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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 762372, 13 pages
http://dx.doi.org/10.1155/2013/762372
Research Article

Bidirectional Dynamic Diversity Evolutionary Algorithm for Constrained Optimization

1Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
2Institute of Advanced Control Technology, Dalian University of Technology, Dalian, Liaoning 116024, China

Received 1 September 2013; Accepted 13 November 2013

Academic Editor: D. Baleanu

Copyright © 2013 Weishang Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. 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, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. A. E. Smith, “Swarm intelligence: from natural to artificial systems [book reviews],” IEEE Transactions on Evolutionary Computation, vol. 4, no. 2, pp. 192–193, 2000. View at Google Scholar
  3. J. Wang and G. Beni, “Swarm intelligence in cellular robotic systems,” in Proceedings of the NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 1989.
  4. H. Bai and B. Zhao, “A survey on application of swarm intelligence computation to electric power system,” in Proceedings of the 6th World Congress on Intelligent Control and Automation (WCICA '06), pp. 7587–7591, IEEE, Dalian, China, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. K. Kim, P. Rao, and J. Burnworth, “Application of swarm intelligence to a digital excitation control system,” in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '08), pp. 1–8, IEEE Conference Proceedings, St. Louis, Mo, USA, September 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. E. Chapman and F. Sahin, “Application of swarm intelligence to the mine detection problem,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC '04), vol. 6, pp. 5429–5434, IEEE Conference Proceedings, October 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. G. Wang, W. Y. Zhang, Q. Ning, and H. L. Chen, “A novel framework based on aco and pso for rna secondary structure prediction,” Mathematical Problems in Engineering, vol. 2013, Article ID 796304, 8 pages, 2013. View at Publisher · View at Google Scholar
  8. X. Fu, W. Liu, B. Zhang, and H. Deng, “Quantum behaved particle swarm optimization with neighborhood search for numerical optimization,” Mathematical Problems in Engineering, vol. 2013, Article ID 469723, 10 pages, 2013. View at Publisher · View at Google Scholar
  9. W. Gao, C. Shao, and Q. Gao, “Pseudo-collision in swarm optimization algorithm and solution: rain forest algorithm,” Acta Physica Sinica, vol. 62, no. 19, Article ID 190202, 2013. View at Google Scholar
  10. X. Jiang, H. Ling, J. Yan, B. Li, and Z. Li, “Forecasting electrical energy consumption of equipment maintenance using neural network and particle swarm optimization,” Mathematical Problems in Engineering, vol. 2013, Article ID 194730, 8 pages, 2013. View at Publisher · View at Google Scholar
  11. D. Sendrescu, “Parameter identification of anaerobic wastewater treatment bioprocesses using particle swarm optimization,” Mathematical Problems in Engineering, vol. 2013, Article ID 103748, 8 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  12. J. Chen, Y. Ding, and K. Hao, “The bidirectional optimization of carbon fiber production by neural network with a gaipso hybrid algorithm,” Mathematical Problems in Engineering, vol. 2013, Article ID 768756, 16 pages, 2013. View at Publisher · View at Google Scholar
  13. Y. Zhang, L. Wu, and S. Wang, “UCAV path planning by fitness-scaling adaptive chaotic particle swarm optimization,” Mathematical Problems in Engineering, vol. 2013, Article ID 705238, 9 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  14. C. Liu, O. Yacine, N. Antoine, B. Abdelaziz, and J. Zhou, “The reputation evaluation based on optimized hidden markov model in ecommerce,” Mathematical Problems in Engineering, vol. 2013, Article ID 391720, 11 pages, 2013. View at Publisher · View at Google Scholar
  15. X. Su, W. Fang, Q. Shen, and X. Hao, “An image enhancement method using the quantum-behaved particle swarm optimization with an adaptive strategy,” Mathematical Problems in Engineering, vol. 2013, Article ID 824787, 14 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  16. P. Liu, W. Leng, and W. Fang, “Training anfis model with an improved quantum-behaved particle swarm optimization algorithm,” Mathematical Problems in Engineering, vol. 2013, Article ID 595639, 10 pages, 2013. View at Publisher · View at Google Scholar
  17. J.-Y. Wu, “Solving unconstrained global optimization problems via hybrid swarm intelligence approaches,” Mathematical Problems in Engineering, vol. 2013, Article ID 256180, 15 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  18. K. Deb, “An efficient constraint handling method for genetic algorithms,” Computer Methods in Applied Mechanics and Engineering, vol. 186, no. 2–4, pp. 311–338, 2000. View at Google Scholar · View at Scopus
  19. R. Farmani and J. A. Wright, “Self-adaptive fitness formulation for constrained optimization,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 5, pp. 445–455, 2003. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Nema, J. Y. Goulermas, G. Sparrow, and P. Helman, “A hybrid cooperative search algorithm for constrained optimization,” Structural and Multidisciplinary Optimization, vol. 43, no. 1, pp. 107–119, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  21. S. He, E. Prempain, and Q. H. Wu, “An improved particle swarm optimizer for mechanical design optimization problems,” Engineering Optimization, vol. 36, no. 5, pp. 585–605, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  22. B. Yang, Y. Chen, Z. Zhao, and Q. Han, “A master-slave particle swarm optimization algorithm for solving constrained optimization problems,” in Proceedings of the 6th World Congress on Intelligent Control and Automation (WCICA '06), vol. 1, pp. 3208–3212, Dalian, China, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. X. Huang and T. Chai, “Particle swarm optimization for raw material purchasing plan in large scale ore dressing plant,” Acta Automatica Sinica, vol. 35, no. 5, pp. 632–636, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. C. Wang, M. Wu, W. Cao, and Y. He, “Intelligent integrated modeling and synthetic optimization for blending process in Lead-Zinc sintering,” Acta Automatica Sinica, vol. 35, no. 5, pp. 605–612, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. G. Liu, S. Lao, C. Yuan, L. Hou, and D. Tan, “Oacrrpso algorithm for anti-ship missile path planning,” Acta Automatica Sinica, vol. 38, no. 9, pp. 1528–1537, 2012. View at Google Scholar
  26. R. A. Vural, T. Yildirim, T. Kadioglu, and A. Basargan, “Performance evaluation of evolutionary algorithms for optimal filter design,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 1, pp. 135–147, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. X. Li and X. Yao, “Cooperatively coevolving particle swarms for large scale optimization,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 2, pp. 210–224, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. T. Blackwell, “A study of collapse in bare bones particle swarm optimization,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 3, pp. 354–372, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. Y. Pehlivanoglu, “A new particle swarm optimization method enhanced with a periodic mutation strategy and neural networks,” IEEE Transactions on Evolutionary Computation, vol. 17, no. 3, pp. 436–452, 2012. View at Google Scholar
  30. W. N. Chen, J. Zhang, Y. Lin et al., “Particle swarm optimization with an aging leader and challengers,” IEEE Transactions on Evolutionary Computation, vol. 17, no. 2, pp. 241–258, 2012. View at Google Scholar
  31. F. Naznin, R. Sarker, and D. Essam, “Progressive alignment method using genetic algorithm for multiple sequence alignment,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 5, pp. 615–631, 2012. View at Google Scholar
  32. Y. Wang and Z. Cai, “Combining multiobjective optimization with differential evolution to solve constrained optimization problems,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 1, pp. 117–134, 2012. View at Publisher · View at Google Scholar · View at Scopus
  33. Y. Wang, Z. Cai, Y. Zhou, and W. Zeng, “An adaptive tradeoff model for constrained evolutionary optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 1, pp. 80–92, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. Z. Cai and Y. Wang, “A multiobjective optimization-based evolutionary algorithm for constrained optimization,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 6, pp. 658–675, 2006. View at Publisher · View at Google Scholar · View at Scopus
  35. R. A. Krohling and L. dos Santos Coelho, “Coevolutionary particle swarm optimization using gaussian distribution for solving constrained optimization problems,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 36, no. 6, pp. 1407–1416, 2006. View at Publisher · View at Google Scholar · View at Scopus
  36. B. Tessema and G. G. Yen, “An adaptive penalty formulation for constrained evolutionary optimization,” IEEE Transactions on Systems, Man, and Cybernetics A, vol. 39, no. 3, pp. 565–578, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. M. Daneshyari and G. G. Yen, “Constrained multiple-swarm particle swarm optimization within a cultural framework,” IEEE Transactions on Systems, Man, and Cybernetics A, vol. 42, no. 2, pp. 475–490, 2012. View at Publisher · View at Google Scholar · View at Scopus
  38. S. Venkatraman and G. G. Yen, “A generic framework for constrained optimization using genetic algorithms,” IEEE Transactions on Evolutionary Computation, vol. 9, no. 4, pp. 424–435, 2005. View at Publisher · View at Google Scholar · View at Scopus
  39. Y. Wang, Y. Jiao, and H. Li, “An evolutionary algorithm for solving nonlinear bilevel programming based on a new constraint-handling scheme,” IEEE Transactions on Systems, Man and Cybernetics C, vol. 35, no. 2, pp. 221–232, 2005. View at Publisher · View at Google Scholar · View at Scopus
  40. J. J. Grefenstette, “Optimization of control parameters for genetic algorithms,” IEEE Transactions on Systems, Man and Cybernetics, vol. 16, no. 1, pp. 122–128, 1986. View at Google Scholar · View at Scopus
  41. D. Whitley, T. Starkweather, and C. Bogart, “Genetic algorithms and neural networks: optimizing connections and connectivity,” Parallel Computing, vol. 14, no. 3, pp. 347–361, 1990. View at Google Scholar · View at Scopus
  42. D. S. Weile and E. Michielssen, “Genetic algorithm optimization applied to electromagnetics: a review,” IEEE Transactions on Antennas and Propagation, vol. 45, no. 3, pp. 343–353, 1997. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, Perth, Wash, USA, December 1995. View at Scopus
  44. R. Eberhart and J. Kennedy, “New optimizer using particle swarm theory,” in Proceedings of the 1995 6th International Symposium on Micro Machine and Human Science, pp. 39–43, Nagoya, Japan, October 1995. View at Scopus
  45. M. Settles and T. Soule, “Breeding swarms: a GA/PSO hybrid,” in Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO ’05), pp. 161–168, ACM, Washington, DC, USA, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  46. J. Robinson, S. Sinton, and Y. Rahmat-Samii, “Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna,” in Proceedings of the 2002 IEEE Antennas and Propagation Society International Symposium, vol. 1, pp. 314–317, IEEE, June 2002. View at Scopus
  47. Y. Rahmat-Samii, “Genetic algorithm (ga) and particle swarm optimization (pso) in engineering electromagnetics,” in Proceedings of the 17th International Conference on Applied Electromagnetics and Communications (ICECom '03), pp. 1–5, 2003.
  48. J. Ronkkonen, X. Li, V. Kyrki, and J. Lampinen, “A framework for generating tunable test functions for multimodal optimization,” in Soft Computing—A Fusion of Foundations, Methodologies and Applications, pp. 1689–1706, Springer, 2010. View at Google Scholar
  49. M. Meissner, M. Schmuker, and G. Schneider, “Optimized particle swarm optimization (OPSO) and its application to artificial neural network training,” BMC Bioinformatics, vol. 7, article 125, 2006. View at Publisher · View at Google Scholar · View at Scopus
  50. C. MacNish, “Towards unbiased benchmarking of evolutionary and hybrid algorithms for real-valued optimisation,” Connection Science, vol. 19, no. 4, pp. 361–385, 2007. View at Publisher · View at Google Scholar · View at Scopus
  51. R. Salomon, “Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms,” BioSystems, vol. 39, no. 3, pp. 263–278, 1996. View at Publisher · View at Google Scholar · View at Scopus
  52. J. G. Digalakis and K. G. Margaritis, “An experimental study of benchmarking functions for genetic algorithms,” International Journal of Computer Mathematics, vol. 79, no. 4, pp. 403–416, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  53. M. A. Potter, The design and analysis of a computational model of cooperative coevolution [Ph.D. thesis], George Mason University, 1997.
  54. W. Gao, “Study on immunized ant colony optimization,” in Proceedings of the 3rd International Conference on Natural Computation (ICNC '07), vol. 3, pp. 792–796, Haikou, China, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  55. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer, Berlin, Germany, 1996. View at MathSciNet
  56. Y. Qing and D. Sheng-Chao, “Amplitude-based coding hybrid quantuminspired evolutionary algorithm,” Computer Engineering and Applications, vol. 43, no. 21, pp. 80–83, 2007. View at Google Scholar
  57. J. Zhao and C. Yan, “A bottleneck assigned binary ant system for multimodal optimization,” in Proceedings of the 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, pp. 6195–6200, Shanghai, China, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  58. D. Cvijović and J. Klinowski, “Taboo search: an approach to the multiple minima problem,” Science, vol. 267, no. 5198, pp. 664–666, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  59. W. Chen, J. Zhang, H. S. H. Chung, W. Zhong, W. Wu, and Y. Shi, “A novel set-based particle swarm optimization method for discrete optimization problems,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 2, pp. 278–300, 2010. View at Publisher · View at Google Scholar · View at Scopus
  60. S. Yang and C. Li, “A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 6, pp. 959–974, 2010. View at Publisher · View at Google Scholar · View at Scopus
  61. M. Farina, K. Deb, and P. Amato, “Dynamic multiobjective optimization problems: test cases, approximations, and applications,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 5, pp. 425–442, 2004. View at Publisher · View at Google Scholar · View at Scopus
  62. P. A. Bosman, “On gradients and hybrid evolutionary algorithms for real-valued multiobjective optimization,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 1, pp. 51–69, 2012. View at Publisher · View at Google Scholar · View at Scopus