A CRITICAL APPROACH TO THE PARTICLE SWARM OPTIMIZATION METHOD FOR FINDING MAXIMUM POINTS

Abstract

Particle Swarm is an optimization method that is used for solving industrial problems and is highly preferred due to its ease of use and it’s ability to find accurate results rapidly in recent years. In this study, it was used to optimize the resistance value of train sets. There are many types of resistance in train sets and the train can't start moving until the traction motors overcome the resistances. Run resistance, ramp resistance, and curve resistance are the resistances that the train must overcome at a constant speed. However, it is known that the acceleration of high-speed trains is very high and the resistance that the train sets must overcome for the change in speeds is acceleration resistance. This study aimed to calculate the acceleration, time, curve, ramp and distance, under certain constraints, for the total resistance value of YHT 65000 train by using the Particle Swarm Method as to obtain the minimum and maximum. Although, the results showed that the Particle Swarm Method returned very successful results for the minimum resistance, the same cannot be said for the maximum resistance.

Keywords

• Railway Systems,Energy Efficiency,Optimization,Particle Swarm Method.

Kaynakça

1. [1] DOĞAN, H., YILANKIRKAN, N., “Türkiye’nin Enerji Verimliliği Potansiyeli ve Projeksiyonu”, Gazi Üniversitesi Fen Bilimleri Dergisi Part:C, Tasarım Ve Teknoloji GU J Sci Part:C 3(1):375-383 (2015).
2. [2] RSSB. T618 technical report: improving the efficiency of traction energy use. London: RSSB; 2007.
3. [3] ANDERSON, R., MAXWELL, R., HARRIS, N. “Maximizing the potential for metros to reduce energy consumption and deliver low-carbon transportation in cities”, Working paper for the CoMET and Nova Metro Benchmarking Groups; 2009.
4. [4] KOKKEN, K. “The reduction of energy consumption in EMU trains”, 6th World congress on railway research (WCRR), Edinburgh; 2003.
5. [5] BOCHARNIKOV, Y., TOBIAS, A., ROBERTS, C., HILLMANSEN, S., GOODMAN, C. “Optimal driving strategies for traction energy saving on DC suburban railways”, IEEE Electr Power Appl 2007;1:675–82.
6. [6] CORAPI, G., DE MARTINIS, V., PLACIDO, A., DE LUCA, G. “Impacts of energy saving strategies (ESSs) on rail services and related effects on travel demand”, WIT transactions on the built environment, Rome; 2014.
7. [7] HULL, G., ROBERTS, C., HILLMANSEN, S. “Simulation of energy efficiency improvement on commuter railways”, IET conference on railway traction systems, Birmingham; 2010.
8. [8] HU, W., SUN, Q., LV, J. “Research on subway trains’ energy conservation running based on PSO”, International conference on information science, electronics and electrical engineering (ISEEE), Sapporo; 2014.

9. [9] RODRIGO, E., TAPIA, S., MERA, JM., SOLER, M. “Optimizing electric rail energy consumption using the lagrange multiplier technique”, J Transp Eng 2013;139(3):321–9.
10. [10] XIN, T., ROBERTS, C., HE, J., HILLMANSEN, S., ZHAO, N., CHEN, L., ve diğerleri “Railway vertical alignment optimisation at stations to minimise energy”, IEEE 17th international conference on intelligent transportation systems, Qingdao; 2014.
11. [11] DAVIS Jr., W.J., “The tractive resistance of electric locomotives and cars”, Gen. Electr. Rev, 29, 2-24, 1926.
12. [12] HARA, T., OHKUSHI, J., NISHIMURA, B., “Aerodynamic drag of trains”, Q. Rep. RTRI, 8 (4), 226-229, 1967.
13. [13] Report ERRI C, 179/RP 9, Utrecht, April, 1993.
14. [14] ROCHARD, B.P., SCHMID, F., “A review of methods to measure and calculate train resistances”, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 214(4), 185-199, 2000.
15. [15] LUKASZEWICZ, P., “A simple method to determine train running resistance from full-scale measurements”, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 221(3), 331-337, 2007.
16. [16] LUKASZEWICZ P., “Running resistance - Results and analysis of full-scale tests with passenger and freight trains in Sweden”, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 221 (2), 183-193, 2007.
17. [17] EN 14067-4 (2005) - A1 (2009). Railway applications - Aerodynamics – Part 4: Requirements and test procedures for aerodynamics on open track.
18. [18] SOMASCHINI, C., ROCCHI, D., TOMASINI G. and SCHITO P. “Simplified Estimation of Train Resistance Parameters: Full Scale Experimental Tests and Analysis”, Proceedings of the Third International Conference on Railway Technology: Research, Development and Maintenance 2016; Paper 58.
19. [19] DORIGO, M., MANIEZZO, V., COLORNI A., “The Ant System: Optimization by a colony of cooperating agents”, IEEE Transactions on Systems, Man, and Cybernetics–Part B, Vol.26, No.1, 1996, pp.1-13. DOI: 10.1109/3477.484436.
20. [20] STUTZLE, T. ve HOOS, HH, “The Max-Min ANT System and Local Search for Combinatorial Optimization Problems”, June 2000 Future Generation Computer Systems 16. DOI: 10.1007/978-1-4615-5775-3_22.
21. [21] EBERHART, R. C. and KENNEDY, J., “A new optimizer using particle swarm theory”, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 39-43. Piscataway, NJ: IEEE Service Center, 1995.
22. [22] KARABOGA, D., BASTURK, B., “Sayısal fonksiyon optimizasyonu için güçlü ve verimli bir algoritma: Yapay arı kolonisi (ABC) algoritması”, Journal Of Global Optimazition 39(3) (2007).
23. [23] PAN, W., “A new Fruit Fly Optimization Algorithm”, Taking the financial distress model as an example”, Knowledge-Based Systems, Volume 26, February 2012, Pages 69-74. DOI: 10.1016/j.knosys.2011.07.001
24. [24] YANG, X.S. DEB, S., “Cuckoo Search via Lévy flights”, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 9-11 Dec. 2009, DOI:10.1109/NABIC.2009.5393690.
25. [25] GANDOMI, A.H., ALAVI A.H., “Krill Herd: A new bio-inspired optimization algorithm”, Communications in Nonlinear Science and Numerical Simulation Volume 17, Issue 12, December 2012, Pages 4831-4845. DOI: 10.1016/j.cnsns.2012.05.010.
26. [26] PASSINO, K.M., “Biomimicry of bacterial foraging for distributed optimization and control”, IEEE Control Systems Magazine, Volume: 22 , Page(s): 52 - 67 Issue: 3 , June 2002. DOI: 10.1109/MCS.2002.1004010.
27. [27] YANG, X.S., “A New Metaheuristic Bat-Inspired Algorithm”, International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO 2008), Tenerife Spain, (2008).
28. [28] YANG X.S., “Lecture Notes in Computer Science ”, 5792 (2009) 169-178.
29. [29] RAJAKUMAR B.R., Procedia Technology 6 (2012) 126-135.
30. [30] MIRJALILI, S., MIRJALILI, S.M., LEWIS A., “Grey Wolf Optimizer”, Advances in Engineering Software Volume 69, March 2014, Pages 46-61. DOI: 10.1016/j.advengsoft.2013.12.007.
31. [31] KAVEH, A., FARHOUDI, N., “A new optimization method: Dolphin echolocation”, May 2013 Advances in Engineering Software 59:53–70. DOI: 10.1016/j.advengsoft.2013.03.004.
32. [32] MEHRABIAN, A.R., LUCAS C., “A novel numerical optimization algorithm inspired from weed colonization”, Ecological Informatics 1 (4): 355-366 (2006) . DOI:10.1016/j.ecoinf.2006.07.003.
33. [33] UYMAZ, S.A., TEZEL, G., YEL E., “Artificial algae algorithm with multi-light source for numerical optimization and applications”, Biosystems. 2015 Dec;138:25-38. DOI: 10.1016/j.biosystems.2015.11.004. Epub 2015 Nov 10.
34. [34] LI, M.D., ZHAO, H., WENG, X.W., HAN T., “A novel nature-inspired algorithm for optimization: Virus colony search”, Advances in Engineering Software 92:65-88, February 2016. DOI: 10.1016/j.advengsoft.2015.11.004.
35. [35] ABEDINIA, O., AMJADY, N., GHASEMI, A., “A new metaheuristic algorithm based on shark smell optimization”, Complexity Volume 21, Issue 5, 2014. DOI: 10.1002/cplx.21634.
36. [36] YU, J.J.Q., LI, V.O.K., “A Social Spider Algorithm for Global Optimization”, Applied Soft Computing, 2015, v. 30, p. 614-627. DOI: 10.1016/j.asoc.2015.02.014.
37. [37] ERDOĞMUŞ, P., “Doğadan Esinlenen Optimizasyon Algoritmaları ve Optimizasyon Algoritmalarının Optimizasyonu”, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 4 (2016) 293-304.
38. [38] LIANG, J.J., QIN, A.K., SUGANTHAN, P.N., BASKAR, S., “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Transaction on Evolutionary Computation, Vol. 10(3), pp. 281-295, 2006.
39. [39] SEVKLI, Z., SEVILGEN, E., “A Hybrid Particle Swarm Optimization Algorithm for Function Optimization”, EvoWorkshop’08. LNCS Vol. 4974, pp. 585-595, 2008.
40. [40] VAN DEN BERG, F., ENGELBRECHT, A., “A new locally convergent particle swarm optimizer”, Proceeding of IEEE Conference on System, Man and Cybernetics, pp. 96-101, 2002.
41. [41] FAN, S.K., ZAHARA, E. “A hybrid simplex search and particle swarm optimization for unconstrained optimization”, European Journal of Operational Research, Vol. 181, pp. 527-548, 2007.
42. [42] LIU, B., WANG, L., JIN, Y-H., TANG, F., HUANG, D.X., “Improved particle swarm optimization combined with chaos”, Chaos Solutions & Fractals Vol.25, pp. 1261-1271, 2005.
43. [43] SHELOKAR, P.S., SIARRY, P., JAYARAMAN, V.K. KULKARNI, B.D., “Particle swarm and ant colony algorithms hybridized for improved continuous optimization”, Applied Mathematics and Computation, Vol.188, pp.129-142, 2007.
44. [44] SEVKLI, A.Z., SEVILGEN F.E., “StPSO: Strengthened particle swarm optimization”, Turk J. Elec. Eng & Comp Sci, Vol.18, No.6, 2010, TÜBİTAK. doi:10.3906/elk-0909-18.