Elitist Rao Algorithms and R-Method for Optimization of Energy Systems

References

• T. Fichter, F. Trieb, and M. Moser, “Optimized integration of renewable energy technologies into Jordan’s power plant portfolio,” Heat Transf. Eng., vol. 35, no. 3, pp. 281–301, 2014. DOI: 10.1080/01457632.2013.825183.
   Web of Science ®Google Scholar
• M. H. Ahmadi, A. H. Mohammadi, S. Dehghani, and M. A. Barranco-Jiménez, “Multi-objective thermodynamic-based optimization of output power of solar dish-Stirling engine by implementing an evolutionary algorithm,” Energy Convers. Manag., vol. 75, pp. 438–445, Nov. 2013. DOI: 10.1016/j.enconman.2013.06.030.
   Web of Science ®Google Scholar
• M. Mehrpooya, M. Ashouri, and A. Mohammadi, “Thermoeconomic analysis and optimization of a regenerative two-stage organic Rankine cycle coupled with liquefied natural gas and solar energy,” Energy, vol. 126, pp. 899–914, May 2017. DOI: 10.1016/j.energy.2017.03.064.
   Web of Science ®Google Scholar
• R. Jing et al., “A multi-objective optimization and multi-criteria evaluation integrated framework for distributed energy system optimal planning,” Energy Convers. Manag., vol. 166, pp. 445–462, Jun. 2018. DOI: 10.1016/j.enconman.2018.04.054.
   Web of Science ®Google Scholar
• J. Mahmoudimehr and P. Sebghati, “A novel multi-objective dynamic programming optimization method: Performance management of a solar thermal power plant as a case study,” Energy, vol. 168, pp. 796–814, Feb. 2019. DOI: 10.1016/j.energy.2018.11.079.
   Web of Science ®Google Scholar
• S. Khanmohammadi, F. Musharavati, O. Kizilkan, and D. D. Nguyen, “Proposal of a new parabolic solar collector assisted power-refrigeration system integrated with thermoelectric generator using 3E analyses: Energy, exergy, and exergo-economic,” Energy Convers. Manag., vol. 220, article no. 113055, (13 pages), Sep. 2020. DOI: 10.1016/j.enconman.2020.113055.
   Web of Science ®Google Scholar
• Z. Song, T. Liu, and Q. Lin, “Multi-objective optimization of a solar hybrid CCHP system based on different operation modes,” Energy, vol. 206, article no. 118125, (15 pages), Sep. 2020. DOI: 10.1016/j.energy.2020.118125.
   Web of Science ®Google Scholar
• A. Behzadi, A. Habibollahzade, P. Ahmadi, E. Gholamian, and E. Houshfar, “Multi-objective design optimization of a solar based system for electricity, cooling, and hydrogen production,” Energy, vol. 169, pp. 696–709, Feb. 2019. DOI: 10.1016/j.energy.2018.12.047.
   Web of Science ®Google Scholar
• C. Duan, X. Wang, S. Shu, C. Jing, and H. Chang, “Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm,” Energy Convers. Manag., vol. 84, pp. 88–96, Aug. 2014. DOI: 10.1016/j.enconman.2014.04.003.
   Web of Science ®Google Scholar
• Y. Li, S. Liao, and G. Liu, “Thermo-economic multi-objective optimization for a solar-dish Brayton system using NSGA-II and decision making,” Int. J. Electr. Power Energy Syst., vol. 64, pp. 167–175, Jan. 2015. DOI: 10.1016/j.ijepes.2014.07.027.
   Web of Science ®Google Scholar
• A. R. Starke, J. M. Cardemil, and S. Colle, “Multi-objective optimization of a solar-assisted heat pump for swimming pool heating using genetic algorithm,” Appl. Therm. Eng., vol. 142, pp. 118–126, Sep. 2018. DOI: 10.1016/j.applthermaleng.2018.06.067.
   Web of Science ®Google Scholar
• M. Meyer, M. Mehrabi, and J. P. Meyer, “Modeling and multi-objective optimization of heat transfer characteristics and pressure drop of nanofluids in microtubes,” Heat Transf. Eng., vol. 42, no. 21, pp. 1811–1826, 2021. DOI: 10.1080/01457632.2020.1826740.
   Web of Science ®Google Scholar
• R. V. Rao, H. S. Keesari, P. Oclo, and J. Taler, “An adaptive multi-team perturbation-guiding Jaya algorithm for optimization and its applications,” Eng. Comput., vol. 36, no. 1, pp. 391–419, Jan. 2020. DOI: 10.1007/s00366-019-00706-3.
   Web of Science ®Google Scholar
• R. V. Rao, H. S. Keesari, P. Oclon, and J. Taler, “Improved multi-objective Jaya optimization algorithm for a solar dish Stirling engine,” J. Renew. Sustain. Energy, vol. 11, no. 2, article no. 25903 (22 pages), 2019. DOI: 10.1063/1.5083142.
   Web of Science ®Google Scholar
• R. V. Rao, A. Saroj, P. Oclon, J. Taler, and D. Taler, “Single- and multi-objective design optimization of plate-fin heat exchangers using Jaya algorithm,” Heat Transf. Eng., vol. 39, no. 13–14, pp. 1201–1216, 2018. DOI: 10.1080/01457632.2017.1363629.
   Web of Science ®Google Scholar
• N. Damanik, H. C. Ong, C. W. Tong, T. M. I. Mahlia, and A. S. Silitonga, “A review on the engine performance and exhaust emission characteristics of diesel engines fueled with biodiesel blends,” Environ. Sci. Pollut. Res. Int., vol. 25, no. 16, pp. 15307–15325, Jun. 2018. DOI: 10.1007/s11356-018-2098-8.
   PubMed Web of Science ®Google Scholar
• G. Pohit and D. Misra, “Optimization of performance and emission characteristics of diesel engine with biodiesel using Grey-Taguchi method,” J. Eng., vol. 2013, article no. 915357, (8 pages), 2013. DOI: 10.1155/2013/915357.
   Google Scholar
• O. M. Ali, R. Mamat, G. Najafi, T. Yusaf, and S. M. S. Ardebili, “Optimization of biodiesel-diesel blended fuel properties and engine performance with ether additive using statistical analysis and response surface methods,” Energies, vol. 8, no. 12, pp. 14136–14150, 2015. DOI: 10.3390/en81212420.
   Web of Science ®Google Scholar
• Y. D. Bharadwaz, B. G. Rao, V. D. Rao, and C. Anusha, “Improvement of biodiesel methanol blends performance in a variable compression ratio engine using response surface methodology,” Alexandria Eng. J., vol. 55, no. 2, pp. 1201–1209, 2016. DOI: 10.1016/j.aej.2016.04.006.
   Web of Science ®Google Scholar
• A. Atmanli, E. Ileri, and N. Yilmaz, “Optimization of diesel-butanol-vegetable oil blend ratios based on engine operating parameters,” Energy, vol. 96, pp. 569–580, Feb. 2016. DOI: 10.1016/j.energy.2015.12.091.
   Web of Science ®Google Scholar
• A. Sharma, M. Muqeem, A. F. Sherwani, and M. Ahmad, “Optimization of diesel engine input parameters running on Polanga biodiesel to improve performance and exhaust emission using MOORA technique with standard deviation,” Energy Sources Part A Recover. Util. Environ. Eff., vol. 40, no. 22, pp. 2753–2770, 2018. DOI: 10.1080/15567036.2018.1511647.
   Web of Science ®Google Scholar
• S. Dey, N. M. Reang, M. Deb, and P. K. Das, “Study on performance-emission trade-off and multi-objective optimization of diesel-ethanol-palm biodiesel in a single cylinder CI engine: A Taguchi-fuzzy approach,” Energy Sources Part A Recover. Util. Environ. Eff., 2020. DOI: 10.1080/15567036.2020.1767234.
   Web of Science ®Google Scholar
• S. Dey, N. M. Reang, A. Majumder, M. Deb, and P. K. Das, “A hybrid ANN-fuzzy approach for optimization of engine operating parameters of a CI engine fueled with diesel-palm biodiesel-ethanol blend,” Energy, vol. 202, article no. 117813 (17 pages), pp. 17, Jul. 2020.10.1016/j.energy.2020.117813.
   Web of Science ®Google Scholar
• S. Saravanan, R. Kaliyanasunder, B. Rajesh Kumar, and G. Lakshmi Narayana Rao, “Effect of design parameters on performance and emissions of a CI engine operated with diesel-biodiesel-higher alcohol blends,” Renew. Energy, vol. 148, pp. 425–436, Apr. 2020. DOI: 10.1016/j.renene.2019.10.049.
   Web of Science ®Google Scholar
• J. Hirkude and V. Belokar, “Investigations on performance of CI engine with waste palm oil biodiesel-diesel blends using response surface methodology,” in Advances in Energy Research, Vol. 2, Springer Proceedings in Energy, S. Singh and V. Ramadesigan, Eds. Singapore: Springer, 2020, pp. 505–514. DOI: 10.1007/978-981-15-2662-6_46.
   Google Scholar
• K. I. Wong, P. K. Wong, C. S. Cheung, and C. M. Vong, “Modeling and optimization of biodiesel engine performance using advanced machine learning methods,” Energy, vol. 55, pp. 519–528, Jun. 2013. DOI: 10.1016/j.energy.2013.03.057.
   Web of Science ®Google Scholar
• P. K. Wong, K. I. Wong, C. M. Vong, and C. S. Cheung, “Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search,” Renew. Energy, vol. 74, pp. 640–647, Feb. 2015. DOI: 10.1016/j.renene.2014.08.075.
   Web of Science ®Google Scholar
• A. Shirneshan, B. H. Samani, and B. Ghobadian, “Optimization of biodiesel percentage in fuel mixture and engine operating conditions for diesel engine performance and emission characteristics by Artificial Bees Colony Algorithm,” Fuel, vol. 184, pp. 518–526, Nov. 2016. DOI: 10.1016/j.fuel.2016.06.117.
   Web of Science ®Google Scholar
• F. Jaliliantabar, B. Ghobadian, G. Najafi, R. Mamat, and A. P. Carlucci, “Multi-objective NSGA-II optimization of a compression ignition engine parameters using biodiesel fuel and exhaust gas recirculation,” Energy, vol. 187, article no. 115970 (15 pages), pp. 15, Nov. 2019.10.1016/j.energy.2019.115970.
   Web of Science ®Google Scholar
• A. T. D. Perera, P. U. Wickramasinghe, M. N. Vahid, and J.-L. Scartezzini, “Machine learning methods to assist energy system optimization,” Appl. Energy, vol. 243, pp. 191–205, Jun. 2019. DOI: 10.1016/j.apenergy.2019.03.202.
   Web of Science ®Google Scholar
• T. Gao and W. Lu, “Machine learning toward advanced energy storage devices and systems,” iScience, vol. 24, no. 1, article no. 101936 (33 pages), 2021. DOI: 10.1016/j.isci.2020.101936.
   PubMed Web of Science ®Google Scholar
• M. M. Rahman et al., “Prospective methodologies in hybrid renewable energy systems for energy prediction using artificial neural networks,” Sustainability, vol. 13, no. 4, article no. 2393 (28 pages), 2021. DOI: 10.3390/su13042393.
   Web of Science ®Google Scholar
• Y. Gao, J. Li, and M. Hong, “Machine learning based optimization model for energy management of energy storage system for large industrial park,” Processes, vol. 9, no. 5, article no. 825 (23 pages), 2021. DOI: 10.3390/pr9050825.
   Web of Science ®Google Scholar
• W. Khan, S. Walker, and W. Zeiler, “Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach,” Energy, vol. 240, article no. 122812 (16 pages), pp. 122812, Feb. 2022. DOI: 10.1016/j.energy.2021.122812.
   Web of Science ®Google Scholar
• R. V. Rao and H. S. Keesari, “Solar assisted heat engine systems: Multi-objective optimization and decision making,” Int. J. Ambient Energy, vol. 43, no. 1, pp. 149–175, 2022. DOI: 10.1080/01430750.2019.1636870.
   Web of Science ®Google Scholar
• M. H. Shojaeefard and J. Zare, “An investigation of the potential of improving an R1234yf parallel flow condenser performance using modeling and hybrid procedure of the modified NSGA-II and TOPSIS,” Heat Transf. Eng., vol. 39, no. 15, pp. 1405–1422, 2018. DOI: 10.1080/01457632.2017.1366239.
   Web of Science ®Google Scholar
• R. V. Rao, “Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems,” Int. J. Ind. Eng. Comput. vol. 11, no. 1, pp. 107–130, 2020. DOI: 10.5267/j.ijiec.2019.6.002.
   Web of Science ®Google Scholar
• R. V. Rao and R. B. Pawar, “Constrained design optimization of selected mechanical system components using Rao algorithms,” Appl. Soft Comput., vol. 89, article no. 106141 (22 pages), Apr. 2020. DOI: 10.1016/j.asoc.2020.106141.
   Web of Science ®Google Scholar
• R. V. Rao and H. S. Keesari, “Rao algorithms for multi-objective optimization of selected thermodynamic cycles,” Eng. Comput., vol. 37, no. 4, pp. 3409–3437, Oct. 2021. DOI: 10.1007/s00366-020-01008-9.
   Web of Science ®Google Scholar
• R. V. Rao and H. S. Keesari, “A self-adaptive population Rao algorithm for optimization of selected bio-energy systems,” J. Comput. Des. Eng., vol. 8, no. 1, pp. 69–96, Feb. 2021. DOI: 10.1093/jcde/qwaa063.
   Web of Science ®Google Scholar
• J. Bureick, H. Alkhatib, and I. Neumann, “Fast converging elitist genetic algorithm for knot adjustment in B-spline curve approximation,” J. Appl. Geodesy, vol. 13, no. 4, pp. 317–328, 2019. DOI: 10.1515/jag-2018-0015.
   Web of Science ®Google Scholar
• A. Wu and Z.-L. Yang, “An elitist transposon quantum-based particle swarm optimization algorithm for economic dispatch problems,” Complexity, vol. 2018, article no. 7276585 (16 pages), 2018. DOI: 10.1155/2018/7276585.
   Web of Science ®Google Scholar
• R. Rajesh, “Network design for resilience in supply chains using novel crazy elitist TLBO,” Neural Comput. Appl., vol. 32, no. 11, pp. 7421–7437, Jun. 2020. DOI: 10.1007/s00521-019-04260-3.
   Web of Science ®Google Scholar
• S. Prakash, V. Trivedi, and M. Ramteke, “An elitist non-dominated sorting bat algorithm NSBAT-II for multi-objective optimization of phthalic anhydride reactor,” Int. J. Syst. Assur. Eng. Manag., vol. 7, no. 3, pp. 299–315, Sep. 2016. DOI: 10.1007/s13198-016-0467-6.
   Web of Science ®Google Scholar
• V. Ho-Huu, T. Nguyen-Thoi, T. Vo-Duy, and T. Nguyen-Trang, “An adaptive elitist differential evolution for optimization of truss structures with discrete design variables,” Comput. Struct., vol. 165, pp. 59–75, Mar. 2016. DOI: 10.1016/j.compstruc.2015.11.014.
   Web of Science ®Google Scholar
• J. Li, S. Fong, R. K. L. Wong, R. Millham, and K. K. L. Wong, “Elitist binary wolf search algorithm for heuristic feature selection in high-dimensional bioinformatics datasets,” Sci. Rep., vol. 7, no. 1, article no. 4354 (14 pages), 2017. DOI: 10.1038/s41598-017-04037-5.
   PubMed Web of Science ®Google Scholar
• X. Wu and S. Wu, “An elitist quantum-inspired evolutionary algorithm for the flexible job-shop scheduling problem,” J. Intell. Manuf., vol. 28, no. 6, pp. 1441–1457, Aug. 2017. DOI: 10.1007/s10845-015-1060-6.
   Web of Science ®Google Scholar
• R. V. Rao and A. Saroj, “Multi-objective design optimization of heat exchangers using elitist-Jaya algorithm,” Energy Syst., vol. 9, no. 2, pp. 305–341, May 2018. DOI: 10.1007/s12667-016-0221-9.
   Google Scholar
• R. V. Rao and R. J. Lakshmi, “Ranking of Pareto-optimal solutions and selecting the best solution in multi-and many-objective optimization problems using R-method,” Soft Comput. Lett., vol. 3, article no. 100015 (18 pages), Dec. 2021. DOI: 10.1016/j.socl.2021.100015.
   Google Scholar
• R. V. Rao and R. J. Lakshmi, “R-method: A simple ranking method for multi-attribute decision-making in the industrial environment,” J. Project Manag., vol. 6, no. 4, pp. 223–230, 2021. DOI: 10.5267/j.jpm.2021.5.001.
   Web of Science ®Google Scholar
• R. V. Rao, “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems,” Int. J. Ind. Eng. Comput., vol. 7, no. 1, pp. 19–34, 2016. DOI: 10.5267/j.ijiec.2015.8.004.
   Google Scholar
• D. Karaboga and B. Akay, “A comparative study of Artificial Bee Colony algorithm,” Appl. Math. Comput., vol. 214, no. 1, pp. 108–132, Aug. 2009. DOI: 10.1016/j.amc.2009.03.090.
   Web of Science ®Google Scholar
• R. V. Rao and V. Patel, “Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems,” IJIEC, vol. 4, no. 1, pp. 29–50, 2013. DOI: 10.5267/j.ijiec.2012.09.001.
   Google Scholar
• R. V. Rao and R. B. Pawar, “Self-adaptive multi-population Rao algorithms for engineering design optimization,” Appl. Artif. Intell., vol. 34, no. 3, pp. 187–250, 2020. DOI: 10.1080/08839514.2020.1712789.
   Web of Science ®Google Scholar
• M. H. Ahmadi, H. Sayyaadi, A. H. Mohammadi, and M. A. Barranco-Jimenez, “Thermo-economic multi-objective optimization of solar dish-Stirling engine by implementing evolutionary algorithm,” Energy Convers. Manag., vol. 73, pp. 370–380, Sep. 2013. DOI: 10.1016/j.enconman.2013.05.031.
   Web of Science ®Google Scholar