorithms in search, optimization & machine learning. Addison-Wesley, 1989.Search in Google Scholar

[7] K. Price, R.M. Storn, and J.A. Lampinen, Differential evolution: A practical approach to global optimization, Springer, ISBN 978-3-540-20950-8, 2005.Search in Google Scholar

[8] T. Mulumba, and K. A. Folly, Power system stabilizer design: comparative analysis between differential evolution and population- based incremental learning”, In: 20th Southern African Universities’ Power Engineering Conference (SAUPEC ), 2011.Search in Google Scholar

[9] M. Dorigo, and G Di Caro, The Ant Colony Optimization: a new teta-Heuristic, In: Evolutionary Computation (CEC), 1999.Search in Google Scholar

[10] J. F. Kennedy, R. C. Eberhart R.C., & Y. Shi, Swarm Intelligence. Morgan Kaufmann, 2001.Search in Google Scholar

[11] T. K. Das, and G.K. Venayagamoorthy ”Design of Power System Stabilizers using Small Population Based PSO,” IEEE PES General Meeting 2006.10.1109/PES.2006.1709322Search in Google Scholar

[12] J. R. Greene, Population-Based Incremental Learning as a Simple,Versatile Tool for Engineering Optimization, In: Proceedings of the First International Conf. on EC and Applications, 1996, pp.258-269Search in Google Scholar

[13] F. Southey, F. Karray, Approching Evolutionary Robotics through Population-Based Incremental Learning, In: IEEE International Conference on Systems, Man and Cybernetics, Vol. 2, 1999, pp. 710-715.Search in Google Scholar

[14] KA Folly, Performance Evaluation of power system stabilizers based on Population-Based Incremental Learning (PBIL) Algorithm, International Journal of Power and Energy Systems, Vol. 33, Issue 7, 2011, pp. 1279-1287.10.1016/j.ijepes.2011.05.004Search in Google Scholar

[15] K.A. Folly, Design of Power System Stabilizer: A Comparison Between Genetic Algorithms (GAs) and Population-Based Incremental Learning (PBIL), In: Proc. of the IEEE PES ,General Meeting, 200610.1109/PES.2006.1709635Search in Google Scholar

[16] K.A. Folly, Robust Controller Design Based on a Combination of Genetic Algorithms (GAs) and Competitive Learning, In: International Joint Conference on Neural Networks, 2007, pp. 3045-3050.10.1109/IJCNN.2007.4371446Search in Google Scholar

[17] P. Mitra, C. Yan, L. Grant, G.K. Venayagamoorthy, and K. Folly, Comparative Study of Population-Based Techniques for Power System Stabilizer Design, in Proc. of Intelligent System Applications to Power Systems, 2009.10.1109/ISAP.2009.5352927Search in Google Scholar

[18] P. Kundur, Power System Stability and Control. McGraw - Hill, Inc. 1994.Search in Google Scholar

[19] KA Folly, An Improved Population-Based Incremental Learning Algorithm, International Journal of Swarm Intelligence Research (IJSIR), Vol.4, No.1, 2013, pp. 35-61.10.4018/jsir.2013010102Search in Google Scholar

[20] C. Conzalez, J.A. Lozano and P. Larranaga, The convergence behavior of the PBIL Algorithm: A preliminary approach, In: Proc. of Artificial Neural Nets and Genetic Algorithms, 2001.10.1007/978-3-7091-6230-9_56Search in Google Scholar

[21] R. Rastegar, A. Hariri, M. Mazoochi, The Population-Based Incremental Learning Algorithm Converges to Local Optima, Neurocomputing, 69, 2006, pp. 1772-1775.10.1016/j.neucom.2005.12.116Search in Google Scholar

[22] KA Folly, G.K. Venayagamoorthy, Effect of Learning Rate on the Performance of the Population-Based Incremental Learning Algorithm, In: Proc. of the International Joint Conf. on Neural Network (IJCNN), 2009.10.1109/IJCNN.2009.5179080Search in Google Scholar

[23] S. Yang and H. Richter, Hyper-Learning for Population-Based Incremental Learning in Dynamic Environment, In: IEEE Congress on Evolutionary Computation, 2009.10.1109/CEC.2009.4983011Search in Google Scholar

[24] M. Ventresca, H. R. Tizhoosh, A diversity Maintaining Population Based Incremental Learning Algorithm, Information Sciences, 178, 2008, pp. 4038-405610.1016/j.ins.2008.07.005Search in Google Scholar

[25] S. Y. Yang, S.L. Ho, G.Z. Ni, J.M. Machado and K.F.Wong, A new Implementation of Population- Based Incremental Learning Method for Optimizations in Electromagnetics, IEEE Trans. On Magnetics 43 (4), 2007, pp. 1601-1604.10.1109/TMAG.2006.892112Search in Google Scholar

[26] S. Yang and X. Yao, Experimental Study on Population-Based Incremental Learning Algorithms for Dynamic Optimization Problems, Soft Computing, 9(11), 2005, pp. 815-83410.1007/s00500-004-0422-3Search in Google Scholar

[27] G. Rogers, Power system oscillations, Kluwer academic Publishers, 2000 10.1007/978-1-4615-4561-3Search in Google Scholar