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Parallel Pbil Applied to Power System Controller Design

, Hyper-Learning for Population-Based Incremental Learning in Dynamic Environment, In: IEEE Congress on Evolutionary Computation, 2009. [24] M. Ventresca, H. R. Tizhoosh, A diversity Maintaining Population Based Incremental Learning Algorithm, Information Sciences, 178, 2008, pp. 4038-4056 [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. [26] S. Yang

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Advanced Supervision Of Oil Wells Based On Soft Computing Techniques

] Gong, M., L.C.Jiao., H.F.Du. “Multiobjective innmure algorithm with nondominated nieghbordbased selection” Evolutionary Computation, Volume 16, pp. 225-255, 2008. [7] Shahab, D,M., ”Recent Development in Application of Artificial Intelligence in Petroleum Engineering”. paper SPE 89033. Society of Petroleum Engineers. 2005. [8] Popa A., Ramos R., Cover A. “Integration of Artificial Intelligence and Lean Sigma for Large-Field Production Optimization: Application to Kern River Field”, Paper SPE 97247, pp. 34-45, 2005. [9] Cordero, S., Moreno, F. “ Una

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Fine Tuning of Agent-Based Evolutionary Computing

References [1] P. Adamidis. Parallel evolutionary algorithms: A review. In Proceedings of the 4th Hellenic-European Conference on Computer Mathematics and its Applications (HERCMA 1998), Athens, Greece, 1998. [2] T. Bäck and H.-P. Schwefel. Evolutionary computation: An overview. In T. Fukuda and T. Furuhashi, editors, Proceedings of the Third IEEE Conference on Evolutionary Computation. IEEE Press, 1996. [3] A. Byrski, M. Kisiel-Dorohinicki, and E. Nawarecki. Agent-based evolution of neural network architecture. In M. Hamza, editor, Proc. of the

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Population Diversity Maintenance In Brain Storm Optimization Algorithm

. Eiben, R. Hinterding, and Z. Michalewicz, “Parameter control in evolutionary algorithms,” IEEE Transactions on Evolutionary Computation , vol. 3, no. 2, pp. 124–141, July 1999. [5] S. F. Adra, T. J. Dodd, I. A. Griffin, and P. J. Fleming, “Convergence acceleration operator for multiobjective optimization,” IEEE Transactions on Evolutionary Computation , vol. 12, no. 4, pp. 825–847, August 2009. [6] Y. Jin and B. Sendhoff, “A systems approach to evolutionary multiobjective structural optimization and beyond,” IEEE Computational Intelligence Magazine

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Swarm Intelligence Algorithm Based on Competitive Predators with Dynamic Virtual Teams

, IEEE Transactions on, vol. 45, 2015, pp. 191–204 [5] Shi Cheng, Yuhui Shi, Quande Qin, TO Ting, and Ruibin Bai, Maintaining population diversity in brain storm optimization algorithm, In Evolutionary Computation, 2014, IEEE, pp. 3230–3237 [6] Shi Cheng, Yuhui Shi, Quande Qin, Qingyu Zhang, and Ruibin Bai, Population diversity maintenance in brain storm optimization algorithm, Journal of Artificial Intelligence and Soft Computing Research, vol. 4, 2014, pp. 83–97 [7] Maurice Clerc and James Kennedy, The particle swarm-explosion, stability, and

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Optimization of Work Schedules Executed using the Flow Shop Model, Assuming Multitasking Performed by Work Crews

KASS program in scheduling”, Technical Transaction, ISSUE 2-B(6): 217-224, 2014. 5. M. Krzemiński, “KASS v.2.2. Scheduling Software for Construction with Optimization Criteria Description”, Acta Physica Polonica A, Vol. 130 No. 6: 1439 – 1442, 2016. 6. R. Marcinkowski, “Metody rozdziału zasobów realizatora w działalności inżynieryjno – budowlanej”, WAT, 2002 7. M. L. Pinedo, “Scheduling: Theory, Algorithms, and Systems” Springer, 2012 8. M. Rogalska, W. Bożejko, Z. Hejducki, “Time/cost optimization using hybrid evolutionary algorithm in

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Directed Evolution – A New Metaheuristc for Optimization

evolution techniques to solve hard combinatorial problems, Proceedings of the Computer Science & Information Technologies Conference. CSIT 2009, p. 225-229. [5] Berlik, S., Directed Evolutionary Algorithms by Means of the Skew-Normal Distribution, In S. Co. 2009 Sixth Conference. Complex Data Modeling and Computationally Intensive Statistical Methods for Estimation and Prediction. Maggioli Editore, 2009, p.67. [6] Rotar, C., Directed Evolution-a Bio-inspired Optimization Technique, Proceedings of International Conference on Theory and Applications in

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Enhancing Island Model Genetic Programming by Controlling Frequent Trees

, in Proceedings of the 5th International Conference on Genetic Algorithms (1993), pp. 177–183 [5] D. Whitley, Statistics and Computing 4, 65 (1994) [6] J.J.G. Chrisila B. Pettey, Michael R. Leuze, in Proceeding Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application (1987), pp. 155–161 [7] D. Andre, J.R. Koza, in Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications PDPTA’96, Volume III (1996), pp. 1163–1174 [8] W.F. Punch, in

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B-Tree Algorithm Complexity Analysis to Evaluate the Feasibility of its Application in the University Course Timetabling Problem

. [4] R. Johnsonbaugh, Discrete Mathematics, 6th Edition, Prentice Hall, U.S.A., ISBN 0-13-117686-2, ISBN 0-13-117686-2. [5] B. Paechter, R. C. Ranking, A. Cumming and T. C. Fogarty, Timetabling the Classes of an Entire University with an Evolutionary Algorithm. Parallel Problem Solving from Nature (PPSN) V. Lectures Notes in Computer Science 1498, Springer-Verlag, Berlin, pp. 865-874, 1998. [6] O. Rossi-Doria, M. Samples, M. Birattari, M. Chiarandini, M. Dorigo, L. M. Gambardella, J. Knowles, M. Manfrin, M. Mastrolilli, B. Paechter, L

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Cuckoo Search Algorithm for Optimal Placement and Sizing of Static Var Compensator in Large-Scale Power Systems

problems. International Journal of Industrial Engineering Computations, 3(4), 535-560. [17] Ratnaweera, A., Halgamuge, S. K., & Watson, H. C. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients.Evolutionary Computation, IEEE Transactions on, 8(3), 240-255. [18] Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied mathematics and computation, 188(2), 1567-1579

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