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Evolutionary Algorithm for Calculating Available Transfer Capability

Restoration Energy in Distribution Systems, Journal of Electrical Engineering 63 No. 1 (2012), 47-52. [13] ELMAOUHAB, A.-BOUDOUR, M.-GUEDDOUCHE, R. : New Evolutionary Technique for Optimization Shunt Capacitors in Distribution Networks, Journal of Electrical Engineering 62 No. 3 (2011), 163-167. [14] PERUMAL, M. P.-NANJUDAPAN, D. : Performance Enhancement of Embedded System BasedMultilevel Inverter Using Genetic Algorithm, Journal of Electrical Engineering 62 No. 4 (2011), 190-198. [15] ABOURA, S.-OMARI, A.-MEGUENNI, K. Z

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Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation

.—MOUSTAFA, M. A.—MANSOUR, M. M.: New Heuristic Strategies for Reactive Power Compensation of Radial Distribution Feeders, IEEE Trans. Power Delivery 17 No. 4 (Oct 2002), 1128-1135. MASOUM, M. A. S.—LADJEVARDI, M.—JAFARIAN, A.—FUCHS, E. F.: Optimal Placement, Replacement and Sizing of Capacitor Banks in Distorted Distribution Networks by Genetic Algorithms, IEEE Trans. Power Delivery 19 No. 4 (Oct 2004), 1794-1801. HAGHIFAM, M.-R.—MALIK, O. P.: Genetic Algorithm-Based Approach for Fixed and Switchable Capacitors

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Facial Composite System Using Genetic Algorithm

Applied Perception (TAP), 1.1: 19-39. 4. KURT, Binnur, et al. 2006. Active appearance model-based facial composite generation with interactive nature-inspired heuristics. In: Multimedia Content Representation, Classification and Security. Springer Berlin Heidelberg, p. 183-190. 5. GIBSON, Stuart J.; SOLOMON, Christopher J.; BEJARANO, Alvaro. 2003. Pallares Synthesis of Photographic Quality Facial Composites using Evolutionary Algorithms. In: BMVC, pp. 1-10. 6. SOLOMON, Christopher J., GIBSON, Stuart J., MIST, Joseph J

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Prediction of Scour Depth Around Bridge Piers Using Evolutionary Neural Network

. and Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm, Proceedings of IEEE Conference on Systems, Man, and Cybernetics, Iscataway, New Jersey,USA, p. 4104-4109. [16] Consoli, S., Moreno-Perez, J. A., Darby-Dowman, K. and Mladenovic, N. (2010). Discrete particle swarm optimisation for the minimum labelling steiner tree problem, Natural Computing, 9(1), pp. 29–46. [17] Seren, C. (2011). A hybrid jumping particle swarm optimisation method for high dimensional unconstrained discrete problems, IEEE Congress on Evolutionary

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An Efficient Functional Test Generation Method For Processors Using Genetic Algorithms

Processor Automatic Functional Test Generation based on Evolutionary Strategies, In: Proceedings of the 33 rd International Conference on Information Technology Interfaces ITI (V. Luzar-Stiffler, ed.), UCC Zagreb & IEEE CP, Cavtat/Dubrovnik, Croatia, June, 27-30, 2011, pp. 527–532. [21] HUDEC, J. : Processor Functional Test Generation — Some Results with using of Genetic Algorithms, In: Proceedings of the 2 nd Eastern European Regional Conference on the Engineering of Computer Based Systems (ECBS-EERC 2011), Bratislava, Slovakia, Sep, 5–6, 2011 (V. Vranić, ed

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Application and comparison of NSGA-II and MOPSO in multi-objective optimization of water resources systems

References Adeyemo, J.A., 2011. Reservoir operation using multiobjective evolutionary algorithm - A review. Asian Journal of Scientific Research, 4, 1, 16-27. DOI: 10.3923/ajsr.2011. Ajibola, A.S., Adewumi, A.O., 2014. Review of population based meta-heuristics in multi-objective optimization problems. International Journal of Computing, Communications & Instrumentation Engineering (IJCCIE), 1, 1, 126-128. Baltar, A.M., Fontane, D.G., 2006a. A Multi-objective Particle Swarm Optimization Model for Reservoir

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Present-day Problems and Methods of Optimization in Mechatronics

Abstract

It is justified that design is an inverse problem, and the optimization is a paradigm. Classes of design problems are proposed and typical obstacles are recognized. Peculiarities of the mechatronic designing are specified as a proof of a particle importance of optimization in the mechatronic design. Two main obstacles of optimization are discussed: a complexity of mathematical models and an uncertainty of the value system, in concrete case. Then a set of non-standard approaches and methods are presented and discussed, illustrated by examples: a fuzzy description, a constraint-based iterative optimization, AHP ranking method and a few MADM functions in Matlab.

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Variable Structure Neural Networks for Adaptive Robust Control Using Evolutionary Artificial Potential Fields

References [1] QIXIN, C.-YANWEN, H.-JINGLIANG, Z. : An Evolutionary Artificial Potential Field Algorithm for Dynamic Path Planning of Mobile Robot, IEEE/RSJ Int. conf. on Intelligent Robots and Systems, Beijing, Oct 2006, pp. 3331-3336. [2] SHI, D.-YEUNG, D. S.-GAO, J. : Sensitivity Analysis Applied to the Construction of Radial Basis Function Networks, Neural networks 18 (2005), 951-957. [3] LIU, G. P.-KADIRKAMANATHAN, V.-BILLINGS, S. A. : Variable Neural Networks for Adaptive Control of Nonlinear

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Optimal Dispatch of Ancillary Services Via Self-Organizing Migration Algorithm

Transactions on Power Systems 15 (2000), 1232-1239. DAMOUSIS, I. G.—BAKIRTZIS, A. G.—DOKOPOULOS, P. S.: Network-Constrained Economic Dispatch Using Real-Coded Genetic Algorithm, IEEE Transactions on Power Systems 18 (2002), 198-205. Grid Code. ČEPS, Czech Republic. 2006. Available on-line: http://www.ceps.cz/doc/kodex/part_i_ii_iii_an_fin_rev07.pdf HAVEL, P.—HORÁČEK, P.—FANTÍK, J.—JANEČEK, E.: Criteria for Evaluation of Power Balance Control Performance in UCTE Transmission Grid, in: 17th IFAC

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Controller design of isolated power-electricity island using genetic algorithm

R eferences [1] D. E. Goldberg, “Genetic Algorithms in Search, Optimisation And Machine Learning”, Addisson-Wesley, 1989. [2] A. E. Eiben and J. E. Smith, Introduction To Evolutionary Computing , Springer, 2003. [3] I. Sekaj “Evolučné výpočty a ich využitie v praxi”, Bratislava: Iris, 2005 (in Slovak). [4] I. Sekaj, “Control Algorithm Design Based On Evolutionary Algorithms”, Introduction To Modern Robotics , Hong Kong, Iconcept, Press Ltd., 2011, pp. 251–266. [5] I. Sekaj and M. Šrámek, “Robust Controller Design Based on Genetic

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