A Developed Honey Badger Optimization Algorithm for Tackling Optimal Power Flow Problem

References

• T. Niknam, N. M. Rasoul, M. Jabbari, and A. R. Malekpour, “A modified shuffle frog leaping algorithm for multi-objective optimal power flow,” Energy, vol. 36, no. 11, pp. 6420–6432, 2011. DOI: 10.1016/j.energy.2011.09.027.
   Web of Science ®Google Scholar
• F. A. Hashim, E. H. Houssein, K. Hussain, M. S. Mabrouk, and W. Al-Atabany, “Honey badger algorithm: new metaheuristic algorithm for solving optimization problems,” Math. Comput. Simul., vol. 192, pp. 84–110, 2022. DOI: 10.1016/j.matcom.2021.08.013.
   Web of Science ®Google Scholar
• H. Bouchekara, “Optimal power flow using black-hole-based optimization approach,” Appl. Soft Comput., vol. 24, pp. 879–888, 2014. DOI: 10.1016/j.asoc.2014.08.056.
   Web of Science ®Google Scholar
• H. Bouchekara, “Electrostatic discharge algorithm: a novel nature-inspired optimization algorithm and its application to worst-case tolerance analysis of an EMC filter,” IET Sci. Meas. Technol., vol. 13, no. 4, pp. 491–499, 2019. DOI: 10.1049/iet-smt.2018.5194.
   Web of Science ®Google Scholar
• H. Bouchekara, “Electric charged particles optimization and its application to the optimal design of a circular antenna array,” Artif. Intell. Rev., vol. 54, no. 3, pp. 1767–1802, 2021. DOI: 10.1007/s10462-020-09890-x.
   Web of Science ®Google Scholar
• F. A. Hashim, K. Hussain, E. H. Houssein, M. S. Mabrouk, and W. Al-Atabany, “Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems,” Appl. Intell., pp. 1–21, 2021.
   PubMed Web of Science ®Google Scholar
• J. Carpentier, “Contribution a l’etude du dispatching economies,” Bull. Soc. Franc. Electric., vol. 3, no. 1, pp. 431–447, 1962.
   Google Scholar
• R. Mota-Palomino and V. H. Quintana, “Sparse reactive power scheduling by a penalty function-linear programming technique,” IEEE Trans. Power Syst., vol. 1, no. 3, pp. 31–39, 1986. DOI: 10.1109/TPWRS.1986.4334951.
   Web of Science ®Google Scholar
• J. A. Momoh, M. E. El-Hawary, and R. Adapa, “A review of selected optimal power flow literature to 1993. II. Newton, linear programming and interior point methods,” IEEE Trans. Power Syst., vol. 14, no. 1, pp. 105–111, 1999. DOI: 10.1109/59.744495.
   Web of Science ®Google Scholar
• H. Wei, H. Sasaki, J. Kubokawa, and R. Yokoyama, “An interior point nonlinear programming for optimal power flow problems with a novel data structure,” IEEE Trans. Power Syst., vol. 13, no. 3, pp. 870–877, 1998. DOI: 10.1109/59.708745.
   Web of Science ®Google Scholar
• R. C. Burchett, H. H. Happ, and D. R. Vierath, “Quadratically convergent optimal power flow,” IEEE Trans. Power Appl. Syst., vol. PAS-103, no. 11, pp. 3267–3275, 1984. DOI: 10.1109/TPAS.1984.318568.
   Web of Science ®Google Scholar
• M. R. Adaryani and A. Karami, “Artificial bee colony algorithm for solving multi-objective optimal power flow problem,” Int. J. Electr. Power Energy Syst., vol. 53, pp. 219–230, 2013. DOI: 10.1016/j.ijepes.2013.04.021.
   Web of Science ®Google Scholar
• M. A. Shaheen, H. M. Hasanien, S. F. Mekhamer, and H. E. Talaat, “Optimal power flow of power systems including distributed generation units using sunflower optimization algorithm,” IEEE Access, vol. 7, pp. 109289–109300, 2019. DOI: 10.1109/ACCESS.2019.2933489.
   Web of Science ®Google Scholar
• N. Daryani, M. T. Hagh, and S. Teimourzadeh, “Adaptive group search optimization algorithm for multi-objective optimal power flow problem,” Appl. Soft Comput., vol. 38, pp. 1012–1024, 2016. DOI: 10.1016/j.asoc.2015.10.057.
   Web of Science ®Google Scholar
• A. A. A. Mohamed, Y. S. Mohamed, A. A. El-Gaafary, and A. M. Hemeida, “Optimal power flow using moth swarm algorithm,” Electr. Power Syst. Res., vol. 142, pp. 190–206, 2017. DOI: 10.1016/j.epsr.2016.09.025.
   Web of Science ®Google Scholar
• M. A. Abido, “Optimal power flow using tabu search algorithm,” Electr. Power Compon. Syst., vol. 30, no. 5, pp. 469–483, 2002. DOI: 10.1080/15325000252888425.
   Web of Science ®Google Scholar
• S. Abd el-sattar, S. Kamel, M. Ebeed, and F. Jurado, “An improved version of salp swarm algorithm for solving optimal power flow problem,” Soft Comput., vol. 25, no. 5, pp. 4027–4052, 2021. DOI: 10.1007/s00500-020-05431-4.
   Web of Science ®Google Scholar
• O. Akdag, A. Ates, and C. Yeroglu, “Modification of Harris hawks optimization algorithm with random distribution functions for optimum power flow problem,” Neural Comput. Appl., vol. 33, no. 6, pp. 1959–1985, 2021. DOI: 10.1007/s00521-020-05073-5.
   Web of Science ®Google Scholar
• G. Deb, K. Chakraborty, and S. Deb, “Modified spider monkey optimization-based optimal placement of distributed generators in radial distribution system for voltage security improvement,” Electr. Power Compon. Syst., vol. 48, no. 9–10, pp. 1006–1020, 2020. DOI: 10.1080/15325008.2020.1829186.
   Web of Science ®Google Scholar
• H. Ebrahimi, M. Abapour, B. Mohammadi‐Ivatloo, S. Golshannavaz, and A. Yazdaninejadi, “Decentralized approach for security enhancement of wind‐integrated energy systems coordinated with energy storages,” Int. J. Energy Res., vol. 46, no. 4, pp. 5006–5027, 2022. DOI: 10.1002/er.7494.
   Web of Science ®Google Scholar
• H. Ebrahimi, A. Yazdaninejadi, and S. Golshannavaz, “Demand response programs in power systems with energy storage system-coordinated wind energy sources: a security-constrained problem,” J. Clean. Prod., vol. 335, pp. 130342, 2022. DOI: 10.1016/j.jclepro.2021.130342.
   Web of Science ®Google Scholar
• S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Adv. Eng. Softw., vol. 95, pp. 51–67, 2016. DOI: 10.1016/j.advengsoft.2016.01.008.
   Web of Science ®Google Scholar
• W. Zhao, Z. Zhang, and L. Wang, “Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications,” Eng. Appl. Artif. Intell., vol. 87, pp. 103300, 2020. DOI: 10.1016/j.engappai.2019.103300.
   Web of Science ®Google Scholar
• H. T. Kahraman et al., “Dynamic FDB selection method and its application: modeling and optimizing of directional overcurrent relays coordination,” Appl. Intell., vol. 52, no. 5, pp. 4873–4908, 2022. DOI: 10.1007/s10489-021-02629-3.
   Web of Science ®Google Scholar
• IEEE-30 Bus, 2020. https://tr.scribd.com/doc/282453109/IEEE-30-Bus-System-Data.
   Google Scholar
• E. E. Elattar and S. K. ElSayed, “Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement,” Energy, vol. 178, pp. 598–609, 2019. [ Mismatch] DOI: 10.1016/j.energy.2019.04.159.
   Web of Science ®Google Scholar
• O. Akdag, “Improved Archimedes optimization algorithm for multi/single-objective optimal power flow,” Electr. Power Syst. Res., vol. 206, pp. 107796, 2022. DOI: 10.1016/j.epsr.2022.107796.
   Web of Science ®Google Scholar
• M. F. Kotb and A. A. El-Fergany, “Optimal power flow solution using moth swarm optimizer considering generating units prohibited zones and valve ripples,” J. Electr. Eng. Technol., vol. 15, pp. 179–192, 2020.
   Web of Science ®Google Scholar
• W. Bai, I. Eke, and K. K. Lee, “An improved artificial bee colony optimization algorithm based on orthogonal learning for OPF,” Control Eng. Pract., vol. 61, pp. 163–172, 2017. DOI: 10.1016/j.conengprac.2017.02.010.
   Web of Science ®Google Scholar
• H. Pulluri, R. Naresh, and V. Sharma, “An enhanced self-adaptive differential evolution based solution methodology for multi-objective optimal power flow,” Appl. Soft Comput., vol. 54, pp. 229–245, 2017. DOI: 10.1016/j.asoc.2017.01.030.
   Web of Science ®Google Scholar
• S. Sivasubramani and K. S. Swarup, “Multi-objective harmony search algorithm for optimal power flow problem,” Int. J. Electr. Power Energy Syst., vol. 33, no. 3, pp. 745–752, 2011. DOI: 10.1016/j.ijepes.2010.12.031.
   Web of Science ®Google Scholar
• A. F. Attia, R. A. El Sehiemy, and H. M. Hasanien, “Optimal power flow solution in power systems using a novel sine-cosine algorithm,” Int. J. Electr. Power Energy Syst., vol. 99, pp. 331–343, 2018. DOI: 10.1016/j.ijepes.2018.01.024.
   Web of Science ®Google Scholar
• A. R. Kumar and L. Premalatha, “Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 393–399, 2015. DOI: 10.1016/j.ijepes.2015.05.011.
   Web of Science ®Google Scholar
• H. Bouchekara, M. A. Abido, and M. Boucherma, “Optimal power flow using teaching-learning-based optimization technique,” Electr. Power Syst. Res., vol. 114, pp. 49–59, 2014. DOI: 10.1016/j.epsr.2014.03.032.
   Web of Science ®Google Scholar
• H. Bouchekara, A. E. Chaib, M. A. Abido, and R. A. El-Sehiemy, “Optimal power flow using an improved colliding bodies optimization algorithm,” Appl. Soft Comput., vol. 42, pp. 119–131, 2016. DOI: 10.1016/j.asoc.2016.01.041.
   Web of Science ®Google Scholar
• M. A. Taher, S. Kamel, F. Jurado, and M. Ebeed, “An improved moth‐flame optimization algorithm for solving optimal power flow problem,” Int. Trans. Electr. Energy Syst., vol. 29, no. 3, pp. e2743, 2019. DOI: 10.1002/etep.2743.
   Web of Science ®Google Scholar
• R. P. Singh, V. Mukherjee, and S. P. Ghoshal, “Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem,” Appl. Soft Comput., vol. 40, pp. 161–177, 2016. DOI: 10.1016/j.asoc.2015.11.027.
   Web of Science ®Google Scholar
• M. A. Taher, S. Kamel, F. Jurado, and M. Ebeed, “Modified grasshopper optimization framework for optimal power flow solution,” Electr. Eng., vol. 101, no. 1, pp. 121–148, 2019. DOI: 10.1007/s00202-019-00762-4.
   Web of Science ®Google Scholar
• A. M. El-Fergany and H. M. Hasanien, “Single and multi-objective optimal power flow using grey wolf optimizer and differential evolution algorithms,” Electr. Power Compon. Syst., vol. 43, no. 13, pp. 1548–1559, 2015. DOI: 10.1080/15325008.2015.1041625.
   Web of Science ®Google Scholar
• A. E. Chaib, H. Bouchekara, R. Mehasni, and M. A. Abido, “Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm,” Int. J. Electr. Power Energy Syst., vol. 81, pp. 64–77, 2016. DOI: 10.1016/j.ijepes.2016.02.004.
   Web of Science ®Google Scholar
• M. Ghasemi, S. Ghavidel, E. Akbari, and A. A. Vahed, “Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos,” Energy, vol. 73, pp. 340–353, 2014. DOI: 10.1016/j.energy.2014.06.026.
   Web of Science ®Google Scholar
• S. Gupta et al., “Robust optimization approach for optimal power flow solutions using Rao algorithms,” Energies, vol. 14, no. 17, pp. 5449, 2021. DOI: 10.3390/en14175449.
   Web of Science ®Google Scholar
• T. L. Duong and T. T. Guyen, “Application of sunflower optimization algorithm for solving the security constrained optimal power flow problem,” Eng. Technol. Appl. Sci. Res., vol. 10, no. 3, pp. 5700–5705, 2020. DOI: 10.48084/etasr.3511.
   Web of Science ®Google Scholar
• V. Raviprabakaran and R. C. Subramanian, “Enhanced ant colony optimization to solve the optimal power flow with ecological emission,” Int. J. Syst. Assur. Eng. Manag., vol. 9, no. 1, pp. 58–65, 2018. DOI: 10.1007/s13198-016-0471-x.
   Web of Science ®Google Scholar
• H. Bouchekara, M. Abido, and A. E. Chaib, “Optimal power flow using an improved electromagnetism-like mechanism method,” Electr. Power Compon. Syst., vol. 44, no. 4, pp. 434–449, 2016. DOI: 10.1080/15325008.2015.1115919.
   Web of Science ®Google Scholar
• M. Abdo, S. Kamel, M. Ebeed, J. Yu, and F. Jurado, “Solving non-smooth optimal power flow problems using a developed grey wolf optimizer,” Energies, vol. 11, no. 7, pp. 1692, 2018. DOI: 10.3390/en11071692.
   Web of Science ®Google Scholar
• S. Khunkitti, A. Siritaratiwat, S. Premrudeepreechacharn, R. Chatthaworn, and N. R. Watson, “A hybrid DA-PSO optimization algorithm for multiobjective optimal power flow problems,” Energies, vol. 11, no. 9, pp. 2270, 2018. DOI: 10.3390/en11092270.
   Web of Science ®Google Scholar
• S. S. Reddy, P. R. Bijwe, and A. R. “Abhyankar, “Faster evolutionary algorithm based optimal power flow using incremental variables,” Int. J. Electr. Power Energy Syst., vol. 19, pp. 8210–8254, 2014.
   Google Scholar
• X. He, W. Wang, J. Jiang, and L. Xu, “An improved artificial bee colony algorithm and its application to multi-objective optimal power flow,” Energies, vol. 8, no. 4, pp. 2412–2437, 2015. DOI: 10.3390/en8042412.
   Web of Science ®Google Scholar
• T. Niknam, M. R. Narimani, J. Aghaei, and R. Azizipanah-Abarghooee, “Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index,” IET Gener. Transm. Distrib., vol. 6, no. 6, pp. 515–527, 2012. DOI: 10.1049/iet-gtd.2011.0851.
   Web of Science ®Google Scholar
• J. Polprasert, W. Ongsakul, and V. N. Dieu, “Optimal reactive power dispatch using improved pseudo-gradient search particle swarm optimization,” Electr. Power Compon. Syst., vol. 44, no. 5, pp. 518–532, 2016. DOI: 10.1080/15325008.2015.1112449.
   Web of Science ®Google Scholar