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Mathematical Problems in Engineering
Volume 2015, Article ID 316826, 12 pages
http://dx.doi.org/10.1155/2015/316826
Research Article

An Optimization Algorithm with Novel RFA-PSO Cooperative Evolution: Applications to Parameter Decision of a Snake Robot

1School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
2State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110000, China

Received 14 July 2014; Revised 30 October 2014; Accepted 13 November 2014

Academic Editor: Victor Santibáñez

Copyright © 2015 Qin 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. D. C. Walters and G. B. Sheble, “Genetic algorithm solution of economic dispatch with value point loading,” IEEE Transactions on Power Systems, vol. 8, no. 3, pp. 1325–1332, 1993. View at Publisher · View at Google Scholar · View at Scopus
  2. M. H. Moradi and M. Abedini, “A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems,” International Journal of Electrical Power and Energy Systems, vol. 34, no. 1, pp. 66–74, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. H.-I. Liny and Y.-C. Liu, “Minimum-jerk robot joint trajectory using particle swarm optimization,” in Proceedings of the 1st International Conference on Robot, Vision and Signal Processing (RVSP '11), pp. 118–121, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. G. R. Harik, F. G. Lobo, and D. E. Goldberg, “The compact genetic algorithm,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp. 287–297, 1999. View at Publisher · View at Google Scholar · View at Scopus
  5. F. van den Bergh and A. P. Engelbrecht, “A cooperative approach to participle swam optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 225–239, 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. R. Eberhart and J. Kennedy, “New optimizer using particle swarm theory,” in Proceedings of the 6th International Symposium on Micro Machine and Human Science, pp. 39–43, October 1995. View at Scopus
  7. C. A. Coello Coello, G. T. Pulido, and M. S. Lechuga, “Handling multiple objectives with particle swarm optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 256–279, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. J.-J. Kim and J.-J. Lee, “Adaptation of quadruped gaits using surface classification and gait optimization,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '13), pp. 716–721, Tokyo, Japan, November 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. Y.-L. Li, W. Shao, L. You, and B.-Z. Wang, “An improved PSO algorithm and its application to UWB antenna design,” IEEE Antennas and Wireless Propagation Letters, vol. 12, pp. 1236–1239, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. C.-M. Lee and C.-N. Ko, “Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm,” Neurocomputing, vol. 73, no. 1–3, pp. 449–460, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. T.-H. Yi, H.-N. Li, and M. Gu, “Optimal sensor placement for health monitoring of high-rise structure based on genetic algorithm,” Mathematical Problems in Engineering, vol. 2011, Article ID 395101, 11 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. O. Abu-Arqub, Z. Abo-Hammour, and S. Momani, “Application of continuous genetic algorithm for nonlinear system of second-order boundary value problems,” Applied Mathematics & Information Sciences, vol. 8, no. 1, pp. 235–248, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. F. Valdez and P. Melin, “Parallel evolutionary computing using a cluster for mathematical function optimization,” in Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS '07), pp. 598–603, San Diego, Calif, USA, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. Y.-T. Kao and E. Zahara, “A hybrid genetic algorithm and particle swarm optimization for multimodal functions,” Applied Soft Computing Journal, vol. 8, no. 2, pp. 849–857, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. F. Valdez, P. Melin, and O. Castillo, “An improved evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 2625–2632, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. W.-S. 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 Publisher · View at Google Scholar · View at Scopus
  17. J. Yu, M. Tan, J. Chen, and J. Zhang, “A survey on CPG-inspired control models and system implementation,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 3, pp. 441–456, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. Wang and H. Gu, “A bristle-based pipeline robot for Ill-constraint pipes,” IEEE/ASME Transactions on Mechatronics, vol. 13, no. 3, pp. 383–392, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. Z. Wang and H. Gu, “A review of locomotion mechanisms of urban search and rescue robot,” Industrial Robot, vol. 34, no. 5, pp. 400–411, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. H. Zhao, Z. Wang, H. Shang, W. Hu, and G. Qin, “A time-controllable Allan variance method for MEMS IMU,” Industrial Robot, vol. 40, no. 2, pp. 111–120, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. Z. Wang, J. He, H. Shang, and H. Gu, “Forward kinematics analysis of a six-DOF Stewart platform using PCA and NM algorithm,” Industrial Robot, vol. 36, no. 5, pp. 448–460, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. Z. Wang, J. He, and H. Gu, “Forward kinematics analysis of a six-degree-of-freedom stewart platform based on independent component analysis and Nelder-Mead algorithm,” IEEE Transactions on Systems, Man, and Cybernetics A:Systems and Humans, vol. 41, no. 3, pp. 589–597, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Oliveira, V. Matos, C. P. Santos, and L. Costa, “Multi-objective parameter CPG optimization for gait generation of a biped robot,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '13), pp. 3130–3135, Karlsruhe, Germany, May 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. M.-A. Sato, Y. Nakamura, and S. Ishii, “Reinforcement learning for biped locomotion,” in Artificial Neural Networks—ICANN 2002, vol. 2415, pp. 777–782, Springer, 2002. View at Publisher · View at Google Scholar
  25. C. Wang, G. Xie, L. Wang, and M. Cao, “CPG-based locomotion control of a robotic fish: using linear oscillators and reducing control parameters via PSO,” International Journal of Innovative Computing, Information and Control, vol. 7, no. 7, pp. 4237–4249, 2011. View at Google Scholar · View at Scopus
  26. H. Kalani, A. Akbarzadeh, and H. Bahrami, “Application of statistical techniques in modeling and optimization of a snake robot,” Robotica, vol. 31, no. 4, pp. 623–641, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. K. Inoue, S. Ma, and C. Jin, “Optimization of CPG-network for decentralized control of a snake-like robot,” in Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO '05), pp. 730–735, Hongkong, China, July 2005. View at Scopus
  28. Z. Wang, M. Jiang, Y. Hu, and H. Li, “An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 4, pp. 691–699, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Jiang, H. Shang, Z. Wang, H. Li, and Y. Wang, “A method to deal with installation errors of wearable accelerometers for human activity recognition,” Physiological Measurement, vol. 32, no. 3, pp. 347–358, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Kamimura, H. Kurokawa, E. Yoshida, S. Murata, K. Tomita, and S. Kokaji, “Automatic locomotion design and experiments for a modular robotic system,” IEEE/ASME Transactions on Mechatronics, vol. 10, no. 3, pp. 314–325, 2005. View at Publisher · View at Google Scholar · View at Scopus
  31. C. Liu, Q. Chen, and D. Wang, “CPG-inspired workspace trajectory generation and adaptive locomotion control for Quadruped Robots,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 41, no. 3, pp. 867–880, 2011. View at Publisher · View at Google Scholar · View at Scopus
  32. R. Ding, J. Yu, Q. Yang, M. Tan, and J. Zhang, “CPG-based dynamics modeling and simulation for a biomimetic amphibious robot,” in Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO '09), pp. 1657–1662, Guilin, China, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. A. Crespi and A. J. Ijspeert, “Amphibot II: an amphibious snake robot that crawls and swims using a central pattern generator,” in Proceedings of the 9th International Conference on Climbing and Walking Robots, pp. 19–27, Brussels, Belgium, September 2006.
  34. K. Seo and J.-J. E. Slotine, “Models for global synchronization in CPG-based locomotion,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '07), pp. 281–286, Roma, Italy, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  35. C. Zhou and K. H. Low, “Design and locomotion control of a biomimetic underwater vehicle with fin propulsion,” IEEE/ASME Transactions on Mechatronics, vol. 17, no. 1, pp. 25–35, 2012. View at Publisher · View at Google Scholar · View at Scopus
  36. X.-M. Hu, J. Zhang, Y. Yu et al., “Hybrid genetic algorithm using a forward encoding scheme for lifetime maximization of wireless sensor networks,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 5, pp. 766–781, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. M. A. M. de Oca, T. Stützle, M. Birattari, and M. Dorigo, “Frankenstein's PSO: a composite particle swarm optimization algorithm,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 1120–1132, 2009. View at Publisher · View at Google Scholar · View at Scopus
  38. P. Melin, F. Olivas, O. Castillo, F. Valdez, J. Soria, and M. Valdez, “Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic,” Expert Systems with Applications, vol. 40, no. 8, pp. 3196–3206, 2013. View at Publisher · View at Google Scholar · View at Scopus
  39. O. Castillo, R. Martínez-Marroquín, P. Melin, F. Valdez, and J. Soria, “Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot,” Information Sciences, vol. 192, pp. 19–38, 2012. View at Publisher · View at Google Scholar · View at Scopus
  40. Z. C. Yan and Y. S. Luo, “A particle swarm optimization algorithm based on simulated annealing,” in Advanced Materials Research, vol. 989, pp. 2301–2305, 2014. View at Google Scholar