Abstract
A wide variety of evolutionary optimization algorithms have been used by researcher for optimal design of shell and tube heat exchangers (STHX). The purpose of optimization is to minimize capital and operational costs subject to efficiency constraints. This paper comprehensively examines performance of genetic algorithm (GA) and cuckoo search (CS) for solving STHX design optimization. While GA has been widely adopted in the last decade for STHX optimal design, there is no report on application of CS method for this purpose. Simulation results in this paper demonstrate that CS greatly outperforms GA in terms of finding admissible and optimal configurations for STHX. It is also found that CS method not only has a lower computational requirement, but also generates the most consistent results.
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References
Xie, G., Sunden, B., Wang, Q.: Optimization of compact heat exchangers by a genetic algorithm. Applied Thermal Engineering 28(8-9), 895–906 (2008)
Rao, R.V., Patel, V.: Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Applied Mathematical Modelling 37(3), 1147–1162 (2013)
Caputo, A.C., Pelagagge, P.M., Salini, P.: Heat exchanger design based on economic optimisation. Applied Thermal Engineering 28(10), 1151–1159 (2008)
Sanaye, S., Hajabdollahi, H.: Multi-objective optimization of shell and tube heat exchangers. Applied Thermal Engineering 30, 1937–1945 (2010)
Mariani, V.C., Duck, A.R.K., Guerra, F.A., Coelho, L.D.S., Rao, R.V.: A chaotic quantum-behaved particle swarm approach applied to optimization of heat exchangers. Applied Thermal Engineering 42, 119–128 (2012)
Hall, S., Ahmad, S., Smith, R.: Capital cost targets for heat exchanger networks comprising mixed materials of construction, pressure ratings and exchanger types. Computers & Chemical Engineering 14(3), 319–335 (1990)
Taal, M., Bulatov, I., Klemes, J., Stehlik, P.: Cost estimation and energy price forecasts for economic evaluation of retrofit projects. Applied Thermal Engineering 23(14), 1819–1835 (2003)
H., H.J.: Adaptation in Natural and Artificial Systems. Michigan Press (1975)
Rechenberg, I.: Evolutionsstrategie. Fromman-Hozboog Verlag (1973)
Hasancebi, O., Erbatur, F.: Evaluation of crossover techniques in genetic algorithm based optimum structural design. Computers and Structures 78(1-3), 435–448 (2000)
Kaya, M.: The effects of two new crossover operators on genetic algorithm performance. Applied Soft Computing 11(1), 881–890 (2011)
Goldberg, D.E.: Genetic Algorithm in Search, Optimization, and Machine Learning. Addision-Wesley, Reading (1989)
Yang, X.S., Deb, S.: Cuckoo search via levy flights. In: World Congress on Nature & Biologically Inspired Computing, pp. 210–214 (2009)
Yang, X.S.: Nature-inspired metaheuristic algorithms. Luniver Press (2008)
Yang, X.-S., Deb, S.: Cuckoo search via levy flights. In: World Congress on Nature and Biologically Inspired Computing, pp. 210–214 (2009)
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Khosravi, R., Khosravi, A., Nahavandi, S. (2014). Application of Cuckoo Search for Design Optimization of Heat Exchangers. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_22
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DOI: https://doi.org/10.1007/978-3-319-12640-1_22
Publisher Name: Springer, Cham
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