Search Results

1 - 10 of 409 items :

Clear All

REFERENCES 1. Balkacem K., Foudil, C. (2016), A virtual viscoelastic based aggregation model for self-organization of swarm robots system , TAROS 2016: Towards Autonomous Robotic Systems, 202–213, . 2. Brambilla M. Ferrante E., Birattari M., Dorigo M . (2013), Swarm robotics: a review from the swarm engineering perspective, Swarm Intel l., 7(1), 1-41. 3. Cheah C.C., Hou S.P., Slotine J.J. (2009), Region-based shape control for a swarm of robots, Automatica , 45(10), 2406-2411. 4. Christensen A.L., O’Grady R., Dorigo M. (2009), From fireflies to fault

References 1. Chen, C.-L., Hsu, T.-P. & Chang, J.-R. (2003) A Novel Approach to Determine the Astronomical Vessel Position. Journal of Marine Science and Technology, 11(4), 221-235. 2. Chiesa, A. and Chiesa, R. (1990) A Mathematical Method of Obtaining an Astronomical Vessel Position. The Journal of Navigation, 43, 125-129. 3. DeWitt, C. (1974) Optimal Estimation of a Multi-Star Fix. NAVIGATION, Journal of The Institute of Navigation, 21(4), 320-325. 4. Eberhart, R. and Kennedy, J. (1995) A new optimizer using particle swarm theory. Proceedings of the 6th

References 1. Kennedy, J., R. C. Eberhart. Particle Swarm Optimization. – In: Proc. of IEEE International Conference on Neutral Networks, Perth, Australia, [s. n.], 1995, pp. 1942-1948. 2. Clerc, M., J. Kennedy. The Particle Swarm-Explosion, Stability, and Convergence in a Multidimensional Complex Space. – IEEE Transactions on Evolutionary Computation, Vol. 6 , 2002, No 1, pp. 58-73. 3. Chatterjee, A., P. Siarry. Computational Intelligence in Image Processing. Berlin, Springer, 2013, pp. 57-65. 4. Vaisakh, K., P. Praveena, S. R. M. Rao, K. Meah. Solving Dynamic

Estimation and Collision Detection”, 2015 IEEE International Conference on Robotics and Automation (ICRA) , pp. 5290–5296. [8] Vishay Semiconductors, “Vsmf4710”, available online: , Rev.1.4 (25 th September 2013). [9] DeviationTX Project, available online: (10 th June 2016). [10] ViconMotion Systems Ltd. Vicon, “Datastream SDK”, available online: . [11] A. Kushleyev, V. Kumar and D. Mellinger, “Towards a Swarm of Agile Micro Quadrotors

References [1], last access date: September 08, 2012 [2] M. Dorigo and T. Stützle, Ant Colony Optimization, MIT Press, Cambridge, Massachusetts, 2004 [3] J. Kennedy, R.C. Eberhart, and Y. Shi, Swarm Intelligence, Morgan Kauf- mann, San Francisco, 2001 [4] J. Kennedy and R.C. Eberhart, Particle Swarm Optimization [5] Y. Shi and R.C. Eberhart, A Modied Particle Swarm Optimizer [6] M. Clerc, Particle Swarm Optimization, ISTE, London, U.K., 2006 [7] Xin-She Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press

References 1. Bozorg-Haddad, O., M. Solgi, H. A. Loáiciga. Meta-Heuristic and Evolutionary Algorithms for Engineering Optimization. Hoboken, USA, John Wiley & Sons Inc, 2017. 2. Kennedy, J., R. C. Eberhart. Particle Swarm Optimization. – In: Proc. of IEEE International Conference on Neural Networks, 1995, pp. 1942-1948. 3. Kiranyaz, S., T. Ince, A. Yildirim, M. Gabbouj. Evolutionary Artificial Neural Networks by Multi-Dimensional Particle Swarm Optimization. – Neural Networks, Vol. 22 , 2009, Issue 10, pp. 1448-1462. 4. Heo, J. S., K. Y. Lee, R. Garduno

Investigation of Swarm Theory and Applications. ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2009: 209-218. 12. Bae J., Kim Y. Adaptive controller design for spacecraft formation flying using sliding mode controller and neural networks. Journal of the Franklin Institute, 2012, 349(2): 578-603. 13. Wang H. Flocking of networked uncertain Euler–Lagrange systems on directed graphs. Automatica, 2013, 49(9): 2774-2779. 14. Cepeda-Gomez R., Olgac N., Sierra D

References [1] Bartz-Beielstein T. and Zaefferer M. Model-based methods for continuous and discrete global optimization. Applied Soft Computing , 55:154–167, 2017. [2] Cai X., Qiu H., Gao L., Jiang C., and Shao X. An efficient surrogate-assisted particle swarm optimization algorithm for high-dimensional expensive problems. Knowledge-Based Systems , 184:104901, nov 2019. [3] Chugh T., Rahat A., Volz V., and Zaefferer M. Towards Better Integration of Surrogate Models and Optimizers. In High-Performance Simulation-Based Optimization , pages 137–163. 2020. [4

References 1. Vapnik, V. N. The Nature of Statistical Learning Theory. NY, Springer-Verlag, 1995. 2. Vapnik, V. N. Estimation of Dependencies Based on Empirical Data. Berlin, Springer-Verlag, 1982. 3. Guo, Yaxiang, Shifei Ding. Advances in Support Vector Machines. – Computer Science, Vol. 38 , 2011, No 2, pp. 14-17. 4. Wu, Q., R. Law, E. Wu. A Hybrid-Forecasting Model Reducing Gaussian Noise Based on the Gaussian Support Vector Regression Machine and Chaotic Particle Swarm Optimization. – Information Sciences, Vol. 23 , 2013, No 8, pp. 96-110. 5. Ding, G., L

. Tambouratzis, Conditional Random Fields versus template-matching in MT phrasing tasks involving sparse training data, Pattern Recognition Letters, 53:44-52, 2015. [17] A.J. Booker, J.E. Dennis Jr., P.D. Frank, D.B. Serafini, V. Torczon, and M.W. Trosset, A Rigorous Framework for Optimization of Expensive Functions by Surrogates, Structural Optimisation, 17:1-13, 1999. [18] Y. Jin, Surrogate-assisted evolutionary computation: Recent advances and future challenges, Swarm and Evolutionary Computation, 1(2):61-70, 2011. [19] K. Stoeber, P. Wagner, J. Helbig, S. Koester, D. Stall