Have a personal or library account? Click to login
Population Diversity Maintenance In Brain Storm Optimization Algorithm Cover

Population Diversity Maintenance In Brain Storm Optimization Algorithm

Open Access
|Mar 2015

References

  1. [1] K. A. De Jong, “An analysis of the behavior of a class of genetic adaptive systems,” Ph.D. dissertation, Department of Computer and Communication Sciences, University of Michigan, August 1975.
  2. [2] M. L. Mauldin, “Maintaining diversity in genetic search,” in Proceedings of the National Conference on Artificial Intelligence (AAAI 1984), August 1984, pp. 247–250.
  3. [3] D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc., 1989.
  4. [4] Á. E. Eiben, R. Hinterding, and Z. Michalewicz, “Parameter control in evolutionary algorithms,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 124–141, July 1999.10.1109/4235.771166
  5. [5] S. F. Adra, T. J. Dodd, I. A. Griffin, and P. J. Fleming, “Convergence acceleration operator for multiobjective optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 4, pp. 825–847, August 2009.10.1109/TEVC.2008.2011743
  6. [6] Y. Jin and B. Sendhoff, “A systems approach to evolutionary multiobjective structural optimization and beyond,” IEEE Computational Intelligence Magazine, vol. 4, no. 3, pp. 62–76, August 2009.10.1109/MCI.2009.933094
  7. [7] R. K. Sundaram, A First Course in Optimization Theory. Cambridge University Press, 1996.10.1017/CBO9780511804526
  8. [8] R. C. Purshouse and P. J. Fleming, “On the evolutionary optimization of many conflicting objectives,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 770–784, December 2007.10.1109/TEVC.2007.910138
  9. [9] S. F. Adra and P. J. Fleming, “Diversity management in evolutionary many-objective optimization,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 2, pp. 183–195, April 2011.10.1109/TEVC.2010.2058117
  10. [10] A. Engelbrecht, X. Li, M. Middendorf, and L. M. Gambardella, “Editorial special issue: Swarm intelligence,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 4, pp. 677–680, August 2009.10.1109/TEVC.2009.2022002
  11. [11] J. Kennedy, R. Eberhart, and Y. Shi, Swarm Intelligence. Morgan Kaufmann Publisher, 2001.
  12. [12] E. K. Burke, S. Gustafson, and G. Kendall, “A survey and analysis of diversity measures in genetic programming,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2002, pp. 716–723.
  13. [13] Y. Shi and R. Eberhart, “Population diversity of particle swarms,” in Proceedings of the 2008 Congress on Evolutionary Computation (CEC2008), 2008, pp. 1063–1067.
  14. [14] Y. Shi and R. Eberhart, “Monitoring of particle swarm optimization,” Frontiers of Computer Science, vol. 3, no. 1, pp. 31–37, March 2009.10.1007/s11704-009-0008-4
  15. [15] S. Cheng and Y. Shi, “Diversity control in particle swarm optimization,” in Proceedings of 2011 IEEE Symposium on Swarm Intelligence (SIS 2011), Paris, France, April 2011, pp. 110–118.10.1109/SIS.2011.5952581
  16. [16] S. Cheng, Y. Shi, and Q. Qin, “Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization: From Population Diversity Perspective,” International Journal of Swarm Intelligence Research (IJSIR), vol. 2, no. 3, pp. 43–69, July-September 2011.10.4018/jsir.2011070104
  17. [17] S. Cheng, “Population diversity in particle swarm optimization: Definition, observation, control, and application,” Ph.D. dissertation, Department of Electrical Engineering and Electronics, University of Liverpool, May 2013.
  18. [18] S. Cheng, Y. Shi, and Q. Qin, “A study of normalized population diversity in particle swarm optimization,” International Journal of Swarm Intelligence Research (IJSIR), vol. 4, no. 1, pp. 1–34, January-March 2013.10.4018/jsir.2013010101
  19. [19] Y. Shi, “Brain storm optimization algorithm,” in Advances in Swarm Intelligence, ser. Lecture Notes in Computer Science, Y. Tan, Y. Shi, Y. Chai, and G.Wang, Eds. Springer Berlin/Heidelberg, 2011, vol. 6728, pp. 303–309.10.1007/978-3-642-21515-5_36
  20. [20] Y. Shi, “An optimization algorithm based on brainstorming process,” International Journal of Swarm Intelligence Research (IJSIR), vol. 2, no. 4, pp. 35–62, October-December 2011.10.4018/ijsir.2011100103
  21. [21] X. Guo, Y. Wu, and L. Xie, “Modified brain storm optimization algorithm for multimodal optimization,” in Advances in Swarm Intelligence, ser. Lecture Notes in Computer Science, Y. Tan, Y. Shi, and C. A. C. Coello, Eds. Springer International Publishing, 2014, vol. 8795, pp. 340–351.10.1007/978-3-319-11897-0_40
  22. [22] J. Xue, Y. Wu, Y. Shi, and S. Cheng, “Brain storm optimization algorithm for multi-objective optimization problems,” in Advances in Swarm Intelligence, ser. Lecture Notes in Computer Science, Y. Tan, Y. Shi, and Z. Ji, Eds. Springer Berlin / Heidelberg, 2012, vol. 7331, pp. 513–519.10.1007/978-3-642-30976-2_62
  23. [23] L. Xie and Y. Wu, “A modified multi-objective optimization based on brain storm optimization algorithm,” in Advances in Swarm Intelligence, ser. Lecture Notes in Computer Science, Y. Tan, Y. Shi, and C. Coello, Eds. Springer International Publishing, 2014, vol. 8795, pp. 328–339.10.1007/978-3-319-11897-0_39
  24. [24] Z.-H. Zhan, W.-N. Chen, Y. Lin, Y.-J. Gong, Y. long Li, and J. Zhang, “Parameter investigation in brain storm optimization,” in 2013 IEEE Symposium on Swarm Intelligence (SIS), April 2013, pp. 103–110.10.1109/SIS.2013.6615166
  25. [25] S. Cheng, Y. Shi, Q. Qin, and S. Gao, “Solution clustering analysis in brain storm optimization algorithm,” in Proceedings of The 2013 IEEE Symposium on Swarm Intelligence, (SIS 2013). Singapore: IEEE, 2013, pp. 111–118.10.1109/SIS.2013.6615167
  26. [26] S. Cheng, Y. Shi, Q. Qin, T. O. Ting, and R. Bai, “Maintaining population diversity in brain storm optimization algorithm,” in Proceedings of 2014 IEEE Congress on Evolutionary Computation, (CEC 2014). Beijing, China: IEEE, 2014, pp. 3230–3237.10.1109/CEC.2014.6900255
  27. [27] Z. hui Zhan, J. Zhang, Y. hui Shi, and H. lin Liu, “A modified brain storm optimization,” in Evolutionary Computation (CEC), 2012 IEEE Congress on, June 2012, pp. 1–8.10.1109/CEC.2012.6256594
  28. [28] H. Jadhav, U. Sharma, J. Patel, and R. Roy, “Brain storm optimization algorithm based economic dispatch considering wind power,” in 2012 IEEE International Conference on Power and Energy (PECon 2012), Kota Kinabalu, Malaysia, December 2012, pp. 588–593.10.1109/PECon.2012.6450282
  29. [29] C. Sun, H. Duan, and Y. Shi, “Optimal satellite formation reconfiguration based on closed-loop brain storm optimization,” IEEE Computational Intelligence Magazine, vol. 8, no. 4, pp. 39–51, November 2013.10.1109/MCI.2013.2279560
  30. [30] H. Duan, S. Li, and Y. Shi, “Predatorcprey brain storm optimization for dc brushless motor,” IEEE Transactions on Magnetics, vol. 49, no. 10, pp. 5336–5340, October 2013.
  31. [31] H. Duan and C. Li, “Quantum-behaved brain storm optimization approch to solving loney's solenoid problem,” IEEE Transactions on Magnetics, p. in press, 2014.
  32. [32] Y. Tan and Y. Zhu, “Fireworks algorithm for optimization,” in Advances in Swarm Intelligence, ser. Lecture Notes in Computer Science, Y. Tan, Y. Shi, and K. C. Tan, Eds. Springer Berlin Heidelberg, 2010, vol. 6145, pp. 355–364.10.1007/978-3-642-13495-1_44
  33. [33] S. Zheng, A. Janecek, and Y. Tan, “Enhanced fireworks algorithm,” in 2013 IEEE Congress on Evolutionary Computation (CEC), June 2013, pp. 2069–2077.10.1109/CEC.2013.6557813
  34. [34] Y. Shi, J. Xue, and Y. Wu, “Multi-objective optimization based on brain storm optimization algorithm,” International Journal of Swarm Intelligence Research (IJSIR), vol. 4, no. 3, pp. 1–21, July-September 2013.10.4018/ijsir.2013070101
  35. [35] C. Darwin, On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life, 5th ed. London: John Murray, 1869.
  36. [36] M. Affenzeller, S. Winkler, S. Wagner, and A. Beham, Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications, ser. Numerical Insights, A. Sydow, Ed. Chapman & Hall/CRC Press, 2009, vol. 6.10.1201/9781420011326
  37. [37] S. Cheng, Y. Shi, and Q. Qin, “Dynamical exploitation space reduction in particle swarm optimization for solving large scale problems,” in Proceedings of 2012 IEEE Congress on Evolutionary Computation, (CEC 2012). Brisbane, Australia: IEEE, 2012, pp. 3030–3037.10.1109/CEC.2012.6252937
  38. [38] S. Cheng, Y. Shi, and Q. Qin, “Population diversity based study on search information propagation in particle swarm optimization,” in Proceedings of 2012 IEEE Congress on Evolutionary Computation, (CEC 2012). Brisbane, Australia: IEEE, 2012, pp. 1272–1279.10.1109/CEC.2012.6256502
  39. [39] K. P. Murphy, Machine Learning: A Probabilistic Perspective, ser. Adaptive computation and machine learning series. Cambridge, Massachusetts: The MIT Press, 2012.
  40. [40] D. Zhou, Y. Shi, and S. Cheng, “Brain storm optimization algorithm with modified step-size and individual generation,” in Advances in Swarm Intelligence, ser. Lecture Notes in Computer Science, Y. Tan, Y. Shi, and Z. Ji, Eds. Springer Berlin / Heidelberg, 2012, vol. 7331, pp. 243–252.10.1007/978-3-642-30976-2_29
  41. [41] S. Cheng, Y. Shi, and Q. Qin, “Population diversity of particle swarm optimizer solving single and multi-objective problems,” International Journal of Swarm Intelligence Research (IJSIR), vol. 3, no. 4, pp. 23–60, 2012.10.4018/jsir.2012100102
  42. [42] S. Cheng, Y. Shi, and Q. Qin, “Promoting diversity in particle swarm optimization to solve multimodal problems,” in Neural Information Processing, ser. Lecture Notes in Computer Science, B.-L. Lu, L. Zhang, and J. Kwok, Eds. Springer Berlin / Heidelberg, 2011, vol. 7063, pp. 228–237.10.1007/978-3-642-24958-7_27
  43. [43] W. Cedeño and V. R. Vemuri, “On the use of niching for dynamic landscapes,” in Proceedings of 1997 IEEE Congress on Evolutionary Computation, (CEC 1997). IEEE, 1997, pp. 361–366.
  44. [44] A. Della Cioppa, C. De Stefano, and A. Marcelli, “Where are the niches? dynamic fitness sharing,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 4, pp. 453–465, August 2007.10.1109/TEVC.2006.882433
  45. [45] A. Ghosh, S. Tsutsui, and H. Tanaka, “Function optimization in nonstationary environment using steady state genetic algorithms with aging of individuals,” in Proceedings of 1998 IEEE Congress on Evolutionary Computation, (CEC 1998). IEEE, 1998, pp. 666–671.
  46. [46] Y. Jin and B. Sendhoff, “Constructing dynamic optimization test problems using the multi-objective optimization concept,” in Applications of Evolutionary Computing, ser. Lecture Notes in Computer Science, G. R. Raidl, S. Cagnoni, J. Branke, D. W. Corne, R. Drechsler, Y. Jin, C. G. Johnson, P. Machado, E. Marchiori, F. Rothlauf, G. D. Smith, and G. Squillero, Eds. Springer Berlin / Heidelberg, 2004, vol. 3005, pp. 525–536.10.1007/978-3-540-24653-4_53
  47. [47] D. H.Wolpert andW. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67–82, April 1997.10.1109/4235.585893
  48. [48] X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 82–102, July 1999.10.1109/4235.771163
  49. [49] J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281–295, June 2006.10.1109/TEVC.2005.857610
  50. [50] T. Blackwell and P. Bentley, “Don't push me! collision-avoiding swarms,” in Proceedings of The Fourth Congress on Evolutionary Computation (CEC 2002), May 2002, pp. 1691–1696.
Language: English
Page range: 83 - 97
Published on: Mar 1, 2015
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2015 Shi Cheng, Yuhui Shi, Quande Qin, Qingyu Zhang, Ruibin Bai, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.