1. bookVolume 4 (2014): Edizione 2 (April 2014)
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Population Diversity Maintenance In Brain Storm Optimization Algorithm

Pubblicato online: 01 Mar 2015
Volume & Edizione: Volume 4 (2014) - Edizione 2 (April 2014)
Pagine: 83 - 97
Dettagli della rivista
License
Formato
Rivista
eISSN
2449-6499
Prima pubblicazione
30 Dec 2014
Frequenza di pubblicazione
4 volte all'anno
Lingue
Inglese

[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.Search in Google Scholar

[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.Search in Google Scholar

[3] D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc., 1989.Search in Google Scholar

[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 1999.10.1109/4235.771166Search in Google Scholar

[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.2008.2011743Search in Google Scholar

[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.2009.933094Search in Google Scholar

[7] R. K. Sundaram, A First Course in Optimization Theory. Cambridge University Press, 1996.10.1017/CBO9780511804526Search in Google Scholar

[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.2007.910138Search in Google Scholar

[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.2010.2058117Search in Google Scholar

[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.2009.2022002Search in Google Scholar

[11] J. Kennedy, R. Eberhart, and Y. Shi, Swarm Intelligence. Morgan Kaufmann Publisher, 2001.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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/s11704-009-0008-4Search in Google Scholar

[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.2011.5952581Search in Google Scholar

[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.2011070104Search in Google Scholar

[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.Search in Google Scholar

[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.2013010101Search in Google Scholar

[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_36Search in Google Scholar

[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.2011100103Search in Google Scholar

[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_40Search in Google Scholar

[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_62Search in Google Scholar

[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_39Search in Google Scholar

[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.2013.6615166Search in Google Scholar

[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.2013.6615167Search in Google Scholar

[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.2014.6900255Search in Google Scholar

[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.2012.6256594Search in Google Scholar

[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.2012.6450282Search in Google Scholar

[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.2013.2279560Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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_44Search in Google Scholar

[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.2013.6557813Search in Google Scholar

[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.2013070101Search in Google Scholar

[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.Search in Google Scholar

[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. 6.10.1201/9781420011326Search in Google Scholar

[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.2012.6252937Search in Google Scholar

[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.2012.6256502Search in Google Scholar

[39] K. P. Murphy, Machine Learning: A Probabilistic Perspective, ser. Adaptive computation and machine learning series. Cambridge, Massachusetts: The MIT Press, 2012.Search in Google Scholar

[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_29Search in Google Scholar

[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.2012100102Search in Google Scholar

[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_27Search in Google Scholar

[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.Search in Google Scholar

[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.2006.882433Search in Google Scholar

[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.Search in Google Scholar

[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_53Search in Google Scholar

[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 1997.10.1109/4235.585893Search in Google Scholar

[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 1999.10.1109/4235.771163Search in Google Scholar

[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.2005.857610Search in Google Scholar

[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.Search in Google Scholar

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