Comparison of Heuristic Approaches in Weight Optimization of Different Power Levels Transformers

References

• C. Congcong, L. Yueyu, and W. Jiaojiao, “A dynamic optimization method of intelligent material distribution based on hybrid heuristic algorithm,” in ICITM 2020 – 2020 9th International Conference on Industrial Technology and Management, Feb. 2020, pp. 210–4. DOI:10.1109/ICITM48982.2020.9080369.
   Google Scholar
• S. Aslan, and S. Demirci, “Immune plasma algorithm: A novel meta-heuristic for optimization problems,” IEEE. Access. 2020. DOI:10.1109/ACCESS.2020.3043174.
   PubMedGoogle Scholar
• M. Dorigo, “Optimization, learning and natural algorithms,” PhD Thesis, Dipartimento di Elettronica, 1992.
   Google Scholar
• M. Dorigo, G. Di Caro, and L. M. Gambardella, “Ant algorithms for discrete optimization,” Artif. Life, Vol. 5, no. 2, pp. 137–72, 1999. DOI:10.1162/106454699568728.
   PubMed Web of Science ®Google Scholar
• S. Oliveira, M. S. Hussin, A. Roli, M. Dorigo, and T. Stutzle, “Analysis of the population-based ant colony optimization algorithm for the TSP and the QAP,” in 2017 Proceedings of IEEE Congress on Evolutionary Computation CEC 2017, Jul. 2017, pp. 1734–41. DOI:10.1109/CEC.2017.7969511.
   Google Scholar
• M. B. Sushma, S. Roy, and A. Maji, “Exploring and exploiting ant colony optimization algorithm for vertical highway alignment development,” Comput. Civ. Infrastruct. Eng. Jan. 2022. DOI:10.1111/MICE.12814.
   Web of Science ®Google Scholar
• W. Deng, J. Xu, and H. Zhao, “An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem,” IEEE. Access., Vol. 7, pp. 20281–92, 2019. DOI:10.1109/ACCESS.2019.2897580.
   Web of Science ®Google Scholar
• W. Hou, Z. Xiong, C. Wang, and H. Chen, “Enhanced ant colony algorithm with communication mechanism for mobile robot path planning,” Rob. Auton. Syst, Vol. 148, pp. 103949, Feb. 2022. DOI:10.1016/J.ROBOT.2021.103949.
   Web of Science ®Google Scholar
• D. T. Pham, and A. Haj Darwish, “Using the bees algorithm with Kalman filtering to train an artificial neural network for pattern classification,” Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng, Vol. 224, no. 7, pp. 885–92, Nov. 2010. DOI:10.1243/09596518JSCE1004.
   Google Scholar
• D. T. Pham, S. Otri, A. Ghanbarzadeh, and E. Koc, “Application of the Bees algorithm to the training of learning vector quantisation networks for control chart pattern recognition,” Oct. 2006, pp. 1624–9. DOI:10.1109/ictta.2006.1684627.
   Google Scholar
• S. Daneshgar, and R. Zahedi, “Optimization of power and heat dual generation cycle of gas microturbines through economic, exergy and environmental analysis by bee algorithm,” Energy Rep, Vol. 8, pp. 1388–96, Nov. 2022. DOI:10.1016/J.EGYR.2021.12.044.
   Web of Science ®Google Scholar
• J. Wang, J. Cao, R. S. Sherratt, and J. H. Park, “An improved ant colony optimization-based approach with mobile sink for wireless sensor networks,” J. Supercomput, Vol. 74, no. 12, pp. 6633–45, Dec. 2018. DOI:10.1007/s11227-017-2115-6.
   Web of Science ®Google Scholar
• M. Shouran, F. Anayi, and M. Packianather, “Design of sliding mode control optimised by the bees algorithm for LFC in the great Britain power system,” Mater. Today Proc. Nov. 2021. DOI:10.1016/J.MATPR.2021.10.322.
   Google Scholar
• V. M. Mohan, R. M. Balajee, N. M. Jyothi, and M. N. V. Kiranbabu, “A contemporary access of improved BEE colony optimization for scheduling problems in cloud environment,” in Proceedings of 3rd IEEE International Conference on Inventive Research in Computing Applications ICIRCA 2021, Sep. 2021, pp. 780–5. DOI:10.1109/ICIRCA51532.2021.9544594.
   Google Scholar
• L. Baronti, M. Castellani, and D. T. Pham, “An analysis of the search mechanisms of the bees algorithm,” Swarm Evol. Comput, Vol. 59, pp. 100746, Dec. 2020. DOI:10.1016/J.SWEVO.2020.100746.
   Web of Science ®Google Scholar
• D. Kumar, A. K. Mishra, and K. Chatterjee, “Power and frequency control of a wind energy power system using artificial bee colony algorithm,” in ICONSTEM 2017, Chennai, 2017, pp. 561–5.
   Google Scholar
• X. Yang, Nature-inspired metaheuristic algorithms. 2010.
   Google Scholar
• V. Kumar, and D. Kumar, “A systematic review on firefly algorithm: past, present, and future,” Arch. Comput. Methods Eng., Vol. 28, no. 4, pp. 3269–91, Jun. 2021. DOI:10.1007/S11831-020-09498-Y/TABLES/5.
   Web of Science ®Google Scholar
• T. Demirdelen, “Kuru tip transformatör optimizasyonuna yeni Bir yaklaşım: ateş böceği algoritması,” Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Derg, Vol. 33, no. 1, pp. 87–96, Mar. 2018. DOI:10.21605/CUKUROVAUMMFD.420675.
   Google Scholar
• S. Tosun, A. Öztürk, H. Demir, and L. Kuru, “Weight optimization of dry type transformer with Tabu search algorithm method,” J. Adv. Technol. Sci., Vol. 1, no. 1, pp. 17–26, Mar. 2012.
   Google Scholar
• A. Ozturk, L. Kuru, S. Tosun, H. Demir, and E. Kuru, “Weight optimization of a core form oil transformer by using heuristic search algorithms,” J. Eng. Res. Appl. Sci., Vol. 1, no. 1, pp. 44–54, Jun. 2012.
   Google Scholar
• M. Celebi, “Weight optimization of a dry transformer by genetic algorithm and validation by finite element method,” Kahramanmaras Sutcu Imam Univ. J. Eng. Sci, Vol. 12, no. 2, pp. 30–36, 2019.
   Google Scholar
• L. Yang, X. Liu, and J. Yang, “A new compensation method for insulated core transformer power supply and its optimization using genetic algorithm,” Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., Vol. 960, Apr. 2020. DOI:10.1016/J.NIMA.2020.163585.
   Web of Science ®Google Scholar
• M. Zeraati, R. Arshadizadeh, N. P. S. Chauhan, and G. Sargazi, “Genetic algorithm optimization of magnetic properties of Fe-Co-Ni nanostructure alloys prepared by the mechanical alloying by using multi-objective artificial neural networks for the core of transformer,” Mater. Today Commun, Vol. 28. Sep. 2021. DOI:10.1016/J.MTCOMM.2021.102653.
   Web of Science ®Google Scholar
• I. O. Aksu, and T. Demirdelen, “A comprehensive study on dry type transformer design with swarm-based metaheuristic optimization methods for industrial applications,” Energy Sources, Part A Recover. Util. Environ. Eff., Vol. 40, no. 14, pp. 1743–52, Jul. 2018. DOI:10.1080/15567036.2018.1486908.
   Web of Science ®Google Scholar
• D. Azizian, M. Bigdeli, and J. Faiz, “Design optimization of cast-resin transformer using nature-inspired algorithms,” Arab. J. Sci. Eng., Vol. 41, no. 9, pp. 3491–500, 2016. DOI:10.1007/S13369-016-2066-X.
   Web of Science ®Google Scholar
• A. Abdo, H. Liu, H. Zhang, J. Guo, and Q. Li, “A new model of faults classification in power transformers based on data optimization method,” Electr. Power Syst. Res, Vol. 200, pp. 107446, Nov. 2021. DOI:10.1016/J.EPSR.2021.107446.
   Web of Science ®Google Scholar
• S. H. H. Kazmi, et al., “Cost optimized dynamic design of offshore windfarm transformers with reliability and contingency considerations,” Int. J. Electr. Power Energy Syst., Vol. 128, pp. 106684, Jun. 2021. DOI:10.1016/J.IJEPES.2020.106684.
   Web of Science ®Google Scholar
• D. T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, and M. Zaidi, “Bee algorithm a novel approach to function optimisation,” 2005.
   Google Scholar
• B. Akay, “Performance analysis of artificial bee colony algorithm on numerical optimization problems,” Ph.D. thesis, Erciyes University, Graduate School of Natural and Applied Sciences, 2009.
   Google Scholar
• M. Dorigo, and L. M. Gambardella, “Ant colonies for the travelling salesman problem,” BioSystems, Vol. 43, no. 2, pp. 73–81, Jul. 1997. DOI:10.1016/S0303-2647(97)01708-5.
   PubMed Web of Science ®Google Scholar
• W. Yong, “Hybrid max-min ant system with four vertices and three lines inequality for traveling salesman problem,” Soft. Comput., Vol. 19, no. 3, pp. 585–96, 2015. DOI:10.1007/s00500-014-1279-8.
   Web of Science ®Google Scholar
• P. Maheshwari, A. K. Sharma, and K. Verma, “Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization,” Ad. Hoc. Netw., Vol. 110, pp. 102317, Jan. 2021. DOI:10.1016/J.ADHOC.2020.102317.
   Web of Science ®Google Scholar
• F. Dahan, K. El Hindi, A. Ghoneim, and H. Alsalman, “An enhanced ant colony optimization based algorithm to solve QoS-aware web service composition,” IEEE. Access., Vol. 9, pp. 34098–111, 2021. DOI:10.1109/ACCESS.2021.3061738.
   Web of Science ®Google Scholar
• T. Boduroglu, Electric Machinery Notes. İstanbul: Istanbul Technical University, 1994.
   Google Scholar
• J. C. Olivares-Galvan, et al., “Core lamination selection for distribution transformers based on sensitivity analysis,” Electr. Eng, Vol. 95, no. 1, pp. 33–42, Feb. 2012. DOI:10.1007/S00202-012-0237-7.
   Web of Science ®Google Scholar
• A. Safigianni, and V. Sarris, “Economical estimation of the maintenance of distribution transformers,” in Proceedings of EighthIASTED International Conference on Power Energy Systems, Corfu, Greece, 2008, pp. 23–5.
   Google Scholar