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Without doubt, one of the powerful and effective optimiser in the area of evolutionary algorithms and improved particle swarm optimisation (PSO) is the self-organising hierarchical PSO with time-varying acceleration coefficients (HPSO-TVAC) which has been implemented successfully in the many problems (cited by 2430 until now). Real-world problems are multi-variable problems with real-world different complexities. The classical HPSO-TVAC optimisation technique often converges to local optima solution for some of the real-world problems. Therefore, finding efficient modern versions of the PSO algorithm (here HPSO-TVAC) to solve the real-world problems are absorbing a growing attention in recent years. A novel HPSO-TVAC algorithm for real-world optimisation is proposed. The simulation results show that proposed HPSO-TVAC new version, NHPSO-JTVAC, is powerful and very competitive for real-world optimisation.
References
-
-
1)
-
2. Eberhart, R.C., Kennedy, J.: ‘A new optimizer using particle swarm theory’. Proc. Sixth Int. Symp. Micromachine Human Science, 1995, vol. 1, pp. 39–43.
-
2)
-
29. Ratnaweera, A., Halgamuge, S., Watson, H.: ‘Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients’, IEEE Trans. Evol. Comput., 2004, 8, (3), pp. 240–255 (doi: 10.1109/TEVC.2004.826071).
-
3)
-
3. Ran, C., Yaochu, J.: ‘A competitive swarm optimizer for large scale optimization’, Trans. Cybern., 2015, 45, (2), pp. 191–204 (doi: 10.1109/TCYB.2014.2322602).
-
4)
-
23. Liang, J.J., Qin, A.K., Suganthan, P.N., et al: ‘Comprehensive learning particle swarm optimizer for global optimization of multimodal functions’, IEEE Trans. Evol. Comput., 2006, 10, (3), pp. 281–295 (doi: 10.1109/TEVC.2005.857610).
-
5)
-
6. Suganthan, P.N., Hansen, N., Liang, J.J., et al: ‘Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization’. , Nanyang Technological University, Singapore, 2005.
-
6)
-
31. Wang, Y., Cai, Z.X., Zhang, Q.F.: ‘Differential evolution with composite trial vector generation strategies and control parameters’, IEEE Trans. Evol. Comput., 2011, 15, (1), pp. 55–66 (doi: 10.1109/TEVC.2010.2087271).
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