Hybrid Soft Computing Approaches pp 155-183 | Cite as

# Collaborative Simulated Annealing Genetic Algorithm for Geometric Optimization of Thermo-electric Coolers

## Abstract

Thermo-electric Coolers (TECs) nowadays are applied in a wide range of thermal energy systems. This is due to its superior features where no refrigerant and dynamic parts are needed. TECs generate no electrical or acoustical noise and are environment friendly. Over the past decades, many researches were employed to improve the efficiency of TECs by enhancing the material parameters and design parameters. The material parameters are the most significant, but they are restricted by currently available materials and module fabricating technologies. Therefore, the main objective of TECs design is to determine a set of design parameters such as leg area, leg length, and the number of legs. Two elements that play an important role when considering the suitability of TECs in applications are rated of refrigeration (ROR) and coefficient of performance (COP). In this chapter, the technical issues of TECs were discussed. After that, a new method of optimizing the dimension of TECs using collaborative simulated annealing genetic algorithm (CSAGA) to maximize the rate of refrigeration (ROR) was proposed. Equality constraint and inequality constraint were taken into consideration. The results of optimization obtained by using CSAGA were validated by comparing with those obtained by using stand-alone genetic algorithm and simulated annealing optimization technique. This work revealed that CSAGA was more robust and more reliable than stand-alone genetic algorithm and simulated annealing.

## Keywords

Thermo-electrics coolers Thermal energy system Rate of refrigeration Coefficient of performance Collaborative simulated annealing genetic algorithm Geometric properties Material properties Genetic algorithm Simulated annealing## Notes

### Acknowledgments

This research work was supported by Universiti Teknologi PETRONAS (UTP) under the Exploratory Research Grant Scheme-PCS-No. 0153AB-121 (ERGS) of Ministry of Higher Education Malaysia (MOHE). The authors would like to sincerely thank the Department of Fundamental and Applied Sciences (DFAS) and Centre of Graduate Studies (CGS) of UTP for their strong support in carrying out this research work.

## References

- 1.Scherbatskoy SA (1982) Systems, apparatus and methods for measuring while drilling: Google PatentsGoogle Scholar
- 2.Goldsmid, HJ (2009) The thermoelectric and related effects. Introduction to thermoelectricity. Springer, Berlin, pp 1–6Google Scholar
- 3.Deb K (2001) Multi-objective optimization. Multi-objective optimization using evolutionary algorithms, pp 13–46Google Scholar
- 4.Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolut Comput. IEEE Trans 6(2):182–197CrossRefGoogle Scholar
- 5.Van Laarhoven PJ, Aarts EH (1987) Simulated annealing. Springer, New YorkGoogle Scholar
- 6.Storn R, Price K (1995) Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. ICSI Berkeley, BerkeleyGoogle Scholar
- 7.Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57CrossRefGoogle Scholar
- 8.Cheng Y-H, Lin W-K (2005) Geometric optimization of thermoelectric coolers in a confined volume using genetic algorithms. Appl Therm Eng 25(17–18):2983–2997. doi: 10.1016/j.applthermaleng.2005.03.007 CrossRefGoogle Scholar
- 9.Huang Y-X, Wang X-D, Cheng C-H, Lin DT-W (2013) Geometry optimization of thermoelectric coolers using simplified conjugate-gradient method. Energy 59:689–697. doi: 10.1016/j.energy.2013.06.069 CrossRefGoogle Scholar
- 10.Cheng Y-H, Shih C (2006) Maximizing the cooling capacity and COP of two-stage thermoelectric coolers through genetic algorithm. Appl Therm Eng 26(8–9):937–947. doi: 10.1016/j.applthermaleng.2005.09.016 CrossRefGoogle Scholar
- 11.Xuan XC, Ng KC, Yap C, Chua HT (2002) Optimization of two-stage thermoelectric coolers with two design configurations. Energy Convers Manag 43(15):2041–2052. doi: 10.1016/S0196-8904(01)00153-4 CrossRefGoogle Scholar
- 12.Nain PKS, Giri JM, Sharma S, Deb K (2010) Multi-objective performance optimization of thermo-electric coolers using dimensional structural parameters. In: Panigrahi B, Das, S, Suganthan P, Dash S (eds), Swarm, Evolutionary, and Memetic Computing, vol 6466. Springer, Berlin, pp 607–614Google Scholar
- 13.Venkata Rao R, Patel V (2013) Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm. Eng Appl Artif Intell 26(1):430–445. doi: 10.1016/j.engappai.2012.02.016 CrossRefGoogle Scholar
- 14.Yamashita O, Sugihara S (2005) High-performance bismuth-telluride compounds with highly stable thermoelectric figure of merit. J Mater Sci 40(24):6439–6444. doi: 10.1007/s10853-005-1712-6 CrossRefGoogle Scholar
- 15.Rodgers P (2008) Nanomaterials: Silicon goes thermoelectric. Nat Nano 3(2):76–76Google Scholar
- 16.Poudel B, Hao Q, Ma Y, Lan Y, Minnich A, Yu B, Vashaee D (2008) High-thermoelectric performance of nanostructured bismuth antimony telluride bulk alloys. Science 320(5876):634–638CrossRefGoogle Scholar
- 17.Goldsmid HJ (2009) Introduction to thermoelectricity, vol 121. Springer, HeidelbergGoogle Scholar
- 18.Yamashita O, Tomiyoshi S (2004) Effect of annealing on thermoelectric properties of bismuth telluride compounds doped with various additives. J Appl Phys 95(1):161–169. doi: 10.1063/1.1630363 CrossRefGoogle Scholar
- 19.Rowe D, Min G (1996) Design theory of thermoelectric modules for electrical power generation. IEE Proc Sci Meas Technol 143(6):351–356CrossRefGoogle Scholar
- 20.Boussaïd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Information sciencesGoogle Scholar
- 21.Lee KY, El-Sharkawi MA (2008) Modern heuristic optimization techniques: theory and applications to power systems, vol 39. Wiley, New YorkGoogle Scholar
- 22.Geng H, Zhu H, Xing R, Wu T (2012) A novel hybrid evolutionary algorithm for solving multi-objective optimization problems. In: Huang, D-S, Jiang, C, Bevilacqua V, Figueroa J (eds), Intelligent computing technology, vol 7389. Springer, Berlin, pp 128–136Google Scholar
- 23.Vasant P (2010) Hybrid simulated annealing and genetic algorithms for industrial production management problems. Int J Comput Methods 7(2):279–297CrossRefMathSciNetGoogle Scholar
- 24.Miettinen K (1999) Nonlinear multiobjective optimization, vol 12. Springer, New YorkGoogle Scholar
- 25.Blum C, Roli A (2008) Hybrid metaheuristics: an introduction. Hybrid metaheuristics. Springer, Heidelberg, pp 1–30Google Scholar
- 26.Blum C, Roli A, Sampels M (2008) Hybrid metaheuristics: an emerging approach to optimization, vol 114. Springer, BerlinGoogle Scholar
- 27.Shahsavari-Pour N, Ghasemishabankareh B (2013) A novel hybrid meta-heuristic algorithm for solving multi objective flexible job shop scheduling. J Manuf Syst 32(4):771–780. doi: 10.1016/j.jmsy.2013.04.015 CrossRefGoogle Scholar
- 28.Zhao D, Tan G (2014) A review of thermoelectric cooling: materials, modeling and applications. Appl Therm Eng 66(1–2):15–24. doi: 10.1016/j.applthermaleng.2014.01.074 CrossRefGoogle Scholar
- 29.Chen P-H, Shahandashti SM (2009) Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints. Autom Constr 18(4):434–443. doi: 10.1016/j.autcon.2008.10.007 CrossRefGoogle Scholar
- 30.Vasant P, Barsoum N (2009) Hybrid simulated annealing and genetic algorithms for industrial production management problemsGoogle Scholar
- 31.Dingjun C, Chung-Yeol L, Cheol-Hoon P (2005, 16–16 Nov 2005) Hybrid genetic algorithm and simulated annealing (HGASA) in global function optimization. In: 17th IEEE international conference on paper presented at the tools with artificial intelligence, ICTAI 05, 2005Google Scholar
- 32.Hazra J, Sinha A (2009) Application of soft computing methods for economic dispatch in power systems. Int J Electr Electron Eng 2:538–543Google Scholar
- 33.Liu K, Du X, Kang L (2007) Differential evolution algorithm based on simulated annealing. In: Kang L, Liu Y, Zeng S (eds), Advances in computation and intelligence, vol 4683. Springer, Berlin, pp 120–126Google Scholar
- 34.Jing-Yu Y, Qing L, De-Min S (2006, 13–16 Aug 2006) A differential evolution with simulated annealing updating method. International Conference on paper presented at the machine learning and cybernetics, 2006Google Scholar
- 35.Xu X, Wang S (2007) Optimal simplified thermal models of building envelope based on frequency domain regression using genetic algorithm. Energy Build 39(5):525–536. doi: 10.1016/j.enbuild.2006.06.010 CrossRefGoogle Scholar
- 36.Gozde H, Taplamacioglu MC (2011) Automatic generation control application with craziness based particle swarm optimization in a thermal power system. Int J Electr Power Energy Syst 33(1):8–16CrossRefGoogle Scholar
- 37.Kou H-S, Lee J-J, Chen C-W (2008) Optimum thermal performance of microchannel heat sink by adjusting channel width and height. Int Commun Heat Mass Transfer 35(5):577–582CrossRefGoogle Scholar
- 38.Pezzini P, Gomis-Bellmunt O, Sudrià-Andreu A (2011) Optimization techniques to improve energy efficiency in power systems. Renew Sustain Energy Rev 15(4):2028–2041CrossRefGoogle Scholar
- 39.Sharma N (2012) A particle swarm optimization algorithm for optimization of thermal performance of a smooth flat plate solar air heater. Energy 38(1):406–413CrossRefGoogle Scholar
- 40.Eynard J, Grieu S, Polit M (2011) Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption. Eng Appl Artif Intell 24(3):501–516CrossRefGoogle Scholar