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Celestial Navigation Fix Based on Particle Swarm Optimization

, 58, 315-335. 8. Kaplan, G. H. (1995) Determining the Position and Motion of a Vessel from Celestial Observations. NAVIGATION, Journal of The Institute of Navigation, 42(4), 631-648. 9. Kennedy, J. and Eberhart, R. (1997) A Discrete Binary Version of the Particle Swarm Optimization. Proceedings of IEEE International Conference on Neutral Networks, Perth, Australia, 4104-4108. 10. Metcalf, T. R. and Metcalf, F. T. (1991) On the Overdetermined Celestial Fix. NAVIGATION, Journal of The Institute of Navigation, 38(1), 79

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Particle Swarm Optimization Algorithm with Adaptive Chaos Perturbation

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

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A Novel Multi-Epoch Particle Swarm Optimization Technique

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

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Parameter Selection of a Support Vector Machine, Based on a Chaotic Particle Swarm Optimization Algorithm

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

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A Kernel Based Clustering Algorithm using Particle Swarm Optimization

References [1] B. T. Chen and M. Y. Chen, Applying particles swarm optimization for support vector machines on predicting company _nancial crisis, 2010 International Conference on Business and Economics Research, 1, (2011), 301-305 [2] C. Y. Chen and F. Ye, Particle Swarm Optimization Algorithm and Its Application to Clustering Analysis, Proceedings of the 2004 IEEE lnternational Conference on Networking, Sensing & Control, (2004), 789-794 [3] C. H. Chou, M. C. Su, and E. Lai, Symmetry as A New Measure

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Evaluating Geometric Characteristics of Planar Surfaces using Improved Particle Swarm Optimization

swarm optimization. Precision Engineering , 34, 338-344. [17] Luo, J., Wang, Q., Fu, L. (2012). Application of modified artificial bee colony algorithm to flatness error evaluation. Optics and Precision Engineering , 20, 422-430. [18] Lai, H.Y., Jywe, W.Y., Chen, C.K., Liu, C.H. (2000). Precision modeling of form errors for cylindricity evaluation using genetic algorithm. Precision Engineering , 24, 310-319. [19] Lee, C.T., Lee, C.C. (2014). On a hybrid particle swarm optimization method and its application in mechanism design. Proceedings of the

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A Novel Social Network Structural Balance Based on the Particle Swarm Optimization Algorithm

Networks. - Information Systems Frontiers, 2014, pp. 1-19. 10. Antal, T., P. L. Krapivsky, S. Redner. Dynamics of Social Balance on Networks. - Physical Review E, Vol. 72, 2005, No 3, pp. 36-121. 11. Kennedy, J. Particle Swarm Optimization. Encyclopedia of Machine Learning. Springer, US, 2010, pp. 760-766. 12. Huang, Y., H. Wu, Y. Xiao et al. Logistics RFID Network Optimization Based on Robust PSO under Uncertain Conditions. - Control Theory & Applications, Vol. 31, 2014, No 3, pp. 319-326. 13. Wang, G., J

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Multiobjective Improved Particle Swarm Optimisation for Transmission Congestion and Voltage Profile Management using Multilevel UPFC

References Albatsh, F. M., Mekhilef, S., Ahmad, S. and Mokhlis, H. (2017). Fuzzy-Logic-Based UPFC and Laboratory Prototype Validation for Dynamic Power Flow Control in Transmission Lines. IEEE Transactions on Industrial Electronics, 64(12), pp. 9538–9548. Batra, I. and Ghosh, S. (2018). An Improved Tent Map-Adaptive Chaotic Particle Swarm Optimization (ITM-CPSO)-Based Novel Approach Toward Security Constraint Optimal Congestion Management. Iranian Journal of Science and Technology, Transactions of Electrical Engineering , pp. 1–29. Dutta, S

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An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

, 2011, 1171-1181. [4] Benameur, L., J. Alami, and A. El Imrani. A New Hybrid Particle Swarm Optimization Algorithm for Handling Multiobjective Problem Using Fuzzy Clustering Technique. in Computational Intelligence, Modelling and Simulation, 2009. CSSim '09. International Conference on .2009, 48-53. [5] Blake, C.L., D.J. Newman, and C.J. Merz, UCI Repository of machine learning databases , University of California, Irvine, Dept. of Information and Computer Sciences 1998. [6] Bouyer, A., H. Ghafarzadeh, and O. Tarkhaneh, An Efficient Hybrid

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Particle Swarm Optimization for Solving a Class of Type-1 and Type-2 Fuzzy Nonlinear Equations

. 1110-1115. [7] G. Zhou and Y. Gan, Hybrid conjugate gradient method for solving fuzzy nonlinear equations, Logistics Systems and Intelligent Management, 2010 International Conference on, Harbin, 2010, pp. 462-464. [8] Zimmerman, H. J. (1996). Fuzzy Set theory and its Application (second edition), Allied Publishers, Indian Reprint. [9] J. Kennedy and R. Eberhart, Particle swarm optimization, Proc. IEEE Int. Conf. Neural Networks, vol. IV, pp. 1942-1948, 1995. [10] R.E. Moore, Interval analysis, Prentice-Hall, Englewood Cliffs, NJ, 1966

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