Hardware opposition-based PSO applied to mobile robot controllers

Engineering Applications of Artificial Intelligence

Volumn 28, Issue , 2014, Pages 64-77

  Muñoz, Daniel M ^a   Llanos, Carlos H ^a   Coelho, Leandro Dos S ^b,c   Ayala Rincón, Mauricio ^a  

^a UNIVERSITY OF BRASILIA   (Brazil)

^b PONTIFICAL CATHOLIC UNIVERSITY OF PARANÁ   (Brazil)

^c FEDERAL UNIVERSITY OF PARANÁ   (Brazil)

Author keywords

Evolvable hardware; Fault tolerance; FPGAs; Learning from demonstration; Mobile robots; Swarm intelligence

Indexed keywords

COMPLEX OPTIMIZATION PROBLEMS; COMPUTATIONAL LIMITATIONS; EMBEDDED SYSTEM APPLICATIONS; EVOLVABLE HARDWARE; LEARNING FROM DEMONSTRATION; OPPOSITION-BASED LEARNING; PARTICLE SWARM OPTIMIZATION ALGORITHM; SWARM INTELLIGENCE;

ALGORITHMS; APPLICATION PROGRAMS; COMPUTATIONAL METHODS; CONTROLLERS; EMBEDDED SYSTEMS; FAULT TOLERANCE; FIELD PROGRAMMABLE GATE ARRAYS (FPGA); MOBILE ROBOTS; NEURAL NETWORKS; PARTICLE SWARM OPTIMIZATION (PSO);

HARDWARE;

EID: 84892858663     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2013.12.003     Document Type: Article

References (48)

1. Al-Qunaieer, H.R.S., Tizhoosh, F.S., 2010. Opposition based computing a survey. In: Proceedings of the International Joint Conference on Neural Networks. Barcelona, Spain, pp. 1098-7576.
2. B. Argall, S. Chernova, M. Veloso, and B. Browning A survey of robot learning from demonstration Robot. Auton. Syst. 47 5 2009 469 483
3. A. Banks, J. Vincent, and C. Anyakoha A review of particle swarm optimization. Part I. Background and development Int. J. Nat. Comput. 6 4 2007 467 484
4. A. Billard, S. Callinon, R. Dillmann, and S. Schaal Robot programming by demonstration B. Siciliano, O. Khatib, Handbook of Robotics 2008 Springer New York, USA (Chapter 59)
5. Boyd, S. Real time embedded convex optimization. In: International Symposium on Mathematical Programming, online (August 2009). URL http://ismp2009.eecs.northwestern.edu/Plenaries/ .
6. T. Bräuln Embedded Robotics 2006 Springer-Verlag Germany
7. M. Cavuslu, C. Karakuzu, and F. Karakaya Neural identification of dynamic systems on FPGA with improved PSO learning Appl. Soft Comput. 12 9 2012 2707 2718
8. A. Chatterjee, K. Pulasinghe, K. Watanabe, and K. Izumi A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems IEEE Trans. Ind. Electron. 52 6 2005 1478 1489
9. S. Chowdhury, D. Chakrabarti, and H. Saha Medical diagnosis using adaptive perceptive particle swarm optimization and its hardware realization using field programmable gate array J. Med. Syst. 33 6 2009 447 465
10. R. Duren, R. Marks, P. Reynolds, and M. Trumbo Real-time neural network inversion on the SRC-6e reconfigurable computer IEEE Trans. Neural Netw. 18 3 2007 889 900
11. Eberhart, R., Kennedy, J., 1995. A new optimizer using particle swarm theory. In: Proceedings of the International Symposium on Micro Machine and Human Science. IEEE, Nagoya, Japan, pp. 39-43.
12. A. Farmahini-Farahani, S. Vakili, S. Fakhraie, S. Safari, and C. Lucas Parallel scalable hardware implementation of asynchronous discrete particle swarm optimization Eng. Appl. Artif. Intell. 23 2 2010 177 187
13. L. Gupta, and R. Mehra Modified PSO based adaptive IIR filter design for system identification on FPGAs Int. J. Comput. Appl. 22 5 2011 1 7
14. S. Hauck, and A. Dehon Reconfigurable Computing. The Theory and Practice of FPGA-based Computing 2008 Elsevier Inc. Burlington, MA, United States
15. Jabeen, H., Jalil, Z., Baig, A., 2009. Opposition based initialization in particle swarm optimization (O-PSO). In: Proceedings of the ACM Conference on Genetic and Evolutionary Computation, Montreal, Canada, pp. 2047-2052.
16. N. Jin, and Y. Rahmat-Samii Parallel particle swarm optimization and finite-difference time-domain (PSO/FDTD) algorithm for multiband and wide-band patch antenna designs Int. J. Numer. Methods Eng. 53 11 2005 3459 3468
17. M. Kaucic A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization J. Glob. Optim. 55 1 2013 165 188
18. Kennedy, J., Eberhart, R., 1995. Particle swarm optimization. In: Proceedings of the International Conference on Neural Networks, Perth, Australia, pp. 1942-1948.
19. S. Kilts Advanced FPGA Design Architecture, Implementation and Optimization 2007 John Wiley & Sons NJ, United States
20. M. Knudson, and K. Tumer Adaptive navigation for autonomous robots Robot. Auton. Syst. 59 6 2011 410 420
21. B. Koh, A. George, R. Haftka, and B. Fregly Parallel asynchronous particle swarm optimization Int. J. Numer. Methods Eng. 67 4 2006 578 595
22. C. Lin, and H. Tsai FPGA implementation of a wavelet neural network with particle swarm optimization learning Trans. Math. Comput. Model. 47 9 2007 982 996
23. Lin, H., Xingshi, H., 2007. A novel opposition-based particle swarm optimization for noisy problems. In: Third International Conference on Natural Computation (ICNC 2007), vol. 3, pp. 624-629.
24. Mehmood, S., Cagnoni, S., Mordonini, M., Matrela, G., 2008. Hardware-oriented adaptation of a particle swarm optimization algorithm for object detection. In: Proceedings of the IEEE Euromicro International Conference on Digital System Design, Parma, Italy, pp. 904-911.
25. Muñoz, D.M., Llanos, C., Coelho, L.S., Ayala-Rincón, M., 2009. Hardware architecture for parallel particle swarm optimization using floating-point arithmetic. In: Proceedings of the International Conference on Intelligent Systems Design and Applications. IEEE, Pisa, Italy, pp. 243-248.
26. Muñoz, D.M., Llanos, C., Coelho, L.S., Ayala-Rincón, M., 2010a. Comparison between two FPGA implementations of the particle swarm optimization algorithm for high performance embedded applications. In: Proceedings of the International Conference on Bio-Inspired Computing, Theories and Applications, Liverpool, UK, pp. 1637-1645.
27. Muñoz, D.M., Llanos, C., Coelho, L.S., Ayala-Rincón, M., 2010b. Hardware particle swarm optimization based on the attractive-repulsive scheme for embedded applications. In: Proceedings of the International Conference on Reconfigurable Computing and FPGAs. IEEE, Cancún, México, pp. 55-60.
28. Muñoz, D.M., Sánchez, D., Llanos, C., Ayala-Rincón, M., 2010c. FPGA-based floating-point library for CORDIC algorithms. In: Proceedings of the International Southern Programmable Logic Conference, Porto de Galinhas, Brazil, pp. 55-60.
29. D.M. Muñoz, D. Sánchez, C. Llanos, and M. Ayala-Rincón Tradeoff of FPGA design of a floating-point library for arithmetic operators J. Integr. Circuits Syst. 5 1 2010 42 52
30. Muñoz, D.M., Llanos, C., Coelho, L.S., Ayala-Rincón, M., 2011. Hardware particle swarm optimization with passive congregation for embedded applications. In: Proceedings of the International Southern Programmable Logic Conference, Córdoba, Argentina, pp. 173-178.
31. Pant, M., Radha, R., Singh, V., 2007. A simple diversity guides particle swarm optimization. In: Proceedings of the IEEE Conference on Evolutionary Computation, Singapore, pp. 3294-3299.
32. Pena, J., Upegui, A., 2007. A population-oriented architecture for particle swarms. In: Proceedings of the International Conference Adaptive Hardware and System. NASA/ESA, Edinburgh, Scotland, pp. 563-571.
33. R. Poli, J. Kennedy, and T. Blackwell Particle swarm optimization Swarm Intell. 1 1 2007 33 57
34. J. Pugh, and A. Martinoli Distributed scalable multi-robot learning using particle swarm optimization Swarm Intell. 3 3 2009 203 222
35. S. Rahnamayan, H. Tizhoosh, and M. Salama Opposition versus randomness in soft computing techniques Appl. Soft Comput. 8 2 2008 906 918
36. S. Rao Engineering Optimization, Theory and Practice 1996 John Wiley & Sons USA
37. Reynolds, P., Duren, R., Trumbo, M., Marks, R., 2005. FPGA implementation of particle swarm optimization for inversion of large neural networks. In: Proceedings of the Swarm Intelligence Symposium. IEEE, Pasadena, CA, USA, pp. 389-392.
38. Riget, J., Vesterstrom, J., 2002. A Diversity-Guided Particle Swarm Optimizer - The ARPSO (Technical Report). EVALife, Aarhus, Denmark.
39. R. Sass, and A. Schmidt Embedded Systems Design with Platform FPGAs Principles and Practices 2010 Morgan Kaufmann
40. J. Schutte, J. Reinbolt, B. Fregly, R. Haftka, and A. George Parallel global optimization with the particle swarm algorithm Int. J. Numer. Methods Eng. 6 13 2004 2296 2315
41. Tewolde, G., Hanna, D., Haskell, R., 2009. Accelerating the performance of particle swarm optimization for embedded applications. In: Proceedings of the International Congress on Evolutionary Computation. IEEE, Trondheim, Norway, pp. 2294-2300.
42. G. Tewolde, D. Hanna, and R. Haskell A modular and efficient hardware architecture for particle swarm optimization algorithm Microprocess. Microsyst. 36 4 2012 289 302
43. Thomas, D., Luk, W., 2008. Resource efficient generators for the floating-point uniform and exponential distributions. In: Proceedings of the International Conference on Application-specific Systems, Architecture and Processors. IEEE, Leuven, Belgium, pp. 102-107.
44. Tizhoosh, H., 2005. Opposition-based learning a new scheme for machine intelligence. In: Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation, Vienna, Austria, pp. 695-701.
45. van den Bergh, F., 2002. An Analysis of Particle Swarm Optimizers (Ph.D. Thesis). Department of Computer Science, University of Pretoria, South Africa.
46. Wang, H., Li, H., Liu, Y., Li, C., Zeng, S., 2007. Opposition-based particle swarm algorithm with cauchy mutation. In: Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, pp. 4750-4756.
47. H. Wang, Z. Wu, S. Rahnamayan, Y. Liu, and M. Ventresca Enhancing particle swarm optimization using generalized opposition-based learning Inf. Sci. 181 20 2011 4699 4714
48. Zhang, C., Z.N, Wu, Z., Gu, L., 2009. A novel swarm model with quasi-oppositional particle. In: Proceedings of the IEEE Forum on Information Technology and Applications, Chengdu, China, pp. 325-330.