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DOĞADAN ESİNLENEN YENİ BİR HİPOPOTAM OPTİMİZASYON ALGORİTMASI İLE OPTİMAL YÜK AKIŞI

Yıl 2025, Cilt: 28 Sayı: 4, 2000 - 2013, 03.12.2025

Hakan Işıker , Kadir Abacı

Öz

Bu çalışmada, optimal yük (güç) akışı (OYA) probleminin doğrusal olmayan ve dışbükey olmayan yapısını etkin biçimde çözmek için ilk kez Hipopotam Optimizasyon (HO) algoritması OYA’ya uyarlanmış ve çoklu değişkenlere özgü sınırlar için modifiye edilmiştir. Orijinal HO’nun tekil sınır yaklaşımı, geliştirilen sınır_kontrolü fonksiyonu ile her kontrol değişkenine özgü alt–üst limitleri gözetebilecek şekilde genişletilmiştir. Keşif, savunma ve sömürü fazlarının dengeli etkileşimi hızlı yakınsama ve yerel minimumlardan kaçış sağlamaktadır. Önerilen yöntem yakıt maliyeti ve aktif güç kaybını eşitlik ve eşitsizlik kısıtları altında optimize etmektedir. IEEE 14 baralı sistemde başlangıç yakıt maliyeti 842,34 $/h’den 828,1560 $/h’ye düşürülmüş; SKH, PSOGSA, MSG-HS, MPSO ve PSO-ANN algoritmalarına karşı 1,184–6,204 $/h aralığında ek iyileşme elde edilmiştir. Türkiye 22 baralı sisteminde de Gradient ve ABC tabanlı yöntemlere kıyasla hem maliyet hem aktif kayıp hedeflerinde üstün sonuçlar elde edilmiştir. HO'nun diğer modern algoritmalara kıyasla daha hızlı yakınsadığını ve daha kararlı sonuçlar ürettiğini ortaya koymaktadır. Bulgular, modifiye HO’nun OYA için etkin, sağlam ve ölçeklenebilir bir alternatif sunduğunu göstermektedir.

Anahtar Kelimeler

Optimal Güç Akışı Meta-Sezgisel Optimizasyon

Kaynakça

  • Abaci, K., Yamaçli, V., & Akdaʇli, A. (2016). Optimal power flow with SVC devices by using the artificial bee colony algorithm. Turkish Journal of Electrical Engineering and Computer Sciences, 24(1). https://doi.org/10.3906/elk-1305-55
  • Abido, M. A. (2002a). Optimal power flow using particle swarm optimization. International Journal of Electrical Power and Energy Systems, 24(7). https://doi.org/10.1016/S0142-0615(01)00067-9
  • Abido, M. A. (2002b). Optimal power flow using tabu search algorithm. Electric Power Components and Systems, 30(5). https://doi.org/10.1080/15325000252888425
  • Abou El Ela, A. A., Abido, M. A., & Spea, S. R. (2010). Optimal power flow using differential evolution algorithm. Electric Power Systems Research, 80(7). https://doi.org/10.1016/j.epsr.2009.12.018
  • Ahmadipour, M., Murtadha Othman, M., Bo, R., Sadegh Javadi, M., Mohammed Ridha, H., & Alrifaey, M. (2024). Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer. Expert Systems with Applications, 235, 121212. https://doi.org/https://doi.org/10.1016/j.eswa.2023.121212
  • Alsac, O., & Stott, B. (1974). Optimal load flow with steady-state security. IEEE Transactions on Power Apparatus and Systems, PAS-93(3). https://doi.org/10.1109/TPAS.1974.293972
  • Amiri, M. H., Mehrabi Hashjin, N., Montazeri, M., Mirjalili, S., & Khodadadi, N. (2024). Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-54910-3
  • Attia, A. F., El Sehiemy, R. A., & Hasanien, H. M. (2018). Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power and Energy Systems, 99. https://doi.org/10.1016/j.ijepes.2018.01.024
  • Bakirtzis, A. G., Biskas, P. N., Zoumas, C. E., & Petridis, V. (2002). Optimal power flow by enhanced genetic algorithm. IEEE Transactions on Power Systems, 17(2). https://doi.org/10.1109/TPWRS.2002.1007886
  • Bathina, V., Devarapalli, R., & García Márquez, F. P. (2023). Hybrid Approach with Combining Cuckoo-Search and Grey-Wolf Optimizer for Solving Optimal Power Flow Problems. Journal of Electrical Engineering and Technology, 18(3). https://doi.org/10.1007/s42835-022-01301-1
  • Ben Attous, D., & Labb, Y. (2010). Particle swarm optimisation based optimal power flow for units with non-smooth fuel cost functions. Modelling, Measurement and Control A, 83(3–4).
  • Bhattacharya, A., & Chattopadhyay, P. K. (2011). Application of biogeography-based optimisation to solve different optimal power flow problems. IET Generation, Transmission and Distribution, 5(1). https://doi.org/10.1049/iet-gtd.2010.0237
  • Biswas, P. P., Suganthan, P. N., Mallipeddi, R., & Amaratunga, G. A. J. (2018). Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques. Engineering Applications of Artificial Intelligence, 68. https://doi.org/10.1016/j.engappai.2017.10.019
  • Burchett, R. C., Happ, H. H., & Vierath, D. R. (1984). Quadratically Convergent Optimal Power Flow. IEEE Transactions on Power Apparatus and Systems, PAS-103(11), 3267–3275. https://doi.org/10.1109/TPAS.1984.318568
  • Butti, O. S. T. Al, Burunkaya, M., Rahebi, J., & Lopez-Guede, J. M. (2024). Optimal Power Flow Using PSO Algorithms Based on Artificial Neural Networks. IEEE Access, 12, 154778–154795. https://doi.org/10.1109/ACCESS.2024.3479097
  • Carpentier, J. (1962). Contribution a l’etude du dispaching economique. Bull. Soc. Francaise Electricians, 8, 431–447.
  • Dabbagchi, I., & Christie, R. (1993). 30 Bus Power Flow Test Case. University of Washington.
  • Dommel, H. W., & Tinney, W. F. (1968). Optimal Power Flow Solutions. IEEE Transactions on Power Apparatus and Systems, PAS-87(10), 1866–1876. https://doi.org/10.1109/TPAS.1968.292150
  • Duman, S., Yorukeren, N., & Altas, I. H. (2015). A novel modified hybrid PSOGSA based on fuzzy logic for non-convex economic dispatch problem with valve-point effect. International Journal of Electrical Power and Energy Systems, 64. https://doi.org/10.1016/j.ijepes.2014.07.031
  • El-Fergany, A. A., & Hasanien, H. M. (2015). Single and Multi-objective Optimal Power Flow Using Grey Wolf Optimizer and Differential Evolution Algorithms. Electric Power Components and Systems, 43(13). https://doi.org/10.1080/15325008.2015.1041625
  • Frank, S., & Rebennack, S. (2016). An introduction to optimal power flow: Theory, formulation, and examples. IIE Transactions (Institute of Industrial Engineers), 48(12). https://doi.org/10.1080/0740817X.2016.1189626
  • Guha, N., Wang, Z., Wytock, M., & Majumdar, A. (2019). Machine Learning for AC Optimal Power Flow.
  • Keswani, R., Verma, H. K., & Sharma, S. K. (2023). Multi-objective Optimal Power Flow Employing a Hybrid Sine Cosine–Grey Wolf Optimizer. Iranian Journal of Science and Technology - Transactions of Electrical Engineering. https://doi.org/10.1007/s40998-023-00631-8
  • Kurban, M., & Filik, Ü. (2007a). Türkiye’deki 22 Baralı 380 Kv’luk Güç Sisteminin İki Farklı Yöntem Kullanılarak Ekonomik Dağıtım Analizi. Sakarya University Journal of Science, 11(1), 78–86. https://doi.org/10.16984/saufbed.11036
  • Kurban, M., & Filik, Ü. B. (2007b). Türkiye’deki 22 Baralı 380 KV’luk Güç Sistemi İçin Ekonomik Dağıtım ve Optimal Güç Akışı Yöntemlerinin Karşılaştırmalı Analizi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 13(3), 369–378.
  • Lai, L. L., Ma, J. T., Yokoyama, R., & Zhao, M. (1997). Improved genetic algorithms for optimal power flow under both normal and contingent operation States. International Journal of Electrical Power and Energy Systems, 19(5). https://doi.org/10.1016/s0142-0615(96)00051-8
  • Lee, K. Y., Park, Y. M., & Ortiz, J. L. (1985). A United Approach to Optimal Real and Reactive Power Dispatch. IEEE Power Engineering Review, PER-5(5). https://doi.org/10.1109/MPER.1985.5526580
  • Li, C., Zhao, H., & Chen, T. (2010). The hybrid differential evolution algorithm for optimal power flow based on simulated annealing and tabu search. 2010 International Conference on Management and Service Science, MASS 2010. https://doi.org/10.1109/ICMSS.2010.5578512
  • Li, S., Gong, W., Hu, C., Yan, X., Wang, L., & Gu, Q. (2021). Adaptive constraint differential evolution for optimal power flow. Energy, 235. https://doi.org/10.1016/j.energy.2021.121362
  • M. Shaheen, A., El-Sehiemy, R. A., Hasanien, H. M., & Ginidi, A. (2024). An enhanced optimizer of social network search for multi-dimension optimal power flow in electrical power grids. International Journal of Electrical Power & Energy Systems, 155, 109572. https://doi.org/https://doi.org/10.1016/j.ijepes.2023.109572
  • Mittal, U., Nangia, U., Jain, N. K., & Gupta, S. (2025). Optimal power flow solutions for normal and critical loading scenarios using hybrid Rao-2 sine cosine algorithm. Computers and Electrical Engineering, 123, 110230. https://doi.org/https://doi.org/10.1016/j.compeleceng.2025.110230
  • Mohamed, A. A. A., Mohamed, Y. S., El-Gaafary, A. A. M., & Hemeida, A. M. (2017). Optimal power flow using moth swarm algorithm. Electric Power Systems Research, 142. https://doi.org/10.1016/j.epsr.2016.09.025
  • Niknam, T., Narimani, M. R., Aghaei, J., & Azizipanah-Abarghooee, R. (2012). Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index. IET Generation, Transmission and Distribution, 6(6). https://doi.org/10.1049/iet-gtd.2011.0851
  • Pulluri, H., Naresh, R., & Sharma, V. (2018). A solution network based on stud krill herd algorithm for optimal power flow problems. Soft Computing, 22(1). https://doi.org/10.1007/s00500-016-2319-3
  • Radosavljević, J., Klimenta, D., Jevtić, M., & Arsić, N. (2015). Optimal Power Flow Using a Hybrid Optimization Algorithm of Particle Swarm Optimization and Gravitational Search Algorithm. Electric Power Components and Systems, 43(17). https://doi.org/10.1080/15325008.2015.1061620
  • Rahman, J., Feng, C., & Zhang, J. (2021). A learning-augmented approach for AC optimal power flow. International Journal of Electrical Power and Energy Systems, 130. https://doi.org/10.1016/j.ijepes.2021.106908
  • Reddy, S. S. (2019). Optimal power flow using hybrid differential evolution and harmony search algorithm. International Journal of Machine Learning and Cybernetics, 10(5). https://doi.org/10.1007/s13042-018-0786-9
  • Rezaei Adaryani, M., & Karami, A. (2013). Artificial bee colony algorithm for solving multi-objective optimal power flow problem. International Journal of Electrical Power and Energy Systems, 53(1). https://doi.org/10.1016/j.ijepes.2013.04.021
  • Roa-Sepulveda, C. A., & Pavez-Lazo, B. J. (2003). A solution to the optimal power flow using simulated annealing. International Journal of Electrical Power and Energy Systems, 25(1). https://doi.org/10.1016/S0142-0615(02)00020-0
  • Roy, P. K., & Mandal, D. (2011). Quasi-oppositional biogeography-based optimization for multi-objective optimal power flow. Electric Power Components and Systems, 40(2). https://doi.org/10.1080/15325008.2011.629337
  • Saini, A., Chaturvedi, D. K., & Saxena, A. K. (2006). Optimal power flow solution: A GA-fuzzy system approach. International Journal of Emerging Electric Power Systems, 5(2). https://doi.org/10.2202/1553-779X.1091
  • Santos, A. J., & Da Costa, G. R. M. (1995). Optimal-power-flow solution by Newton’s method applied to an augmented Lagrangian function. IEE Proceedings-Generation, Transmission and Distribution, 142(1), 33–36.
  • Sayah, S., & Zehar, K. (2008). Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Conversion and Management, 49(11). https://doi.org/10.1016/j.enconman.2008.06.014
  • Sivasubramani, S., & Swarup, K. S. (2011). Multi-objective harmony search algorithm for optimal power flow problem. International Journal of Electrical Power and Energy Systems, 33(3). https://doi.org/10.1016/j.ijepes.2010.12.031
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OPTIMAL POWER FLOW USING A NEW HIPPOPOTAMUS OPTIMIZATION ALGORITHM INSPIRED BY NATURE

Yıl 2025, Cilt: 28 Sayı: 4, 2000 - 2013, 03.12.2025

Hakan Işıker , Kadir Abacı

Öz

This study presents the first adaptation of the Hippopotamus Optimization (HO) algorithm to the optimal power flow (OPF) problem in order to effectively address its nonlinear and non-convex structure, and modifies it to handle variable-specific heterogeneous bounds. The original single-bound approach of HO is extended through a customized boundary_control function so that each control variable can respect its own lower and upper limits. The balanced interaction of the exploration, defense, and exploitation phases enables fast convergence and avoidance of local minima. The proposed method optimizes fuel cost and active power loss under equality and inequality constraints. On the IEEE 14-bus system, the initial fuel cost is reduced from 842,34 $/h to 828,1560 $/h, achieving an additional improvement of 1,184–6,204 $/h over SKH, PSOGSA, MSG-HS, MPSO, and PSO-ANN. On the Turkish 22-bus system, it also attains superior results for both cost and active loss targets compared with Gradient-based and ABC-based methods. This demonstrates that HO converges more rapidly and produces more stable results in comparison to other contemporary algorithms. The findings demonstrate that the modified HO is an effective, robust, and scalable alternative for OPF.

Anahtar Kelimeler

Optimal Power Flow Metaheuristic Optimization

Kaynakça

  • Abaci, K., Yamaçli, V., & Akdaʇli, A. (2016). Optimal power flow with SVC devices by using the artificial bee colony algorithm. Turkish Journal of Electrical Engineering and Computer Sciences, 24(1). https://doi.org/10.3906/elk-1305-55
  • Abido, M. A. (2002a). Optimal power flow using particle swarm optimization. International Journal of Electrical Power and Energy Systems, 24(7). https://doi.org/10.1016/S0142-0615(01)00067-9
  • Abido, M. A. (2002b). Optimal power flow using tabu search algorithm. Electric Power Components and Systems, 30(5). https://doi.org/10.1080/15325000252888425
  • Abou El Ela, A. A., Abido, M. A., & Spea, S. R. (2010). Optimal power flow using differential evolution algorithm. Electric Power Systems Research, 80(7). https://doi.org/10.1016/j.epsr.2009.12.018
  • Ahmadipour, M., Murtadha Othman, M., Bo, R., Sadegh Javadi, M., Mohammed Ridha, H., & Alrifaey, M. (2024). Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer. Expert Systems with Applications, 235, 121212. https://doi.org/https://doi.org/10.1016/j.eswa.2023.121212
  • Alsac, O., & Stott, B. (1974). Optimal load flow with steady-state security. IEEE Transactions on Power Apparatus and Systems, PAS-93(3). https://doi.org/10.1109/TPAS.1974.293972
  • Amiri, M. H., Mehrabi Hashjin, N., Montazeri, M., Mirjalili, S., & Khodadadi, N. (2024). Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-54910-3
  • Attia, A. F., El Sehiemy, R. A., & Hasanien, H. M. (2018). Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power and Energy Systems, 99. https://doi.org/10.1016/j.ijepes.2018.01.024
  • Bakirtzis, A. G., Biskas, P. N., Zoumas, C. E., & Petridis, V. (2002). Optimal power flow by enhanced genetic algorithm. IEEE Transactions on Power Systems, 17(2). https://doi.org/10.1109/TPWRS.2002.1007886
  • Bathina, V., Devarapalli, R., & García Márquez, F. P. (2023). Hybrid Approach with Combining Cuckoo-Search and Grey-Wolf Optimizer for Solving Optimal Power Flow Problems. Journal of Electrical Engineering and Technology, 18(3). https://doi.org/10.1007/s42835-022-01301-1
  • Ben Attous, D., & Labb, Y. (2010). Particle swarm optimisation based optimal power flow for units with non-smooth fuel cost functions. Modelling, Measurement and Control A, 83(3–4).
  • Bhattacharya, A., & Chattopadhyay, P. K. (2011). Application of biogeography-based optimisation to solve different optimal power flow problems. IET Generation, Transmission and Distribution, 5(1). https://doi.org/10.1049/iet-gtd.2010.0237
  • Biswas, P. P., Suganthan, P. N., Mallipeddi, R., & Amaratunga, G. A. J. (2018). Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques. Engineering Applications of Artificial Intelligence, 68. https://doi.org/10.1016/j.engappai.2017.10.019
  • Burchett, R. C., Happ, H. H., & Vierath, D. R. (1984). Quadratically Convergent Optimal Power Flow. IEEE Transactions on Power Apparatus and Systems, PAS-103(11), 3267–3275. https://doi.org/10.1109/TPAS.1984.318568
  • Butti, O. S. T. Al, Burunkaya, M., Rahebi, J., & Lopez-Guede, J. M. (2024). Optimal Power Flow Using PSO Algorithms Based on Artificial Neural Networks. IEEE Access, 12, 154778–154795. https://doi.org/10.1109/ACCESS.2024.3479097
  • Carpentier, J. (1962). Contribution a l’etude du dispaching economique. Bull. Soc. Francaise Electricians, 8, 431–447.
  • Dabbagchi, I., & Christie, R. (1993). 30 Bus Power Flow Test Case. University of Washington.
  • Dommel, H. W., & Tinney, W. F. (1968). Optimal Power Flow Solutions. IEEE Transactions on Power Apparatus and Systems, PAS-87(10), 1866–1876. https://doi.org/10.1109/TPAS.1968.292150
  • Duman, S., Yorukeren, N., & Altas, I. H. (2015). A novel modified hybrid PSOGSA based on fuzzy logic for non-convex economic dispatch problem with valve-point effect. International Journal of Electrical Power and Energy Systems, 64. https://doi.org/10.1016/j.ijepes.2014.07.031
  • El-Fergany, A. A., & Hasanien, H. M. (2015). Single and Multi-objective Optimal Power Flow Using Grey Wolf Optimizer and Differential Evolution Algorithms. Electric Power Components and Systems, 43(13). https://doi.org/10.1080/15325008.2015.1041625
  • Frank, S., & Rebennack, S. (2016). An introduction to optimal power flow: Theory, formulation, and examples. IIE Transactions (Institute of Industrial Engineers), 48(12). https://doi.org/10.1080/0740817X.2016.1189626
  • Guha, N., Wang, Z., Wytock, M., & Majumdar, A. (2019). Machine Learning for AC Optimal Power Flow.
  • Keswani, R., Verma, H. K., & Sharma, S. K. (2023). Multi-objective Optimal Power Flow Employing a Hybrid Sine Cosine–Grey Wolf Optimizer. Iranian Journal of Science and Technology - Transactions of Electrical Engineering. https://doi.org/10.1007/s40998-023-00631-8
  • Kurban, M., & Filik, Ü. (2007a). Türkiye’deki 22 Baralı 380 Kv’luk Güç Sisteminin İki Farklı Yöntem Kullanılarak Ekonomik Dağıtım Analizi. Sakarya University Journal of Science, 11(1), 78–86. https://doi.org/10.16984/saufbed.11036
  • Kurban, M., & Filik, Ü. B. (2007b). Türkiye’deki 22 Baralı 380 KV’luk Güç Sistemi İçin Ekonomik Dağıtım ve Optimal Güç Akışı Yöntemlerinin Karşılaştırmalı Analizi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 13(3), 369–378.
  • Lai, L. L., Ma, J. T., Yokoyama, R., & Zhao, M. (1997). Improved genetic algorithms for optimal power flow under both normal and contingent operation States. International Journal of Electrical Power and Energy Systems, 19(5). https://doi.org/10.1016/s0142-0615(96)00051-8
  • Lee, K. Y., Park, Y. M., & Ortiz, J. L. (1985). A United Approach to Optimal Real and Reactive Power Dispatch. IEEE Power Engineering Review, PER-5(5). https://doi.org/10.1109/MPER.1985.5526580
  • Li, C., Zhao, H., & Chen, T. (2010). The hybrid differential evolution algorithm for optimal power flow based on simulated annealing and tabu search. 2010 International Conference on Management and Service Science, MASS 2010. https://doi.org/10.1109/ICMSS.2010.5578512
  • Li, S., Gong, W., Hu, C., Yan, X., Wang, L., & Gu, Q. (2021). Adaptive constraint differential evolution for optimal power flow. Energy, 235. https://doi.org/10.1016/j.energy.2021.121362
  • M. Shaheen, A., El-Sehiemy, R. A., Hasanien, H. M., & Ginidi, A. (2024). An enhanced optimizer of social network search for multi-dimension optimal power flow in electrical power grids. International Journal of Electrical Power & Energy Systems, 155, 109572. https://doi.org/https://doi.org/10.1016/j.ijepes.2023.109572
  • Mittal, U., Nangia, U., Jain, N. K., & Gupta, S. (2025). Optimal power flow solutions for normal and critical loading scenarios using hybrid Rao-2 sine cosine algorithm. Computers and Electrical Engineering, 123, 110230. https://doi.org/https://doi.org/10.1016/j.compeleceng.2025.110230
  • Mohamed, A. A. A., Mohamed, Y. S., El-Gaafary, A. A. M., & Hemeida, A. M. (2017). Optimal power flow using moth swarm algorithm. Electric Power Systems Research, 142. https://doi.org/10.1016/j.epsr.2016.09.025
  • Niknam, T., Narimani, M. R., Aghaei, J., & Azizipanah-Abarghooee, R. (2012). Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index. IET Generation, Transmission and Distribution, 6(6). https://doi.org/10.1049/iet-gtd.2011.0851
  • Pulluri, H., Naresh, R., & Sharma, V. (2018). A solution network based on stud krill herd algorithm for optimal power flow problems. Soft Computing, 22(1). https://doi.org/10.1007/s00500-016-2319-3
  • Radosavljević, J., Klimenta, D., Jevtić, M., & Arsić, N. (2015). Optimal Power Flow Using a Hybrid Optimization Algorithm of Particle Swarm Optimization and Gravitational Search Algorithm. Electric Power Components and Systems, 43(17). https://doi.org/10.1080/15325008.2015.1061620
  • Rahman, J., Feng, C., & Zhang, J. (2021). A learning-augmented approach for AC optimal power flow. International Journal of Electrical Power and Energy Systems, 130. https://doi.org/10.1016/j.ijepes.2021.106908
  • Reddy, S. S. (2019). Optimal power flow using hybrid differential evolution and harmony search algorithm. International Journal of Machine Learning and Cybernetics, 10(5). https://doi.org/10.1007/s13042-018-0786-9
  • Rezaei Adaryani, M., & Karami, A. (2013). Artificial bee colony algorithm for solving multi-objective optimal power flow problem. International Journal of Electrical Power and Energy Systems, 53(1). https://doi.org/10.1016/j.ijepes.2013.04.021
  • Roa-Sepulveda, C. A., & Pavez-Lazo, B. J. (2003). A solution to the optimal power flow using simulated annealing. International Journal of Electrical Power and Energy Systems, 25(1). https://doi.org/10.1016/S0142-0615(02)00020-0
  • Roy, P. K., & Mandal, D. (2011). Quasi-oppositional biogeography-based optimization for multi-objective optimal power flow. Electric Power Components and Systems, 40(2). https://doi.org/10.1080/15325008.2011.629337
  • Saini, A., Chaturvedi, D. K., & Saxena, A. K. (2006). Optimal power flow solution: A GA-fuzzy system approach. International Journal of Emerging Electric Power Systems, 5(2). https://doi.org/10.2202/1553-779X.1091
  • Santos, A. J., & Da Costa, G. R. M. (1995). Optimal-power-flow solution by Newton’s method applied to an augmented Lagrangian function. IEE Proceedings-Generation, Transmission and Distribution, 142(1), 33–36.
  • Sayah, S., & Zehar, K. (2008). Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Conversion and Management, 49(11). https://doi.org/10.1016/j.enconman.2008.06.014
  • Sivasubramani, S., & Swarup, K. S. (2011). Multi-objective harmony search algorithm for optimal power flow problem. International Journal of Electrical Power and Energy Systems, 33(3). https://doi.org/10.1016/j.ijepes.2010.12.031
  • Su, H., Niu, Q., & Yang, Z. (2023). Optimal Power Flow Using Improved Cross-Entropy Method. Energies, 16(14). https://doi.org/10.3390/en16145466
  • Sun, D. I., Ashley, B., Brewer, B., Hughes, A., & Tinney, W. F. (1984). Optimal Power Flow By Newton Approach. IEEE Transactions on Power Apparatus and Systems, PAS-103(10), 2864–2880. https://doi.org/10.1109/TPAS.1984.318284
  • Thitithamrongchai, C., & Eua-arporn, B. (2007). Self-adaptive Differential Evolution Based Optimal Power Flow for Units with Non-smooth Fuel Cost Functions. Journal of Electrical Systems, 3(2).
  • Turkay, B. E., & Cabadag, R. I. (2013). Optimal power flow solution using particle swarm optimization algorithm. IEEE EuroCon 2013. https://doi.org/10.1109/EUROCON.2013.6625164
  • Walters, D. C., & Sheble, G. B. (1993). Genetic algorithm solution of economic dispatch with valve point loading. IEEE Transactions on Power Systems, 8(3). https://doi.org/10.1109/59.260861
  • Yaşar, C., & Özyön, S. (2011). A new hybrid approach for nonconvex economic dispatch problem with valve-point effect. Energy, 36(10). https://doi.org/10.1016/j.energy.2011.08.041
  • Yigit, E., & Duysak, H. (2019). Determination of Optimal Layer Sequence and Thickness for Broadband Multilayer Absorber Design Using Double-Stage Artificial Bee Colony Algorithm. IEEE Transactions on Microwave Theory and Techniques, 67(8). https://doi.org/10.1109/TMTT.2019.2919574
  • Yigit, E., & Duysak, H. (2021). Fully optimized multilayer radar absorber design using multi-objective abc algorithm. International Journal of Engineering and Geosciences, 6(3). https://doi.org/10.26833/ijeg.743661
Toplam 52 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Elektrik Enerjisi Taşıma, Şebeke ve Sistemleri, Elektrik Tesisleri
Bölüm Araştırma Makalesi
Yazarlar

Hakan Işıker 0000-0002-6465-8480

Kadir Abacı 0000-0001-5627-0032

Yayımlanma Tarihi 3 Aralık 2025
Gönderilme Tarihi 22 Ağustos 2025
Kabul Tarihi 27 Ekim 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 28 Sayı: 4

Kaynak Göster

APA Işıker, H., & Abacı, K. (2025). DOĞADAN ESİNLENEN YENİ BİR HİPOPOTAM OPTİMİZASYON ALGORİTMASI İLE OPTİMAL YÜK AKIŞI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(4), 2000-2013.
 Kapak Resmi İndir