Fitness-distance balance based artificial ecosystem optimisation to solve transient stability constrained optimal power flow problem

References

• Abbasi, M., Abbasi, E., & Mohammadi-Ivatloo, B. (2021). Single and multi-objective optimal power flow using a new differential-based harmony search algorithm. Journal of Ambient Intelligence and Humanized Computing, 12(1), 851–871. https://doi.org/10.1007/s12652-020-02089-6
   Web of Science ®Google Scholar
• Abido, M. A. (2002). Optimal power flow using tabu search algorithm. Electric Power Components and Systems, 30(5), 469–483. https://doi.org/10.1080/15325000252888425
   Web of Science ®Google Scholar
• Adaryani, M. R., & Karami, A. (2013). Artificial bee colony algorithm for solving multi-objective optimal power flow problem. International Journal of Electrical Power & Energy Systems, 53, 219–230. https://doi.org/10.1016/j.ijepes.2013.04.021
   Web of Science ®Google Scholar
• Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2020). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 53(3), 2237–2264. https://doi.org/10.1007/s10462-019-09732-5
   Web of Science ®Google Scholar
• Anand, P., & Arora, S. (2020). A novel chaotic selfish herd optimizer for global optimization and feature selection. Artificial Intelligence Review, 53(2), 1441–1486. https://doi.org/10.1007/s10462-019-09707-6
   Web of Science ®Google Scholar
• Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: A novel approach for global optimization. Soft Computing, 23(3), 715–734. https://doi.org/10.1007/s00500-018-3102-4
   Web of Science ®Google Scholar
• Awad, N. H., Ali, M. Z., Liang, J. J., Qu, B. Y., & Suganthan, P. N. (2017, June). Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems. In 2017 IEEE Congress on Evolutionary Computation (CEC) pp. 372–379. https://doi.org/10.1109/CEC.2017.7969336
   Google Scholar
• Ayan, K., & Kilic, U. (2013). Solution of transient stability-constrained optimal power flow using artificial bee colony algorithm. Turkish Journal of Electrical Engineering & Computer Sciences, 21(2), 360–372. https://doi.org/10.3906/elk-1112-14
   Web of Science ®Google Scholar
• Ayan, K., Kılıç, U., & Baraklı, B. (2015). Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow. International Journal of Electrical Power & Energy Systems, 64, 136–147. https://doi.org/10.1016/j.ijepes.2014.07.018
   Web of Science ®Google Scholar
• Biswas, S., Saha, D., De, S., Cobb, A. D., Das, S., & Jalaian, B. A. (2021, June). Improving differential evolution through Bayesian hyperparameter optimization. In 2021 IEEE Congress on Evolutionary Computation (CEC) pp. 832–840. https://doi.org/10.1109/CEC45853.2021.9504792
   Google Scholar
• Boussaïd, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences, 237, 82–117. https://doi.org/10.1016/j.ins.2013.02.041
   Web of Science ®Google Scholar
• Cai, H. R., Chung, C. Y., & Wong, K. P. (2008). Application of differential evolution algorithm for transient stability constrained optimal power flow. IEEE Transactions on Power Systems, 23(2), 719–728. https://doi.org/10.1109/TPWRS.2008.919241
   Web of Science ®Google Scholar
• Contaxis, G. C., Delkis, C., & Korres, G. (1986). Decoupled optimal load flow using linear or quadratic programing. IEEE Transactions on Power Systems, PWRS-1(1), 1–7. https://doi.org/10.1109/TPWRS.1986.4334888
   Google Scholar
• Del Ser, J., Osaba, E., Molina, D., Yang, X. S., Salcedo-Sanz, S., Camacho, D., & Herrera, F. (2019). Bio-inspired computation: Where we stand and what’s next. Swarm and Evolutionary Computation, 48, 220–250. https://doi.org/10.1016/j.swevo.2019.04.008
   Web of Science ®Google Scholar
• Dereli, S., & Köker, R. (2020). A meta-heuristic proposal for inverse kinematics solution of 7-DOF serial robotic manipulator: Quantum behaved particle swarm algorithm. Artificial Intelligence Review, 53(2), 949–964. https://doi.org/10.1007/s10462-019-09683-x
   Web of Science ®Google Scholar
• Devaraj, D., & Yegnanarayana, B. (2005). Genetic-algorithm-based optimal power flow for security enhancement. IEE Proceedings-Generation, Transmission and Distribution, 152(6), 899–905. https://doi.org/10.1049/ip-gtd:20045234
   Google Scholar
• Dréo, J., Pétrowski, A., Siarry, P., & Taillard, E. (2006). Metaheuristics for hard optimization: Methods and case studies. Springer Science & Business Media.
   Google Scholar
• Duman, S., Guvenc, U., Sonmez, Y., & Yorukeren, N. (2012). Optimal power flow using gravitational search algorithm. Energy Conversion and Management, 59, 86–95. https://doi.org/10.1016/j.enconman.2012.02.024
   Web of Science ®Google Scholar
• Eligüzel, İ. M., & Özceylan, E. (2021). Application of an improved discrete crow search algorithm with local search and elitism on a humanitarian relief case. Artificial Intelligence Review, 54(1), 1–27. https://doi.org/10.1007/s10462-021-10006-2
   PubMedGoogle Scholar
• Gan, D., Thomas, R. J., & Zimmerman, R. D. (2000). Stability-constrained optimal power flow. IEEE Transactions on Power Systems, 15(2), 535–540. https://doi.org/10.1109/59.867137
   Web of Science ®Google Scholar
• Gandomi, A. H. (2014). Interior search algorithm (ISA): A novel approach for global optimization. ISA transactions, 53(4), 1168–1183. https://doi.org/10.1016/j.isatra.2014.03.018
   PubMed Web of Science ®Google Scholar
• Gharehchopogh, F. S., Shayanfar, H., & Gholizadeh, H. (2020). A comprehensive survey on symbiotic organisms search algorithms. Artificial Intelligence Review, 53(3), 2265–2312. https://doi.org/10.1007/s10462-019-09733-4
   Web of Science ®Google Scholar
• Gupta, S., & Deep, K. (2019). An opposition-based chaotic grey wolf optimizer for global optimisation tasks. Journal of Experimental & Theoretical Artificial Intelligence, 31(5), 751–779. https://doi.org/10.1080/0952813X.2018.1554712
   Web of Science ®Google Scholar
• Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646–667. https://doi.org/10.1016/j.future.2019.07.015
   Web of Science ®Google Scholar
• Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.028
   Web of Science ®Google Scholar
• Jabr, R. A., Coonick, A. H., & Cory, B. J. (2002). A primal-dual interior point method for optimal power flow dispatching. IEEE Transactions on Power Systems, 17(3), 654–662. https://doi.org/10.1109/TPWRS.2002.800870
   Web of Science ®Google Scholar
• Jiang, Q., & Huang, Z. (2010). An enhanced numerical discretization method for transient stability constrained optimal power flow. IEEE Transactions on Power Systems, 25(4), 1790–1797. https://doi.org/10.1109/TPWRS.2010.2043451
   Web of Science ®Google Scholar
• Jiang, Q., & Geng, G. (2010). A reduced-space interior point method for transient stability constrained optimal power flow. IEEE Transactions on Power Systems, 25(3), 1232–1240. https://doi.org/10.1109/TPWRS.2009.2037717
   Web of Science ®Google Scholar
• Kahraman, H. T., Aras, S., & Gedikli, E. (2020). Fitness-distance balance (FDB): A new selection method for meta-heuristic search algorithms. Knowledge-Based Systems, 190, 105–169. https://doi.org/10.1016/j.knosys.2019.105169
   Web of Science ®Google Scholar
• Karimi-Mamaghan, M., Mohammadi, M., Meyer, P., Karimi-Mamaghan, A. M., & Talbi, E. G. (2021). Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art. European Journal of Operational Research, 296(2), 393–422. https://doi.org/10.1016/j.ejor.2021.04.032
   Web of Science ®Google Scholar
• Liang, J. J., Qu, B. Y., & Suganthan, P. N. (2013). Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 635, 490.
   Google Scholar
• Liang, J., Suganthan, P. N., Qu, B. Y., Gong, D. W., & Yue, C. T. (2019). Problem definitions and evaluation criteria for the CEC 2020 special session on multimodal multiobjective optimization. 201912, Zhengzhou University, https://doi.org/10.13140/RG.2.2.31746.02247
   Google Scholar
• Mallipeddi, R., & Suganthan, P. N. (2010). Problem definitions and evaluation criteria for the CEC 2010 competition on constrained real-parameter optimization. Nanyang Technological University, Singapore, 24. http://www.al-roomi.org/multimedia/CEC_Database/CEC2010/RealParameterOptimization/CEC2010_RealParameterOptimization_TechnicalReport.pdf
   Google Scholar
• Meraihi, Y., Gabis, A. B., Ramdane-Cherif, A., & Acheli, D. (2021). A comprehensive survey of crow search algorithm and its applications. Artificial Intelligence Review, 54(4), 2669–2716. https://doi.org/10.1007/s10462-020-09911-9
   Web of Science ®Google Scholar
• Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
   Web of Science ®Google Scholar
• Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based Systems, 89, 228–249. https://doi.org/10.1016/j.knosys.2015.07.006
   Web of Science ®Google Scholar
• Mirjalili, S. (2016). SCA: A sine cosine algorithm for solving optimization problems. Knowledge-based Systems, 96, 120–133. https://doi.org/10.1016/j.knosys.2015.12.022
   Web of Science ®Google Scholar
• Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
   Web of Science ®Google Scholar
• Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
   Web of Science ®Google Scholar
• Mittal, H., Pal, R., Kulhari, A., & Saraswat, M. (2016, August). Chaotic kbest gravitational search algorithm (ckgsa). In 2016 ninth international conference on contemporary computing (IC3) (pp. 1–6). IEEE. https://doi.org/10.1109/IC3.2016.7880252
   Google Scholar
• Mo, N., Zou, Z. Y., Chan, K. W., & Pong, T. Y. G. (2007). Transient stability constrained optimal power flow using particle swarm optimization. IET Generation, Transmission & Distribution, 1(3), 476–483. https://doi.org/10.1049/iet-gtd:20060273
   Web of Science ®Google Scholar
• Mohamed, A. A. A., Mohamed, Y. S., El-Gaafary, A. A., & Hemeida, A. M. (2017). Optimal power flow using moth swarm algorithm. Electric Power Systems Research, 142, 190–206. https://doi.org/10.1016/j.epsr.2016.09.025
   Web of Science ®Google Scholar
• Mukherjee, A., Roy, P. K., & Mukherjee, V. (2016). Transient stability constrained optimal power flow using oppositional krill herd algorithm. International Journal of Electrical Power & Energy Systems, 83, 283–297. https://doi.org/10.1016/j.ijepes.2016.03.058
   Web of Science ®Google Scholar
• Niknam, T., Rasoul Narimani, M., Jabbari, M., & Malekpour, A. R. (2011). A modified shuffle frog leaping algorithm for multi-objective optimal power flow. Energy, 36(11), 6420–6432. https://doi.org/10.1016/j.energy.2011.09.027
   Web of Science ®Google Scholar
• Parouha, R. P., & Verma, P. (2021). Design and applications of an advanced hybrid meta-heuristic algorithm for optimization problems. Artificial Intelligence Review, 54(1), 1–80. https://doi.org/10.1007/s10462-021-09962-6
   PubMedGoogle Scholar
• Pierezan, J., Dos Santos Coelho, L., Mariani, V. C., de Vasconcelos Segundo, E. H., & Prayogo, D. (2021). Chaotic coyote algorithm applied to truss optimization problems. Computers & Structures, 242, 106353. https://doi.org/10.1016/j.compstruc.2020.106353
   Web of Science ®Google Scholar
• Piotrowski, A. P., & Napiorkowski, J. J. (2018). Step-by-step improvement of JADE and SHADE-based algorithms: Success or failure? Swarm and Evolutionary Compututation, 43, 88–108. https://doi.org/10.1016/j.swevo.2018.03.007
   Web of Science ®Google Scholar
• Prasad, D., Mukherjee, A., Shankar, G., & Mukherjee, V. (2017). Application of chaotic whale optimisation algorithm for transient stability constrained optimal power flow. IET Science, Measurement & Technology, 11(8), 1002–1013. https://doi.org/10.1049/iet-smt.2017.0015
   Web of Science ®Google Scholar
• Prasad, D., Mukherjee, A., & Mukherjee, V. (2017). Transient stability constrained optimal power flow using chaotic whale optimization algorithm. In: Pijush Samui, Dr. Sanjiban Sekhar Roy, & Valentina E. Balas (Eds.), Handbook of neural computation (pp. 311–332). Academic Press. https://doi.org/10.1016/B978-0-12-811318-9.00017-X.
   Google Scholar
• Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.004
   Web of Science ®Google Scholar
• Roa-Sepulveda, C. A., & Pavez-Lazo, B. J. (2003). A solution to the optimal power flow using simulated annealing. International Journal of Electrical Power & Energy Systems, 25(1), 47–57. https://doi.org/10.1016/S0142-0615(02)00020-0
   Web of Science ®Google Scholar
• Saadat, H. (1999). Power system analysis. McGraw-Hill.
   Google Scholar
• Saha, A., Bhattacharya, A., Das, P., & Chakraborty, A. K. (2020). HSOS: A novel hybrid algorithm for solving the transient-stability-constrained OPF problem. Soft Computing, 24(10), 7481–7510. https://doi.org/10.1007/s00500-019-04374-9
   Web of Science ®Google Scholar
• Sayed, G. I., Darwish, A., & Hassanien, A. E. (2018). A new chaotic multi-verse optimization algorithm for solving engineering optimization problems. Journal of Experimental & Theoretical Artificial Intelligence, 30(2), 293–317. https://doi.org/10.1080/0952813X.2018.1430858
   Web of Science ®Google Scholar
• Shareef, H., Ibrahim, A. A., & Mutlag, A. H. (2015). Lightning search algorithm. Applied Soft Computing, 36, 315–333. https://doi.org/10.1016/j.asoc.2015.07.028
   Web of Science ®Google Scholar
• Soni, B. P., Kumar, R., Gupta, V., Saxena, A., & Surana, S. L. (2018). Application of intelligent grey wolf optimizer for transient stability constrained optimal power flow. In 2018 8th IEEE India International Conference on Power Electronics (IICPE) pp. 1–6. https://doi.org/10.1109/IICPE.2018.8709414
   Google Scholar
• Sörensen, K. (2015). Metaheuristics—the metaphor exposed. International Transactions in Operational Research, 22(1), 3–18. https://doi.org/10.1111/itor.12001
   Web of Science ®Google Scholar
• Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359. https://doi.org/10.1023/A:1008202821328
   Web of Science ®Google Scholar
• Suja, K. R. (2021). Mitigation of power quality issues in smart grid using levy flight based moth flame optimization algorithm. Journal of Ambient Intelligence and Humanized Computing, 12(10), 9209–9228. https://doi.org/10.1007/s12652-020-02626-3
   Web of Science ®Google Scholar
• Suresh, V., & Kumar, S. S. (2020). Optimal reactive power dispatch for minimization of real power loss using SBDE and DE-strategy algorithm. Journal of Ambient Intelligence and Humanized Computing, 1–15. https://doi.org/10.1007/s12652-020-02673-w
   PubMed Web of Science ®Google Scholar
• Tangpatiphan, K., & Yokoyama, A. (2008, October). Evolutionary programming incorporating neural network for transient stability constrained optimal power flow. In 2008 Joint International Conference on Power System Technology and IEEE Power India Conference (pp. 1–8). IEEE. https://doi.org/10.1109/ICPST.2008.4745318
   Google Scholar
• Tharani, B., & Nagarajan, C. (2021). An AC–DC/DC–DC hybrid multi-port embedded energy router based steady-state power flow optimizing in power system using substantial transformative energy management strategy. Journal of Ambient Intelligence and Humanized Computing, 12(5), 5687–5707. https://doi.org/10.1007/s12652-022-04006-5
   Web of Science ®Google Scholar
• Tong, H., Zhu, Y., Pierezan, J., Xu, Y., & Dos Santos Coelho, L. (2022). Chaotic coyote optimization algorithm. Journal of Ambient Intelligence and Humanized Computing, 13, 2807–2827. https://doi.org/10.1007/s12652-021-03234-5.
   Google Scholar
• Turkeš, R., Sörensen, K., & Hvattum, L. M. (2021). Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search. European Journal of Operational Research, 292(2), 423–442. https://doi.org/10.1016/j.ejor.2020.10.045
   Web of Science ®Google Scholar
• Varadarajan, M., & Swarup, K. S. (2008). Solving multi-objective optimal power flow using differential evolution. IET Generation, Transmission & Distribution, 2(5), 720–730. https://doi.org/10.1049/iet-gtd:20070457
   Web of Science ®Google Scholar
• Wang, G. G. (2018). Moth search algorithm: A bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151–164. https://doi.org/10.1007/s12293-016-0212-3
   Web of Science ®Google Scholar
• Xia, Y., Chan, K. W., & Liu, M. (2005). Direct nonlinear primal–dual interior-point method for transient stability constrained optimal power flow. IEE Proceedings - Generation, Transmission and Distribution, 152(1), 11–16. https://doi.org/10.1049/ip-gtd:20041204
   Google Scholar
• Xia, Y., & Chan, K. W. (2006). Dynamic constrained optimal power flow using semi-infinite programming. IEEE Transactions on Power Systems, 21(3), 1455–1457. https://doi.org/10.1109/TPWRS.2006.879241
   Web of Science ®Google Scholar
• Xia, S. W., Zhou, B., Chan, K. W., & Guo, Z. Z. (2015). An improved GSO method for discontinuous non-convex transient stability constrained optimal power flow with complex system model. International Journal of Electrical Power & Energy Systems, 64, 483–492. https://doi.org/10.1016/j.ijepes.2014.07.051
   Web of Science ®Google Scholar
• Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. 2009 World Congress on Nature & Biologically Inspired Computing (Nabic), 210–214. https://doi.org/10.1109/NABIC.2009.5393690
   Google Scholar
• Yapici, H. (2021). Solution of optimal reactive power dispatch problem using pathfinder algorithm. Engineering Optimization, 53(11), 1946-1963. https://doi.org/10.1080/0305215X.2020.1839443
   Web of Science ®Google Scholar
• Yuan, Y., Kubokawa, J., & Sasaki, H. (2003). A solution of optimal power flow with multi contingency transient stability constraints. IEEE Transactions on Power Systems, 18(3), 1094–1102. https://doi.org/10.1109/TPWRS.2003.814856
   Web of Science ®Google Scholar
• Zhao, W., Wang, L., & Zhang, Z. (2020). Artificial ecosystem-based optimization: A novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 32(13), 9383–9425. https://doi.org/10.1007/s00521-019-04452-x
   Web of Science ®Google Scholar