Karl O. Jones
ABSTRACT
Evolutionary Computation (EC) is a recent and lively area of study. Some of the recent approaches within EC are particle Swarm Optimisation (PSO) and Differential Evolution (DE), while one of the latest to be developed is Firefly Algorithm (FA): all of which can be used in optimisation problems. This paper makes a comparison of the effectiveness of these three methods on a specific optimisation problem, specifically tuning the parameters of a PID controller.
- Herrero, J. M., et al. 2002. Optimal PID tuning with Genetic Algorithms for Non-Linear process Models. IFAC 15th Triennial World Congress, Barcelona, Spain.Google Scholar
Cross Ref
- Jones, K. O. and Bouffet, A. 2008. Comparison of Bees Algorithm, Ant Colony Optimisation, and particle Swarm Optimisation for PID Controller Tuning. Proceedings of the 9th International Conference on Computer Systems and Technologies. pp. IIIA.9-1(6). 2008. ISBN 978-954-9641-52-3 Google ScholarDigital Library
- Pham D. T., Ghanbarzadeh A., Koç E., Otri S., Rahim S., and Zaidi M. The Bees Algorithm, A Novel Tool for Complex Optimisation Problems. Proc 2nd Virtual International Conference on Intelligent Production Machines and Systems. 2006, Elsevier (Oxford), pp.454--459.Google ScholarCross Ref
- Kennedy, J. and R. C. Eberhart. 2001. Swarm Intelligence. Morgan Kaufman Publishers. Google ScholarDigital Library
- Kennedy, J. and R. C. Eberhart. 1995. Particle Swarm Optimisation. Proceedings IEEE International Conference on Neural Networks, IV, pp. 1942--1948.Google Scholar
- Storn, R. and K. Price. 1995. Differential Evolution -- A Simple and Efficient Adaptive Scheme for Global Optimisation over Continuous Spaces. Technical Report TR-95-012, ICSI. Available via ftp://ftp.icsi.berkeley.edu/pub/techreports/1995/tr-95012.ps.zGoogle Scholar
- Yang, X. S. 2008. Nature-Inspired Metaheuristic Algorithms, Luniver Press, UK. Google ScholarDigital Library
- Ziegler, J. G. and Nichols, N. B. 1942. Optimum settlings for automatic controllers. ASME Transactions, (Vol. 64), pp. 759--768.Google Scholar
- Leidenfrost, R. and Elmenreich, W. 2008. Establishing wireless time-triggered communication using firefly clock synchronization approach. Proceedings of the 2008 International Workshop on Intelligent Solutions in Embedded Systems. pp. 1--8.Google Scholar
- Krishnanand, K. and Ghose, D. 2006. Glowworm swarm based optimization algorithm fr multimodal functions with collective robotics applications. Multiagent and Grid Systems, 2(3), pp. 209--222.Google ScholarDigital Library
- Jumadinova, J. and Dasgupta, P. 2008. Firefly-inspired synchronization for improved dynamic pricing in online markets. Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems. pp. 402--412. Google ScholarDigital Library
Index Terms
- Comparison of Firefly algorithm optimisation, particle swarm optimisation and differential evolution
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