• Vivek K. PatelEmail author
  • Vimal J. Savsani
  • Mohamed A. Tawhid


Thermal systems deal with the conversion of thermal energy (i.e., heat energy) into mechanical energy, which is further converted into electric energy. Design optimization of thermal systems involves a large number of design variables and constraints. The conventional methods of the thermal system design optimization apply an iterative procedure which may trap in local optimum. Advanced optimization algorithms offer solutions to the problems, because they find a solution nearer to the global optimum within reasonable time and computational costs.


  1. Ahrari A., Atai A.A. (2010) ‘Grenade explosion method-a novel tool for optimization of multimodal functions’, Applied Soft Computing, vol. 10, 1132–1140.CrossRefGoogle Scholar
  2. Bellman, R. E. (1957) Dynamic Programming, Princeton University Press, Princeton, NJ.Google Scholar
  3. Dorigo M., Maniezzo V., Colorni A. (1991) Positive feedback as a search strategy, Technical Report 91–016, Politecnico di Milano, Italy.Google Scholar
  4. Duffin R. J., Peterson E., Zener C. (1967) Geometric Programming, John Wiley, New York.Google Scholar
  5. Eberhart R., Kennedy J. (1997) ‘A new optimizer using particle swarm theory’, In: Proceedings of IEEE Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43.Google Scholar
  6. Eusuff M., Lansey E. (2003) ‘Optimization of water distribution network design using the shuffled frog leaping algorithm’, Journal of Water Resources Planning and Management, vol. 129, 210–225.CrossRefGoogle Scholar
  7. Farmer J.D., Packard N., Perelson A. (1986) ‘The immune system, adaptation and machine learning’, Physica D, vol. 22, 187–204.Google Scholar
  8. Gabriele G. A., Ragsdell K. M. (1977) ‘The generalized reduced gradient method: a reliable tool for optimal design’, ASME Journal of Engineering for Industry, vol. 99, 384–400.CrossRefGoogle Scholar
  9. Geem Z.W., Kim J.H., Loganathan G.V. (2001) ‘A new heuristic optimization algorithm: harmony search’, Simulation, vol. 76, 60–70.Google Scholar
  10. Holland J. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.Google Scholar
  11. Kantorovich L.V. (1940) ‘A new method of solving some classes of extrema problems’, Doklady Akad Science USSR, vol. 28, 211–214.Google Scholar
  12. Karaboga D. (2005) ‘An idea based on honey bee swarm for numerical optimization’, Technical report–TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.Google Scholar
  13. Kashan A. H. (2011) ‘An efficient algorithm for constrained global optimization and application to mechanical engineering design: League championship algorithm (LCA)’, Computer Aided Design, vol. 43, 1769–1792.Google Scholar
  14. Min-Yuan Cheng, Doddy Prayogo (2014) ‘Symbiotic Organisms Search: A new metaheuristic optimization algorithm’, Computers & Structures, vol. 139, 98–112.CrossRefGoogle Scholar
  15. Mirjalili S. (2016) ‘SCA: a sine-cosine algorithm for solving optimization problems. Knowledge-Based Systems’, vol. 96, pp. 120–133.CrossRefGoogle Scholar
  16. Passino K.M. (2002) ‘Biomimicry of Bacterial Foraging for Distributed Optimization and Control’, IEEE Control Systems Magazine, vol. 22, 52–67.Google Scholar
  17. Patel V.K. and Savsani V.J. (2015) ‘Heat transfer search (HTS): a novel optimization algorithm’, Information Sciences, vol. 324, pp. 217–246.Google Scholar
  18. Rao R.V., Savsani V.J. and Vakharia D.P. (2011) ‘Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems’, Computer- Aided Design, vol. 43(3), pp. 303–315.CrossRefGoogle Scholar
  19. Rashedi E., Nezamabadi-pour H., Saryazdi S. (2009) ‘GSA: A gravitational search algorithm’, Information Sciences, vol. 179, 2232–2248.Google Scholar
  20. Savsani P. and Savsani V. (2016) ‘Passing vehicle search (PVS): A novel metaheuristic algorithm’, Applied Mathematical Modelling, vol. 40(5–6), pp. 3951–3978.CrossRefGoogle Scholar
  21. Simon D. (2008) ‘Biogeography-based optimization’, IEEE Transactions on Evolutionary Computation, vol. 12(6), 702–713.CrossRefGoogle Scholar
  22. Storn R., Price K. (1997) ‘Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces’, Journal of Global Optimization, vol. 11, 341–359.MathSciNetCrossRefGoogle Scholar
  23. Wolfe P. (1959) ‘The simplex method for quadratic programming’, Econometrica, vol. 27, 382–398.Google Scholar
  24. Yang X.-S., Deb S. (2009) ‘Cuckoo search via Lévy flights. World Congress on nature & Biologically Inspired Computing (NaBIC 2009)’, IEEE Publications, pp. 210–214.Google Scholar
  25. Zheng Y.J. (2015) ‘Water wave optimization: a new nature-inspired metaheuristic. Computers & Operations Research’, vol. 55, pp. 1–11.MathSciNetCrossRefGoogle Scholar
  26. Zoutendijk G. (1960) Methods of Feasible Directions, Elsevier, Amsterdam.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Vivek K. Patel
    • 1
    Email author
  • Vimal J. Savsani
    • 2
  • Mohamed A. Tawhid
    • 3
  1. 1.Department of Mechanical Engineering, School of TechnologyPandit Deendayal Petroleum UniversityRaisan, GandhinagarIndia
  2. 2.Department of Mechanical EngineeringPandit Deendayal Petroleum UniversityRaisan, GandhinagarIndia
  3. 3.Department of Mathematics and StatisticsThompson Rivers UniversityKamloopsCanada

Personalised recommendations