An improved PSO algorithm with a territorial diversity-preserving scheme and enhanced exploration-exploitation balance

Swarm and Evolutionary Computation

Volumn 11, Issue , 2013, Pages 1-15

  Ostadmohammadi Arani, Behrooz ^a   Mirzabeygi, Pooya ^a   Shariat Panahi, Masoud ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

Author keywords

Composition test functions; Diversity preservation; Exploitation; Exploration; Particle swarm optimization; Premature convergence

Indexed keywords

BENCHMARK FUNCTIONS; COLLISION OPERATORS; EXPLOITATION; EXPLORATION EXPLOITATIONS; IMPROVED PSO ALGORITHMS; PARTICLE SWARM OPTIMIZATION ALGORITHM; PRE-MATURE CONVERGENCES; TEST FUNCTIONS;

NATURAL RESOURCES EXPLORATION; PARTICLE SWARM OPTIMIZATION (PSO);

CLUSTERING ALGORITHMS;

EID: 84878320141     PISSN: 22106502     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.swevo.2012.12.004     Document Type: Article

References (39)

1. J. Kennedy, R. Eberhart, Particle swarm optimization, in: IEEE International Conference on Neural Networks, 1995, 4, pp. 1942-1948.
2. R. Eberhart, Y. Shi, Comparison between genetic algorithms and particle swarm optimization, in: Proceedings of the Seventh International Conference on Evolutionary Programming VII, 1998, pp. 611-616.
3. R. Poli Analysis of the publications on the applications of particle swarm optimisation Journal of Artificial Evolution and Applications 2008 2008 1 10
4. T. Navalertporn, and N.V. Afzulpurkar Optimization of tile manufacturing process using particle swarm optimization Swarm and Evolutionary Computation 1 2 2011 97 109
5. R. Malviya, and D.K. Pratihar Tuning of neural networks using particle swarm optimization to model MIG welding process Swarm and Evolutionary Computation 1 4 2011 223 235
6. N.C. Sahooa, S. Gangulyb, and D. Das Multi-objective planning of electrical distribution systems incorporating sectionalizing switches and tie-lines using particle swarm optimization Swarm and Evolutionary Computation 3 2012 15 32
7. S.N. Deepa, and G. Sugumaran Model order formulation of a multivariable discrete system using a modified particle swarm optimization approach Swarm and Evolutionary Computation 1 4 2011 204 212
8. H. Izakiana, and W. Pedrycz A new PSO-optimized geometry of spatial and spatio-temporal scan statistics for disease outbreak detection Swarm and Evolutionary Computation 4 2012 1 11
9. Z. Jia, S. Hui, Eds. A two sub-swarm exchange particle swarm optimization considering exploration and exploitation, in: Second International Conference on Industrial Mechatronics and Automation (ICIMA) 30-31, May 2010, pp. 530-533.
10. M. Ben Ghalia, Particle swarm optimization with an improved exploration-exploitation balance, in: 51st Midwest Symposium on Circuits and Systems, MWSCAS 10-13 Aug, 2008, pp. 759-762.
11. Y. Wakasa, K. Tanaka, Y. Nishimura, Control-theoretic analysis of exploitation and exploration of the PSO algorithm, in: IEEE International Symposium on Computer-Aided Control System Design, IEEE Multi-Conference on Systems and Control, Yokohama, Japan, 2010, pp. 1807-1812
12. E. Özcan, and M. Yilmaz Particle swarms for multimodal optimization Adaptive and Natural Computing Algorithms 4431 2007 366 375
13. T. Ioan Cristian The particle swarm optimization algorithm: convergence analysis and parameter selection Information Processing Letters 85 6 2003 317 325
14. J. Kennedy Swarm intelligence A. Zomaya, Handbook of Nature-Inspired and Innovative Computing 2006 Springer US 187 219
15. R. Poli, J. Kennedy, and T. Blackwell Particle swarm optimization Swarm Intelligence 1 1 2007 33 57
16. R. Mendes, Population Topologies and Their Influence in Particle Swarm Performance, Ph.D. Thesis, Departamento de Informatica, Escola de Engenharia, Universidade do Minho, Portugal, 2004.
17. J. Kennedy, R. Mendes, Population structure and particle swarm performance, in: Proc. 2002 World Congr. Computational Intelligence, 2002, pp. 1671-1676.
18. A. Mohais, R. Mendes, C. Ward, C. Posthoff, Neighborhood re-structuring in particle swarm optimization, in: Proc. 18th Australian Joint Conf. Artificial Intell. 2005,3809, pp. 776-785.
19. S. Janson, and M. Middendorf A hierarchical particle swarm optimizer and its adaptive variant IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics 35 6 2005 1272 1282
20. T. Peram, K. Veeramachaneni, C.K. Mohan, Fitness-distance-ratio based particle swarm optimization, in: Proc. Swarm Intelligence Symp, 2003, pp. 174-181.
21. J.J. Liang, A.K. Qin, P.N. Suganthan, and S. Baskar Comprehensive learning particle swarm optimizer for global optimization of multimodal functions IEEE Transaction on Evolutionary Computation 10 3 2006 281 295
22. D. Nasir Md, S. Das, S. Sengupta, U. Haldar, and P.N.A. Suganthan Dynamic neighborhood learning based particle swarm optimizer for global numerical optimization Information Sciences 209 2012 16 36
23. K.E. Parsopoulos, M.N. Vrahatis, editors. UPSO: A Unified Particle Swarm Optimization Scheme. Lecture Series on Computational Sciences, 2004, pp. 868-873.
24. J.J. Liang, P.N. Suganthan, Dynamic multi-swarm particle swarm optimizer with local search, in: Proc. IEEE Congr. Evol. Comput. 2005, pp. 522-528.
25. S.Z. Zhao, P.N. Suganthan, Q.-K. Pan, and M.F. Tasgetiren Dynamic multi-swarm particle swarm optimizer with harmony search Expert Systems with Applications 38 10 2011 3735 3742
26. X.F. Xie, W.J. Zhang, Z.L. Yang, Hybrid particle swarm optimizer with mass extinction, in: Proc. Int. Conf. Commun., Circuits Syst. 2002, 2, pp. 1170-1173.
27. T. Krink, J.S. Vesterstrom, J. Riget, Particle swarm optimization with spatial particle extension, in: Proc. IEEE Congr. Evol. Comput. 2002, 2, pp. 1474-1479.
28. C.K. Monson, K.D. Seppi, Adaptive diversity in PSO. GECCO '06: Proceedings of the Conference on Genetic and Evolutionary Computation, New York, NY, 2006, pp. 59-66.
29. M. Lovbjerg, T. Krink, Extending particle swarm optimizers with self-organized criticality, in: Proc. Congr. Evol. Comput. 2002, pp. 1588-1593.
30. J. Riget, J.S. Vesterstrom, A Diversity-Guided Particle Swarm Optimizer - The ARPSO. Denmark: Department of Computer Science, University of Aarhus; 2002. EVALife Technical Report no, 2002.
31. M. Pant, T. Radha, V.P. Singh, Eds. A Simple Diversity Guided Particle Swarm Optimization, in: IEEE Cong. on Evolutionary Computation (CEC 07), Singapore, 2007, pp. 3294-3299.
32. D. Xu, and X. Ai An improved diversity guided particle swarm optimization H. Wang, Y. Shen, T. Huang, Z. Zeng, The Sixth International Symposium on Neural Networks (ISNN 2009) 2009 Springer Berlin/Heidelberg 623 630
33. S.Z. Zhao, and P.N. Suganthan Diversity enhanced particle swarm optimizer for global optimization of multimodal problems IEEE Congress on Evolutionary Computation, Norway 2009 590 597
34. N. Hansen, S.D. Müller, and P. Koumoutsakos Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES) Evolutionary Computation 11 1 2003 1 18
35. J. Derrac, S. García, 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 and Evolutionary Computation 1 1 2011 3 18
36. P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y.-P. Chen, A. Auger, et al. Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. IIT Kanpur, India: Nanyang Technological University Singapore, and KanGAL Report 2005.
37. J.J. Liang, P.N. Suganthan, K. Deb, Novel composition test functions for numerical global optimization, in: Proceedings of IEEE Swarm Intelligence Symposium 2005, pp. 182-191.
38. A. Ahrari, M.R. Saadatmand, M. Shariat-Panahi, and A.A. Atai On the limitations of classical benchmark functions for evaluating robustness of evolutionary algorithms Applied Mathematics and Computation 215 9 2010 3222 3229
39. M. Gaviano, and D. Lera Test functions with variable attraction regions for global optimization problems Journal of Global Optimization 13 2 1998 207 223