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PID Controller Design Based on Global Optimization Technique with Additional Constraints

References [1] DINGYU, X.-YANGQUAN, C. : OptimPID: A MATLAB Interface for Optimum PID Controller Design, 2005, [2] MARTINS, F. G. : Tuning PID Controllers using the ITAE Criterion, International Journal of Engineering Education 21 (2005), 867-873. [3] LEVINE, S. : The Control Handbook, Second Edition, CRC Press, 2010. [4] MathWorks Inc (2014b). Global Optimization Toolbox User’s Manual

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Particle Swarm Clustering Optimization - a novel Swarm Intelligence approach to Global Optimization

Gravitational Search Algorithm, Information Sciences, 179 (13), (2009), 2232-2248 [11] Hamed Shah-Hosseini, The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm, International Journal of Bio-Inspired Compu- tation (IJBIC), 1 (1/2), (2009), 71-79 [12] A. Kaveh and S. Talatahari, A Novel Heuristic Optimization Method: Charged System Search, Acta Mechanica, 213, (2010), 267-289 [13] J. D. Pintér, Global Optimization: Software, Test Problems, and Applications, Ch. 15 in Handbook of Global

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A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization

comparative modeling techniques, Proteins: Structure Function and Genetics 23(4): 463. Hendrickson, B. (1995). The molecule problem: Exploiting structure in global optimization, SIAM Journal of Optimization 5(4): 835. Hertz, J., Krogh, A. and Palmer, R.G. (1991). Introduction to the Theory of Neural Computation , Addison-Wesley, Redwood City, CA. Holland, J.H. (1975). Adaptation in Natural and Artificial Systems , University of Michigan Press, Ann Arbor, MI. Moscato

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Is Differential Evolution Rotationally Invariant?

, 2008, pp. 33–88. [5] How symmetry constrains evolutionary optimizers . In: Proc. of the IEEE Congress on Evolutionary Computation—CEC ‘17, Donostia-San Sebastián, Spain, 2017, IEEE, New York, pp. 1712–1719. [6] PRICE, K.—STORN, R. M.—LAMPINEN, J. A.: Differential evolution: a practical approach to global optimization . Springer Science & Business Media, 2006. [7] STORN, R.: Differential evolution research—trends and open questions . In: Advances in Differential Evolution , Springer-Verlag, Berlin, 2008, pp. 1–31. [8] STORN, R.—PRICE, K

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Random perturbation of the projected variable metric method for nonsmooth nonconvex optimization problems with linear constraints

Programming 37 (3): 269-292. Peng, Y. and Heying, Q. (2009). A filter-variable-metric method for nonsmooth convex constrained optimization, Applied Mathematics and Computation 208 (1): 119-128. Petersen, I. (2006). Minimax LQG control, International Journal of Applied Mathematics and Computer Science 16 (3): 309-323. Pinter, J. (1996). Global Optimization in Action , Kluwer, Dordrecht. Pogu, M. and Souza de Cursi, J. (1994). Global optimization by random perturbation

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The island model as a Markov dynamic system

, P. (1995). Handbook of Global Optimization , Kluwer, Norwell, MA. Iosifescu, M. (1980). Finite Markov Processes and Their Applications , John Wiley & Sons, Alphen aan den Rijn. Kołodziej, J. and Xhafa, F. (2011). Modern approaches to modeling user requirements on resource and task allocation in hierarchical computational grids, International Journal of Applied Mathematics and Computer Science 21 (2) 243-257, DOI: 10.2478/v10006-011-0018-x. Kowalczuk, Z. and Białaszewski, T. (2006). Niching mechanisms in

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Gradual and Cumulative Improvements to the Classical Differential Evolution Scheme through Experiments

References [1] K. Price and R. Storn, Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces, Journal of Global Optimization, 11 (4), (1997), 341 - 359 [2] R. Storn, On the usage of differential evolution for function optimization, Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), (1996), 519 - 523 [3] K. Price, R. Storn, and J. Lampinen, Differential Evolution - A Practical Approach to Global Optimization, Springer

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On Global Optimization of Walking Gaits for the Compliant Humanoid Robot, COMAN Using Reinforcement Learning


In ZMP trajectory generation using simple models, often a considerable amount of trials and errors are involved to obtain locally stable gaits by manually tuning the gait parameters. In this paper a 15 degrees of Freedom dynamic model of a compliant humanoid robot is used, combined with reinforcement learning to perform global search in the parameter space to produce stable gaits. It is shown that for a given speed, multiple sets of parameters, namely step sizes and lateral sways, are obtained by the learning algorithm which can lead to stable walking. The resulting set of gaits can be further studied in terms of parameter sensitivity and also to include additional optimization criteria to narrow down the chosen walking trajectories for the humanoid robot.

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Center-based l1–clustering method

Scitovski, R. (2009). Three points method for searching the best least absolute deviations plane, Applied Mathematics and Computation 215 (3): 983-994. Duda, R., Hart, P. and Stork, D. (2001). Pattern Classification , Wiley, New York, NY. Finkel, D.E. and Kelley, C.T. (2006). Additive scaling and the DIRECT algorithm, Journal of Global Optimization 36 (4): 597-608. Floudas, C.A. and Gounaris, C.E. (2009). A review of recent advances in global optimization, Journal of Global Optimization 45 (4): 3

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Using a vision cognitive algorithm to schedule virtual machines

rate, Cluster Computing 16(3): 575-589. Cai, Y., Qian, J. and Sun, Y. (2006). Outlook algorithm for global optimization, Journal of Guangdong University of Technology 23(2): 1-10. Calheiros, R.N., Ranjan, R., Beloglazov, A., e Rose, C.A.F.D. and Buyya, R. (2011). CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software: Practice and Experience 41(1): 23-50. Chen, D., Li, D., Xiong, M., Bao, H. and Li, X. (2010). GPGPU-aided ensemble

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