Chaotic Quantum-behaved Particle Swarm Optimization Approach Applied to Inverse Heat Transfer Problem
Leandro dos Santos Coelho, Fabio A. Guerra, Bruno Pasquim, Viviana Cocco Mariani
2013
Abstract
Particle swarm optimization (PSO) algorithms are attracting attentions in recent years, due to their ability of keeping good balance between convergence and diversity maintenance. Several attempts have been made to improve the performance of the original PSO algorithm. Inspired by trajectory analysis of the PSO and quantum mechanics, a quantum-behaved particle swarm optimization (QPSO) algorithm was recently proposed. QPSO has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. In this paper, a modified and efficient version of the QPSO combined with chaotic sequences (CQPSO) is proposed and evaluated. We conduct simulations to estimate the unknown variables of an inverse heat transfer problem to verify the performance of the proposed CQPSO method and show that the method can be competitive when compared with the classical QPSO.
References
- Acharjee, P. and Goswami, S. K. (2010). Chaotic particle swarm optimization based robust load flow. International Journal of Electrical Power & Energy Systems 32(2): 141-146.
- Araujo, E. and Coelho, L. S. (2008). Particle swarm approaches using Lozi map chaotic sequences to fuzzy modelling of an experimental thermal-vacuum system. Applied Soft Computing 8(4): 1354-1364.
- Cao, Y. and Kirik, S. (2000). The basin of the strange attractors of some Hénon maps. Chaos, Solitons & Fractals 11(5): 729-734.
- Chuang, L. -Y., Tsai, S. -W., and Yang, C. -H. (2011). Chaotic catfish particle swarm optimization for solving global numerical optimization problems. Applied Mathematics and Computation 217(16): 6900- 6916.
- Clerc, M. and Kennedy, J. F. (2002). The particle swarm: explosion, stability and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1): 58-73.
- Coelho, L. S. and Lee, C. -S. (2008). Solving economic load dispatch problems in power systems using chaotic and gaussian particle swarm optimization approaches. International Journal of Electrical Power & Energy Systems 30(5): 297-307.
- Coelho, L. S. and Mariani, V. C. (2008). Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects. Energy Conversion and Management 49(11): 3080-3085.
- Coelho, L. S. and Mariani, V. C. (2012). Firefly algorithm approach based on chaotic Tinkerbell map applied to multivariable PID controller tuning. Computers & Mathematics with Applications 64(8): 2371-2382.
- Coelho, L. S. and Pessôa, M. W. (2011). A tuning strategy for multivariable PI and PID controllers using differential evolution combined with chaotic Zaslavskii map. Expert Systems with Applications 38(11): 13694-13701.
- Da Silva, C. K. F., Da Silva, Z.E., and Mariani, V.C. (2009). Determination of the diffusion coefficient of dry mushrooms using the inverse method. Journal of Food Engineering 95(1): 1-10.
- Eberhart, R. C. and Kennedy, J. F. (1995). A new optimizer using particle swarm optimization. In Proceedings of the International Symposium on Micro Machine and Human Science, Japan, 39-45.
- Eslami, M., Sharref, H., Khajehzadeh, M. and Mohamed, A. (2012). A survey of the state of the art in particle swarm optimization. Research Journal of Applied Sciences, Engineering and Technology 4(9): 1181- 1197.
- Fang, W., Sun, J., Ding, Y., Wu, X., and Xu, W. (2010). A review of quantum-behaved particle swarm optimization. IETE Technical Review 27(4): 336-348.
- Hénon, M. (1976). A two-dimensional mapping with a strange attractor. Communications in Mathematical Physics 50(1): 69-77.
- Kennedy, J. F. and Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the IEEE Conference on Neural Networks, Perth, Australia, 1942-1948.
- Khare, A. and Rangnekar, S. (2013). Particle swarm optimization: a review. Applied Soft Computing (in press).
- Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of the Atmospheric Sciences 20(2): 130-141.
- Mukhopadhyay, S. and Banerjee, S. (2012). Global optimization of an optical chaotic system by chaotic multi swarm particle swarm optimization. Expert Systems with Applications 39(1) 917-924.
- Parsopoulos, K. E. and Vrahatis, M. H. (2002). Recent approaches to global optimization problems through particle swarm optimization. Natural Computing 1(2- 3): 235-306.
- Peitgen, H. -O., Jürgens, H., and Saupe, D. (2004). Chaos and fractals: new frontiers of science, 2nd edition, Springer, New York, NY, USA.
- Ratnaweera, A., Halgamuge, S., and Watson, H. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation 8(3): 240- 255.
- Rosenbrock, H. H. (1960). An automatic method for finding the greatest or least value of a function. The Computer Journal 3: 175-184.
- Scheerlinck, N., Verboven, P., Fikiin, K. A., de Baerdemacker, J., and Nicolaï, B. M. (2001). Finite element computation of unsteady phase change heat transfer during freezing or thawing of food using a combined enthalpy and Kirchhoff transform method. Transactions of the ASAE, 44(2): 429-438.
- Shang, Y. W. and Qiu, Y. H. (2006). A note on the extended Rosenbrock function, Evolutionary Computation 14(1): 119-126.
- Sun, J., Xu, W. B., and Feng, B. (2004a). A global search strategy of quantum-behaved particle swarm optimization. In Proceedings of IEEE Conference on Cybernetics and Intelligent Systems, Singapore, 111- 116.
- Sun, C. and Lu, S. (2010). Short-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization. Expert Systems with Applications 37(6): 4232-4241.
- Sun, J., Feng, B., and Xu, W. B. (2004b). Particle swarm optimization with particles having quantum behavior. In Proceedings of Congress on Evolutionary Computation, Portland, Oregon, USA, 325-331.
- Sun, J., Wu, X., Palade, V., Fang, W., Lai, C. -H. and Xu, W. (2012). Convergence analysis and improvements of quantum-behaved particle swarm optimization. Information Sciences 193: 81-103.
- Wang, Y., Zhou, J., Lu, Y., Qin, H., and Wang, Y. (2011). Chaotic self-adaptive particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effects. Expert Systems with Applications 38(11): 14231-14237.
- Yang, C. -H., Tsai, S. -W., Chuang, L. -Y., and Yang, C. - H. (2012). An improved particle swarm optimization with double-bottom chaotic maps for numerical optimization. Applied Mathematics and Computation 219(1): 260-279.
Paper Citation
in Harvard Style
dos Santos Coelho L., A. Guerra F., Pasquim B. and Cocco Mariani V. (2013). Chaotic Quantum-behaved Particle Swarm Optimization Approach Applied to Inverse Heat Transfer Problem . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 97-102. DOI: 10.5220/0004538900970102
in Bibtex Style
@conference{ecta13,
author={Leandro dos Santos Coelho and Fabio A. Guerra and Bruno Pasquim and Viviana Cocco Mariani},
title={Chaotic Quantum-behaved Particle Swarm Optimization Approach Applied to Inverse Heat Transfer Problem},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)},
year={2013},
pages={97-102},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004538900970102},
isbn={978-989-8565-77-8},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)
TI - Chaotic Quantum-behaved Particle Swarm Optimization Approach Applied to Inverse Heat Transfer Problem
SN - 978-989-8565-77-8
AU - dos Santos Coelho L.
AU - A. Guerra F.
AU - Pasquim B.
AU - Cocco Mariani V.
PY - 2013
SP - 97
EP - 102
DO - 10.5220/0004538900970102