Bare-bones particle swarm optimization with disruption operator

Applied Mathematics and Computation

Volumn 238, Issue , 2014, Pages 106-122

  Liu, Hao ^a,b   Ding, Guiyan ^b   Wang, Bing ^a  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY LIAONING   (China)

Author keywords

Bare bones PSO (BPSO); Disruption operator; Particle swarm optimization (PSO); Swarm intelligence

Indexed keywords

ARTIFICIAL INTELLIGENCE; ASTROPHYSICS; BONE;

BARE-BONES PARTICLE SWARM OPTIMIZATIONS; BARE-BONES PSO (BPSO); DISRUPTION OPERATOR; EXPLORATION AND EXPLOITATION; NONLINEAR FUNCTIONS; POPULATION DIVERSITY; PRE-MATURE CONVERGENCES; SWARM INTELLIGENCE;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 84899506029     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2014.03.152     Document Type: Article

References (36)

1. J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of IEEE International Conference on Neural Network, Piscataway, NJ, 1995, pp. 1942-1948.
2. G. He, and N.J. Huang A modified particle swarm optimization algorithm with applications Appl. Math. Comput. 219 2012 1053 1060
3. Y. Marinakis, G.R. Iordanidou, and M. Marinaki Particle swarm optimization for the vehicle routing problem with stochastic demands Appl. Soft Comput. 13 2013 1693 1704
4. L.D.S. Coelho Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems Expert Syst. Appl. 37 2010 1676 1683
5. Y. Jiang, X.Y. Li, C.C. Huang, and X.N. Wu Application of particle swarm optimization based on CHKS smoothing function for solving nonlinear bilevel programming problem Appl. Math. Comput. 219 2013 4332 4339
6. Y. Shi, and R.C. Eberhart A modified particle swarm optimizer Proceedings of the Conference on Evolutionary Computation 1998 IEEE Press Piscataway 69 73
7. Y. Shi, R.C. Eberhart, Fuzzy adaptive particle swarm optimization, in: Proceedings of IEEE Congress on Evolutionary Computation, vol. 1, 2001, pp. 101-106. (Pubitemid 32806325)
8. M. Clerc, and J. Kennedy The particle swarm: explosion, stability and convergence in a multi-dimensional complex space IEEE Trans. Evol. Comput. 6 1 2002 58 73 (Pubitemid 34404564)
9. A. Ratnaweera, S. Halgamuge, and H. Watson Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients IEEE Trans. Evol. Comput. 8 3 2004 40 255
10. J. Kennedy, Bare bones particle swarms, in: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, 2003, pp. 80-87.
11. R.A. Krohling, E. Mendel, Bare bones particle swarm optimization with Gaussian or Cauchy jumps, in: Proceedings of IEEE Congress on Evolutionary Computation, Trondheim, Norway, 2009, pp. 3285-3291.
12. H. Zhang, D.D. Kennedy, G.P. Rangaiah, and A. Bonilla-Petriciolet Novel bare-bones particle swarm optimization and its performance for modeling vapor-liquid equilibrium data Fluid Phase Equilib 301 2011 33 45
13. M.G.H. Omran, A.P. Engelbrecht, and A. Salman Bare bones differential evolution Eur J Oper Res 196 2009 128 139
14. C.H. Chen, J.S. Sheu, Unified bare bone particle swarm for economic dispatch with multiple fuel cost functions, in: 2011 7th Asia-Pacific International Conference on Lightning, Chengdu, China, 2011, pp. 214-219.
15. Y. Zhang, D.W. Gong, and Z.H. Ding A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch Inform. Sci. 199 2012 213 227
16. J.Z. Yao, D.F. Han, Improved barebones particle swarm optimization with neighborhood search and its application on ship design, Mathematical Problems in Engineering, vol. 2013, Article ID 175848, 2013, 12 pages.
17. H. Wang, Opposition-based barebones particle swarm for constrained nonlinear optimization problems, Mathematical Problems in Engineering, vol. 2012, Article ID 761708, 2012, 12 pages.
18. H.I. Hsieh, and T.S. Lee A modified algorithm of bare bones particle swarm optimization International Journal of Computer Science Issues 7 6 2010 12 17
19. T. Blackwell A study of collapse in bare bones particle swarm optimization IEEE Trans. Evol. Comput. 16 3 2012 354 372
20. M. Harwit, The Astrophyical Concepts, fourth ed., NewYork, 2006.
21. S. Sarafrazi, H. Nezamabadi-pour, and S. Saryazdi Disruption: A new operator in gravitational search algorithm Scientia Iranica, Transactions D 18 3 2011 539 548
22. J. Riget, and J. Vesterstroem A diversity-guided particle swarm optimizer-The ARPSO 2002 Department of Computer Science, University of Aarhus
23. X. Yao, Y. Liu, and G.M. Lin Evolutionary programming made faster IEEE Trans. Evol. Comput. 3 2 1999 82 102
24. P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y.-P. Chen, S. Andari, Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, in: Proceedings 2005 IEEE Congress on Evolutionary Computation, 2005, pp. 1-50.
25. J.J. Liang, A.K. Qin, P.N. Suganthan, and S. Baskar Comprehensive learning particle swarm optimizer for global optimization of multimodal functions IEEE Trans. Evol. Comput. 10 3 2006 281 295 (Pubitemid 43821759)
26. F. Van den Bergh, An analysis of Particle Swarm Optimizers, Ph.D. thesis, Department of Computer Science, University of Pretoria, South Africa, 2002.
27. Z.H. Zhan, J.Z. Zhan, Y. Li, and H.S.H. Chung Adaptive particle swarm optimization IEEE Trans. Syst. Man Cybern. Part B: Cybern. 39 6 2009 1362 1381
28. S. Garca, A. Fernndez, J. Luengo, and F. Herrera Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power Inform. Sci. 180 2010 2044 2064
29. M.G.H. Omaran, M. Clerc, 2011, , accessed 15, January 2013
30. C. Igel, N. Hansen, and S. Roth Covariance matrix adaptation for multil-objective optimization Evol. Comput. 15 2007 1 28
31. D. Karaboga, and B. Basturk A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm J. Global. Optim. 39 2007 459C471
32. D. Karaboga, and B. Akay A comparative study of artificial bee colony algorithm Appl. Math. Comput. 214 2009 108C132
33. J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems IEEE Trans. Evol. Comput. 10 2006 646C657
34. A.K. Qin, and P.N. Suganthan Self-adaptive differential evolution algorithm for numerical optimization IEEE Trans. Evol. Comput. 1C3 2005 1785C1791
35. J. Derrac, S. Garcia, D. Molina, and F. Herrera A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms Swarm Evol. Comput. 1 2011 3C18
36. P. Civicioglu Backtracking Search Optimization Algorithm for numerical optimization problems Applied Mathematics and Computation 219 15 2013 8121 8144