Locating all the global minima using multi-species particle swarm optimizer: The inertia weight and the constriction factor variants

2006 IEEE Congress on Evolutionary Computation, CEC 2006

Volumn , Issue , 2006, Pages 816-822

  Iwamatsu, Masao ^a,b  

^a IEEE   (Japan)

^b TOKYO CITY UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ANNEALINGSCHEDULE OPTIMIZATION; CONSTRICTION FACTOR VARIANTS; INERTIA-WEIGHT ANNEALING; MULTI-SPECIES PARTICLE SWARM OPTIMIZER (MSPSO); SEARCH SPACE;

CLUSTERING ALGORITHMS; FUNCTION EVALUATION; MODAL ANALYSIS; SCHEDULING; SIMULATED ANNEALING;

GLOBAL OPTIMIZATION;

EID: 34547322670     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (81)

1. Z. Michalewicz, D. B. Fogel, How to Solve It: Modern Heuristics, Springer-Verlag, Berlin Heidelberg New York, 2000.
2. J. Kennedy, R. C. Eberhart, Swarm Intelligence, Morgan Kaufmann, San Francisco, 2001.
3. J. Kennedy, R. C. Eberhart, "Particle swarm optimization," Proc. IEEE International Conference on Neural Networks, IEEE Press, Piscataway NJ, 1995, pp. 1942-1945.
4. P. J. Angelini, "Evolutionary optimization versus particle swarm optimization: Philosophy and performance difference," in Evolutionary Programming V, Lecture Notes in Computer Science, vol.1498, Edited by VS. Porto et al., Springer-Verlag, Berlin, 1998, pp. 601-610.
5. M. Iwamatsu, "Comparison of particle swarm and evolutionary programming as die global conformation optimizer of clusters," International Journal of Modern Physics C, vol. 16, No. 4, 2005, pp. 591-606.
6. I. Parmee, "Exploring the design potential of evolutionary search, exploration and optimization," in Evolutionary Design by Computers, Edited by PJ. Bentley, Morgan Kaufmann, San Francisco, 1999, pp. 117-143.
7. M. Iwamatsu, "Multi-species particle swarm optimizer for multimodal function optimization," IEICE Transactions on Information and System (English edition), vol. E89-D, No.3, 2006, pp. 1181-1187.
8. Y. Shi and R. C. Eberhart, "A modified particle swarm optimizer," Proc. IEEE International Conference on Evolutionary Computation, IEEE Press, Piscataway NJ, 1998, pp. 69-73.
9. M. Clerc and J. Kennedy, "The particle swarm-explosion, stability, and convergence in a multidimensional complex space," IEEE Transactions on Evolutionary Computation vol.6, No.1, 2002, pp. 58-73.
10. Y. Shi and R. C. Eberhart, "Parameter selection in particle swarm, optimization," in Evolutionary Programming V, Lecture Notes in Computer Science, vol.1498, Edited by VS. Porto et al., Springer-Verlag, Berlin, 1998, pp. 591-600.
11. R. C. Eberhart and Y. Shi, "Comparing inertia weight and constriction factors in particle swarm optimization," Proc. 2002 IEEE Congress on Evolutionary Computation (CEC02), IEEE Press, Piscataway NJ, 2002, pp. 84-88.
12. A. P. Engelbrecht, B. S. Masiye, and G. Pampará, "Niching ability of basic particle swarm optimization algorithms," Proc. 2005 IEEE Swarm. Intelligence Symposium. (SIS05), 2005, pp. 397-400.
13. K. Deb and W. M. Spears, "Speciation methods," in Evolutionary Computation 2, Edited by T. Back, D.B. Fogel, and Z. Michalewicz IOP Publishing, Bristol, 2000, pp. 93-100.
14. C-K. Chow and H-T. Tsui, "Autonomous agent response learning by a multi-species particle swarm optimization," Proc. IEEE Congress on Evolutionary Computation (CEC04), IEEE Press, Piscataway NJ, 2004, pp. 778-785.
15. M. Pelikan and D. E. Goldberg, "Genetic algorithms, clustering, and the breaking of symmetry," in Parallel Problem. Solving from Nature-PPSN VI, Lecture Notes in Computer Science, vol.1917, Edited by M. Shoenauer et al., Springer-Verlag, Berlin, 2000, pp. 385-394.
16. J-P. Li, M. E. Balaz, G. T. Parks, P. J. Clarkson, "A species conserving genetic algorithm for multimodal function optimization," Evolutionary Computation, vol.10, No.3, 2002, pp. 207-234.
17. J. Kennedy, "Small worlds and mega-minds, effects of neighborhood topology of particle swarm performance," Proc. 1999 IEEE Congress on Evolutionary Computation (CEC99), IEEE Press, Piscataway NJ, 1999, pp. 1931-1938.
18. J. Kennedy, R. Mendes, "Population structure and particle swarm performance," Proc. 2002 IEEE Congress on Evolutionary Computation (CEC02), IEEE Press, Piscataway NJ, 2002, pp. 1671-1676.
19. J. J. Liang and P. N. Sunganthan, "Dynamic multi-swarm particle swarm optimizer," Proc. 2005 IEEE Swarm Intelligence Symposium (SIS05), 2005, pp. 124-129.
20. D. Beasley, D.R. Bull, R.R. Martin, "A sequential niche technique for multimodal function optimization," Evolutionary Computation vol.1, No.2, 1993, pp. 101-125.
21. K. E. Parsopoulos and M. N. Vrahatis, "Modification of the particle swarm optimizer for locating all the global minima," in Artificial Neural Networks and Genetic Algorithms, Edited by V Kurova, et al., Berlin: Springer-Verlag, 2001, pp. 324-327.
22. K. E. Parsopoulos and M. N. Vrahatis, "On the computation of all global minimizers Üirough particle swarm Optimization," IEEE Transactions on Evolutionary Computation vol.8, No.3, 2004, pp. 211-224.
23. R. Brits, A.P. Engelbrecht, and F. van den Bergh, "Scalability of niche PSO," Proc. 2003 IEEE Swarm Intelligence Symposium (SIS03), 2003, pp. 228-234.
24. D. Parrott and X. Li, "A Particle swarm, model for tracking multiple peaks in a dynamic environment using speciation," Proc. 2004 IEEE Congress on Evolutionary Computation (CEC04), IEEE Press, Piscataway NJ, 2004, pp. 98-103.
25. 823-830
26. An Empirical Study on the Settings of Control Coefficients in Particle Swarm Optimization
27. N.M. Kwok, D.K. Liu, K.C. Tan and Q.P. Ha
28. ARC Centre of Excellence in Autonomous Systems (CAS), Faculty of Engineering, University of Technology, Sydney, Broadway, NSW 2007, Australia
29. ngai.kwok@eng.uts.edu.au
30. Dept. of Electrical and Computer Engineering, National University of Singapore, Singapore 119260, Republic of Singapore
31. The effects of randomness of control coefficients in Particle Swarm Optimization (PSO) are investigated through empirical studies. The PSO is viewed as a method to solve a coverage problem in the solution space when the global-best particle is reported as the solution. Randomness of the control coefficients, therefore, plays a crucial role in providing an efficient and effective algorithm. Comparisons of performances are made between the uniform and Gaussian distributed random coefficients in adjusting particle velocities. Alternative strategies are also tested, they include: i) pre-assigned randomness through the iterations, ii) selective hybrid random adjustment based on the fitness of the particles. Furthermore, the effect of velocity momentum factor is compared between a constant and random momentum. Numerical results show that performances of the proposed variations are comparable to the conventional implementation for simple test functions. However, enhanced performances using the selective and hybrid strategy are observed for complicate functions.
32. ©2006 IEEE
33. J. Kennedy and R. Eberhart, "Particle swarm optimization," Proc. IEEE Intl. Conf. on Neural Networks, Perth, Australia, Nov. 1995, pp. 1942-1948.
34. T. Ray and K. M. Liew, "Society and civilization: an optimization algorithm based on the simulation of social behavior," IEEE Trans. on Evolutionary Computation, vol. 7, no. 4, Aug. 2003, pp. 386-396.
35. R. Salomon, "Evolutionary algorithms and gradient search: similarities and differences," IEEE Trans. on Evolutionary Computation, vol. 2, no. 2, Jul. 1998, pp. 45-55.
36. S. L. Cheng and C. Hwang, "Optimal approximation of linear systems by a differential evolution algorithm," IEEE Trans. on Systems, Man, and Cybernetics ?Pt. A: Systems and Humans, vol. 31, no. 6, Nov. 2001, pp. 698-707.
37. N. Krasnogor and J. Smith, "A tutorial for competent memetic algorithms: model, taxonomy and design issues," IEEE Trans. on Evolutionary Computation, vol. 9, no. 3, Oct. 2005, pp. 474-488.
38. G. Ciuprina, D. loan and I. Munteanu, "Use of intelligent-particle swarm, optimization in electromagnetics," IEEE Trans. on Magnetics, vol. 38, no. 2, Mar. 2002, pp. 1037-1040.
39. G. Venter and J. S. Sobieski, "Particle swarm optimization," Proc. 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Material Conf., Denver, Colorado, Apr. 2002, AIAA 2002-1235.
40. Y. Zheng, L. Ma, L. Zhang and J. Qian, "Robust PID controller design using particle swarm, optimizer," Proc. 2003 IEEE Intl. Symposium on Intelligent Control, Houston, Texas, Oct. 2003, pp. 974-979.
41. S. Naka, T. Genji, T. Yura and Y. Fukuyama, "A hybrid particle swarm optimization for distributed state estimation," IEEE Trans. on Power Systems, vol. 18, no. 1, Feb. 2003, pp. 60-68.
42. D. W. Boeringer and D. H. Werner, "Particle swarm optimization versus genetic algorithms for phased array synthesis," IEEE Trans. on Antennas and Propagation, vol. 52, no. 3, Mar. 2004, pp. 771-779.
43. H. Zhang, X. Li, H. Li and F. Huang, "Particle swarm optimization-based schemes for resource-constrained project scheduling," Automation in Constructions, vol. 14, 2005, pp. 393-404.
44. S. L. Ho, S. Yang, G. Ni, E. W. C. Lo and H. C. Wong, "A particle swarm optimization-based method for multiobjective design optimizations," IEEE Trans. on Magnetics, vol. 41, no. 5, May 2005, pp. 1756-1759.
45. E. Ozcan and C. K. Mohan, "Particle swarm optimization: surfing the waves," Proc. 1999 Congress on Evolutionary Computation, Washington, DC, Jul. 1999, pp. 1939-1944.
46. M. Clerc and J. Kennedy, "The particle swarm - explosion, stability, and convergence in multidimensional complex space," IEEE Trans. on Evolutionary Computation, vol. 6, no. 1, Feb. 2002, pp. 58-73.
47. I. C. Trelea, "The particle swarm, optimization algorithm: convergence analysis and parameter selection," Information Processing Letters, vol. 85, 2003, pp. 317-325.
48. R. Mendes, J. Kennedy and J. Neves, "The fully informed particle swarm: simpler, maybe better," IEEE Trans. on Evolutionary Computation, vol. 8, no. 3, Jun. 2004, pp. 204-210.
49. K. E. Parsopoulos, V. P. Plagianakos, G. D. Magoulas and M. N. Vrahatis, "Objective function stretching to alleviate convergence to local minima," Nonlinear Analysis, vol. 47, 2001, pp. 3419-3424.
50. P. J. Angelinie, "Using selection to improve particle swarm optimization," Proc. 1998 IEEE Intl. Conf. on Evolutionary Computation, Anchorage, Alaska, May 1998, pp. 84-89.
51. M. Lovbjerg, T. K. Rasmussen and T. Krink, "Hybrid particle swarm optimiser with breeding and subpopulations," Proc. Genetic and Evolutionary Computation Conf. 2001, San Francisco, California, Jul. 2001, pp. 469-476.
52. T. Huang and A. Sanagavarapu, "A hybrid boundary condition for robust particle swarm, optimization," IEEE Antennas and Wireless Propagation Letters, vol. 4, 2005, pp. 112-117.
53. H. Fan, "A modification to particle swarm, optimization algorithm," Engineering Computations, vol. 19, no. 8, 2002, pp. 970-989.
54. K. Yasuda, A. Ide and N. Iwasaki, "Adaptive particle swarm, optimization," Proc. 2003 IEEE Intl. Conf. on Systems, Man and cybernetics, Washington, DC, Oct. 2003, pp. 1554-1559.
55. Y. Shi and R. Eberhart, "A modified particle swarm optimizer," Proc. 1998 IEEE Intl. Conf. on Evolutionary Computation, Anchorage, Alaska, May 1998, pp. 69-73.
56. J. Xu and Z. Xin, "An extended particle swarm optimizer," Proc. 19th IEEE Intl. Parallel and Distributed Processing Symposium, Apr. 2005, pp. 193-197.
57. A. Rodriguez and J. A. Reggia, "Extending self-organizing particle systems to problem solving," Artificial Life, vol. 10, 2004, pp. 379-395.
58. Z. Gajic and M. Lelic, Modern Control Systems Engineering, London: Prentice Hall, 1996.
59. 831-837
60. Can Ants Design Mechanical Engineering Systems?
61. Felipe Antonio Chegury Viana, Giovanni Iamin Kotinda, Domingos Alves Rade and Valder Steffen, Jr
62. Federal University of Uberlândia, School of Mechanical Engineering, 2121 João Naves de Ávila Av., Campus Santa Mônica, CEP 38400-902, Uberlândia, Brazil
63. fchegury@mecanica.ufu.br
64. Federal University of Uberlândia, School of Mechanical Engineering
65. kotinda@mecanica.ufu.br
66. Federal University of Uberlândia, School of Mechanical Engineering
67. domingos@ufu.br
68. The present contribution deals with the optimal tuning of a vibrating blade dynamic vibration absorber by using Ant Colony Optimization (ACO). Dynamic vibration absorbers (DVAs) are systems constituted by mass, spring and damping elements, which are coupled to a mechanical system to provide vibration attenuation. The main idea behind the DVAs is the generation of a force that has the same intensity as the excitation force but in the opposite phase. This phenomenon is known as anti-resonance. The tuning of the DVA is the procedure that sets the anti-resonance frequency to a given value by adjusting the DVA parameters. Based on this theory, the optimization problem is described as the minimization of the objective function that relates the difference between the resonance frequencies of the primary system and those of the DVA. To solve the optimization problem, ACO techniques were used. In the early nineties, when the Ant Colony algorithm was first proposed, it was used as an approach for the solution of combinatorial optimization problems, such as the traveling salesman problem. However, the extension for operating with continuous variables is recent and is still being developed. In this context, this paper presents an engineering application for a continuous domain problem. Numerical results are reported, illustrating the success of using the methodology presented, as applied to mechanical systems.
69. ©2006 IEEE
70. G. Venter and J. S. Sobieski, Particle Swarm Optimization, Proceedings of the 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Vol. AIAA 2002-1235, 2002.
71. M. Dorigo, Optimization, Learning and Natural Algorithms, Phd Thesis, Politecnico di Milano, Italy, 1992.
72. K. Socha, ACO for Continuous and Mixed-Variable Optimization, Proceedings in ANTS 2004 - Fourth International Workshop on Ant Colony Optimization and Swarm Intelligence, 2004.
73. M. Dorigo, V. Maniezzo and A. Colorni, 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.
74. Wikipedia contributors, Ant colony optimization, Wikipedia, The Free Encyclopedia, http://en.wikipedia.org/w/index.php?title= Ant_colony_optimization&oldid=32701004, accessed January 20, 2006.
75. S. H. Pourtakdoust and H. Nobahari, An Extension of Ant Colony to Continuous Optimization Problems, Proceedings in ANTS 2004 -Fourth International Workshop on Ant Colony Optimization and Swarm Intelligence, 2004.
76. H. Frahm, Device for Damping Vibrations of Bodies, US Patent 989, 1911, pp. 958.
77. J. P. Den Hartog, Mechanical Vibrations, 4th edition. McGraw-Hill, New York, 1956.
78. G. I. Kotinda, Vibrating Blade Dynamic Vibration Absorber (in Portuguese), M. Sc. Dissertation, Federal University of Uberlândia, MG - Brazil, 2006.
79. V. Steffen, Jr and D. A. Rade, Dynamic Vibration Absorber, Encyclopedia of Vibration, Academic Press, ISBN 0-12-227085-1, 2001, pp. 9-26.
80. D. A. Rade and V. Steffen, Jr, Optimisation of Dynamic Vibration Absorbers over a Frequency Band, Mechanical Systems and Signal Processing 14, 2000, pp. 679-690.
81. M. Yaman, Finite element vibration analysis of a partially covered cantilever beam with concentrated tip mass, Materials and Design, 27, 2006, pp 243-250.