Skip to main content
SpringerLink
Log in

Rao algorithms for multi-objective optimization of selected thermodynamic cycles

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

This work proposes multi-objective Rao algorithms. The basic Rao algorithms are modified for solving multi-objective optimization problems. The proposed algorithms have no algorithm-specific parameters and no metaphorical meaning. Based on the interaction of the population with best, worst, and randomly selected solutions, the proposed algorithms explore the search space. The proposed algorithms handle multiple objectives simultaneously based on dominance principles and crowding distance evaluation. In addition, multi-attribute decision-making method-based selection scheme for identifying the best solutions from the Pareto fronts is included. The proposed algorithm performances are investigated on a case study of solar-assisted Brayton heat engine system and a case study of Stirling heat engine system to see whether there can be any improvement in the performances of the considered systems. Furthermore, the efficiencies of the Rao algorithms are evaluated in terms of spacing, hypervolume, and coverage metrics. The results obtained by the proposed algorithms are compared with those obtained by the latest advanced optimization algorithms. It is observed that the results obtained by the proposed algorithms are superior. The performances of the considered case studies are improved by the application of the proposed optimization algorithms. The proposed optimization algorithms are simple, robust, and can be easily implemented to solve different engineering optimization problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Ahmadi MH, Mohammadi AH, Dehghani S, Barranco-Jiménez MA (2013) Multi-objective thermodynamic-based optimization of output power of Solar Dish-Stirling engine by implementing an evolutionary algorithm. Energy Convers Manag 75:438–445. https://doi.org/10.1016/j.enconman.2013.06.030

    Article  Google Scholar 

  2. Duan C, Wang X, Shu S, Jing C, Chang H (2014) Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm. Energy Convers Manag 84:88–96. https://doi.org/10.1016/j.enconman.2014.04.003

    Article  Google Scholar 

  3. Toghyani S, Kasaeian A, Ahmadi MH (2014) Multi-objective optimization of Stirling engine using non-ideal adiabatic method. Energy Convers Manag 80:54–62. https://doi.org/10.1016/j.enconman.2014.01.022

    Article  Google Scholar 

  4. Ahmadi MH, Ahmadi MA, Mohammadi AH, Mehrpooya M, Feidt M (2014) Thermodynamic optimization of Stirling heat pump based on multiple criteria. Energy Convers Manag 80:319–328. https://doi.org/10.1016/j.enconman.2014.01.031

    Article  Google Scholar 

  5. Soltani R, Mohammadzadeh Keleshtery P, Vahdati M, Khoshgoftarmanesh MH, Rosen MA, Amidpour M (2014) Multi-objective optimization of a solar-hybrid cogeneration cycle: application to CGAM problem. Energy Convers Manag 81:60–71. https://doi.org/10.1016/j.enconman.2014.02.013

    Article  Google Scholar 

  6. Khoshgoftar Manesh MH, Ameryan M (2016) Optimal design of a solar-hybrid cogeneration cycle using Cuckoo Search algorithm. Appl Therm Eng 102:1300–1313. https://doi.org/10.1016/j.applthermaleng.2016.03.156

    Article  Google Scholar 

  7. Li Y, Liao S, Liu G (2015) Thermo-economic multi-objective optimization for a solar-dish Brayton system using NSGA-II and decision making. Int J Electr Power Energy Syst 64:167–175. https://doi.org/10.1016/j.ijepes.2014.07.027

    Article  Google Scholar 

  8. Li Y, Liu G, Liu X, Liao S (2016) Thermodynamic multi-objective optimization of a solar-dish Brayton system based on maximum power output, thermal efficiency and ecological performance. Renew Energy 95:465–473. https://doi.org/10.1016/j.renene.2016.04.052

    Article  Google Scholar 

  9. Sánchez-orgaz S, Pedemonte M, Ezzatti P, Curto-risso PL, Medina A, Hernández AC (2015) Multi-objective optimization of a multi-step solar-driven Brayton plant. Energy Convers Manag 99:346–358. https://doi.org/10.1016/j.enconman.2015.04.077

    Article  Google Scholar 

  10. Arora R, Kaushik SC, Kumar R (2015) Multi-objective optimization of an irreversible regenerative Brayton cycle using genetic algorithm. In: 2015 international conference on futuristic trends on computational analysis and knowledge management, pp 340–346. https://doi.org/10.1109/ablaze.2015.7155017

  11. Arora R, Kaushik SC, Kumar R, Arora R (2016) Soft computing based multi-objective optimization of Brayton cycle power plant with isothermal heat addition using evolutionary algorithm and decision making. Appl Soft Comput J 46:267–283. https://doi.org/10.1016/j.asoc.2016.05.001

    Article  Google Scholar 

  12. Kumar R, Kaushik SC, Kumar R, Hans R (2016) Multi-objective thermodynamic optimization of an irreversible regenerative Brayton cycle using evolutionary algorithm and decision making. Ain Shams Eng J 7:741–753. https://doi.org/10.1016/j.asej.2015.06.007

    Article  Google Scholar 

  13. Zare V, Hasanzadeh M (2016) Energy and exergy analysis of a closed Brayton cycle-based combined cycle for solar power tower plants. Energy Convers Manag 128:227–237. https://doi.org/10.1016/j.enconman.2016.09.080

    Article  Google Scholar 

  14. Naserian MM, Farahat S, Sarhaddi F (2016) Exergoeconomic multi objective optimization and sensitivity analysis of a regenerative Brayton cycle. Energy Convers Manag 117:95–105. https://doi.org/10.1016/j.enconman.2016.03.014

    Article  Google Scholar 

  15. Luo Z, Sultan U, Ni M, Peng H, Shi B, Xiao G (2016) Multi-objective optimization for GPU3 Stirling engine by combining multi-objective algorithms. Renew Energy 94:114–125. https://doi.org/10.1016/j.renene.2016.03.008

    Article  Google Scholar 

  16. Nemati A, Nami H, Yari M, Ranjbar F, Rashid Kolvir H (2016) Development of an exergoeconomic model for analysis and multi-objective optimization of a thermoelectric heat pump. Energy Convers Manag 130:1–13. https://doi.org/10.1016/j.enconman.2016.10.045

    Article  Google Scholar 

  17. Jokar MA, Ahmadi MH, Sharifpur M, Meyer JP, Pourfayaz F, Ming T (2017) Thermodynamic evaluation and multi-objective optimization of molten carbonate fuel cell-supercritical CO2 Brayton cycle hybrid system. Energy Convers Manag 153:538–556. https://doi.org/10.1016/j.enconman.2017.10.027

    Article  Google Scholar 

  18. Yang F, Cho H, Zhang H, Zhang J (2017) Thermoeconomic multi-objective optimization of a dual loop organic Rankine cycle (ORC) for CNG engine waste heat recovery. Appl Energy 205:1100–1118. https://doi.org/10.1016/j.apenergy.2017.08.127

    Article  Google Scholar 

  19. Mehrpooya M, Ashouri M, Mohammadi A (2017) Thermoeconomic analysis and optimization of a regenerative two-stage organic Rankine cycle coupled with liquefied natural gas and solar energy. Energy 126:899–914. https://doi.org/10.1016/j.energy.2017.03.064

    Article  Google Scholar 

  20. Starke AR, Cardemil JM, Colle S (2018) Multi-objective optimization of a solar-assisted heat pump for swimming pool heating using genetic algorithm. Appl Therm Eng 142:118–126. https://doi.org/10.1016/j.applthermaleng.2018.06.067

    Article  Google Scholar 

  21. Dai D, Yuan F, Long R, Liu Z, Liu W (2018) Performance analysis and multi-objective optimization of a Stirling engine based on MOPSOCD. Int J Therm Sci 124:399–406. https://doi.org/10.1016/j.ijthermalsci.2017.10.030

    Article  Google Scholar 

  22. Kim T, Choi BI, Han YS, Do KH (2018) A comparative investigation of solar-assisted heat pumps with solar thermal collectors for a hot water supply system. Energy Convers Manag 172:472–484. https://doi.org/10.1016/j.enconman.2018.07.035

    Article  Google Scholar 

  23. Sanaye S, Taheri M (2018) Modeling and multi-objective optimization of a modified hybrid liquid desiccant heat pump (LD-HP) system for hot and humid regions. Appl Therm Eng 129:212–229. https://doi.org/10.1016/j.applthermaleng.2017.09.116

    Article  Google Scholar 

  24. Ye W, Yang P, Liu Y (2018) Multi-objective thermodynamic optimization of a free piston Stirling engine using response surface methodology. Energy Convers Manag 176:147–163. https://doi.org/10.1016/j.enconman.2018.09.011

    Article  Google Scholar 

  25. Kleef LMTV, Oyewunmi OA, Markides CN (2019) Multi-objective thermo-economic optimization of organic Rankine cycle (ORC) power systems in waste-heat recovery applications using computer-aided molecular design techniques. Appl Energy 251:112513. https://doi.org/10.1016/j.apenergy.2019.01.071

    Article  Google Scholar 

  26. Rao RV, Keesari HS, Oclon KP, Taler J (2019) An adaptive multi-team perturbation-guiding Jaya algorithm for optimization and its applications. Eng Comput. https://doi.org/10.1007/s00366-019-00706-3

    Article  Google Scholar 

  27. Rao RV, Keesari HS, Oclon P, Taler J (2019) Improved multi-objective Jaya optimization algorithm for a solar dish Stirling engine. J Renew Sustain Energy 11:25903. https://doi.org/10.1063/1.5083142

    Article  Google Scholar 

  28. Rao RV, Keesari HS (2019) Solar assisted heat engine systems: multi-objective optimization and decision making. Int J Ambient Energy. https://doi.org/10.1080/01430750.2019.1636870

    Article  Google Scholar 

  29. Bellos E, Tzivanidis C (2019) Multi-objective optimization of a solar assisted heat pump-driven by hybrid PV. Appl Therm Eng 149:528–535. https://doi.org/10.1016/j.applthermaleng.2018.12.059

    Article  Google Scholar 

  30. Bellos E, Tzivanidis C (2019) Investigation and optimization of a solar assisted heat pump driven by nanofluid-based hybrid PV. Appl Therm Eng 198:111831. https://doi.org/10.1016/j.applthermaleng.2019.111831

    Article  Google Scholar 

  31. Kwan TH, Wu X, Yao Q (2019) Performance comparison of several heat pump technologies for fuel cell micro-CHP integration using a multi-objective optimisation approach. Appl Therm Eng 160:114002. https://doi.org/10.1016/j.applthermaleng.2019.114002

    Article  Google Scholar 

  32. Rao RV (2020) Rao algorithms: three metaphor-less simple algorithms for solving optimization problems. Int J Ind Eng Comput 11:107–130. https://doi.org/10.5267/j.ijiec.2019.6.002

    Article  Google Scholar 

  33. Rao RV, Pawar RB (2020) Self-adaptive multi-population Rao algorithms for engineering design optimization. Appl Artif Intell 00:1–64. https://doi.org/10.1080/08839514.2020.1712789

    Article  Google Scholar 

  34. Rao RV, Pawar RB (2020) Constrained design optimization of selected mechanical system components using Rao algorithms. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2020.106141

    Article  Google Scholar 

  35. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  36. Rao RV, Rai DP, Balic J (2017) A multi-objective algorithm for optimization of modern machining processes. Eng Appl Artif Intell 61:103–125. https://doi.org/10.1016/j.engappai.2017.03.001

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, 395007, India

    R. Venkata Rao & Hameer Singh Keesari

Authors
  1. R. Venkata Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hameer Singh Keesari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hameer Singh Keesari.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rao, R.V., Keesari, H.S. Rao algorithms for multi-objective optimization of selected thermodynamic cycles. Engineering with Computers 37, 3409–3437 (2021). https://doi.org/10.1007/s00366-020-01008-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01008-9

Keywords

Search

Navigation