Solving Economic Emission Load Dispatch Using Oppositional Slime Mould Algorithm With Wavelet Mutation

References

• Z-l Gaing, “Particle swarm optimization to solving the economic dispatch considering the generator constraints,” IEEE Trans. Power Syst., vol. 18, no. 3, pp. 1187–1195, 2003. DOI: 10.1109/TPWRS.2003.814889.
   Web of Science ®Google Scholar
• P. Aravindhababu and K. R. Nayar, “Economic dispatch based on optimal lambda using radial basis function network,” Electr. Power Energy Syst., vol. 24, no. 7, pp. 551–556, 2002. DOI: 10.1016/S0142-0615(01)00063-1.
   Web of Science ®Google Scholar
• J. C. Dodu, P. Martin, A. Merlin, and J. Pouget, “An optimal formulation and solution of short-range operating problems for a power system with flow constraints,” Proc. IEEE, vol. 60, no. 1, pp. 54–63, 1972. DOI: 10.1109/PROC.1972.8557.
   Google Scholar
• S. Fanshel and E. S. Lynes, “Economic power generation using linear programming,” IEEE Trans. Power Appl. Syst., vol. 83, no. 4, pp. 347–356, 1964. DOI: 10.1109/TPAS.1964.4766011.
   Google Scholar
• A. Ei-Keib, H. Ma, and J. L. Hart, “Enviromentally constrained economic dispatch using the La Grangian relaxation method,” IEEE Trans. Power Syst., vol. 9, no. 4, pp. 1723–1729, 1994. DOI: 10.1109/59.331423.
   Web of Science ®Google Scholar
• J. Fan and L. Zhang, “Real-time economic dispatch with line flow and emission constraints using quadratic programming,” IEEE Trans. Power Syst., vol. 13, no. 2, pp. 320–325, 1998.
   Web of Science ®Google Scholar
• P.H. Chen and H.C. Chang, “Large-scale economic dispatch by genetic algorithm,” IEEE Trans. Power Syst., vol. 10, no. 4, pp. 1919–1926, 1995.
   Google Scholar
• J-b Park, Y-w Jeong, J-r Shin, S. Member, and K. Y. Lee, “An improved particle swarm optimization for nonconvex economic dispatch problems,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 156–166, 2010. DOI: 10.1109/TPWRS.2009.2030293.
   Web of Science ®Google Scholar
• M. Pradhan, P. K. Roy and T. Pal, “Grey wolf optimization applied to economic load dispatch problems,” Int. J. Electr. Power Energy Syst., vol. 83, pp. 325–334, 2016. DOI: http://dx.doi.org/10.1016/j.ijepes.2016.04.034.
   Web of Science ®Google Scholar
• N. Noman and H. Iba, “Differential evolution for economic load dispatch problems,” Electr. Power Syst. Res., vol. 78, no. 8, pp. 1322–1331, 2008. DOI: 10.1016/j.epsr.2007.11.007.
   Web of Science ®Google Scholar
• T. Apostolopoulos and A. Vlachos, “Application of the firefly algorithm for solving the economic emission load dispatch problem,” Int. J. Comb., vol. 2011, pp. 1–23, 2011. DOI: 10.1155/2011/523806.
   Google Scholar
• S. Duman, U. Güvenç, and N. Yörükeren, “Gravitational search algorithm for economic dispatch with valve-point effects,” Int. Rev. Electr. Eng. (IREE), vol. 5, no. 6, pp. 2890–2895, 2010.
   Web of Science ®Google Scholar
• N. Ghorbani and E. Babaei, “Exchange market algorithm for economic load dispatch,” Electr. Power Energy Syst., vol. 75, pp. 19–27, 2016.
   Web of Science ®Google Scholar
• M. Moradi-Dalvand, B. Mohammadi-Ivatloo, A. Najafi, and A. Rabiee, “Continuous quick group search optimizer for solving non-convex economic dispatch problems,” Electr. Power Syst. Res., vol. 93, pp. 93–105, 2012. DOI: http://dx.doi.org/10.1016/j.epsr.2012.07.009.
   Web of Science ®Google Scholar
• B. Mandal, P. K. Roy, and S. Mandal, “Economic load dispatch using krill herd algorithm,” Int. J. Electr. Power Energy Syst., vol. 57, pp. 1–10, 2014. DOI: http://dx.doi.org/10.1016/j.ijepes.2013.11.016.
   Web of Science ®Google Scholar
• N. J. Singh, J. S. Dhillon, and D. P. Kothari, “Synergic predator-prey optimization for economic thermal power dispatch problem,” Appl. Soft Comput. J., vol. 43, pp. 298–311, 2016. DOI: http://dx.doi.org/10.1016/j.asoc.2016.02.042.
   Web of Science ®Google Scholar
• S. Sahoo, K. M. Dash, R. C. Prusty, and A. K. Barisal, “Comparative analysis of optimal load dispatch through evolutionary algorithms,” Ain Shams Eng. J., vol. 6, no. 1, pp. 107–120, 2015. DOI: http://dx.doi.org/10.1016/j.asej.2014.09.002.
   Google Scholar
• M. Kumar and J. S. Dhillon, “A conglomerated ion-motion and crisscross search optimizer for electric power load dispatch,” Appl. Soft Comput. J., vol. 83, pp. 105641, 2019. DOI: 10.1016/j.asoc.2019.105641.
   Web of Science ®Google Scholar
• J. J. Q. Yu and V. O. K. Li, “A social spider algorithm for solving the non-convex economic load dispatch problem,” Neurocomputing, vol. 171, pp. 955–965, 2016. DOI: http://dx.doi.org/10.1016/j.neucom.2015.07.037.
   Web of Science ®Google Scholar
• T. Jayabarathi, T. Raghunathan, B. R. Adarsh, and P. N. Suganthan, “Economic dispatch using hybrid grey wolf optimizer,” Energy, vol. 111, pp. 630–641, 2016. DOI: http://dx.doi.org/10.1016/j.energy.2016.05.105.
   Web of Science ®Google Scholar
• D. Singh and J. S. Dhillon, “Ameliorated grey wolf optimization for economic load dispatch problem,” Energy, vol. 169, pp. 398–419, 2019. DOI: 10.1016/j.energy.2018.11.034.
   Web of Science ®Google Scholar
• S. Duman, N. Yorukeren, and I. H. Altas, “A novel modified hybrid PSOGSA based on fuzzy logic for non-convex economic dispatch problem with valve-point effect,” Int. J. Electr. Power Energy Syst., vol. 64, pp. 121–135, 2015. DOI: http://dx.doi.org/10.1016/j.ijepes.2014.07.031.
   Web of Science ®Google Scholar
• M. S. P. Subathra, S. E. Selvan, T. A. A. Victoire, A. H. Christinal, and U. Amato, “A hybrid with cross-entropy method and sequential quadratic programming to solve economic load dispatch problem,” IEEE Syst. J., vol. 9, no. 3, pp. 1031–1044, 2015. DOI: 10.1109/JSYST.2013.2297471.
   Web of Science ®Google Scholar
• M. Singh and J. S. Dhillon, “Multiobjective thermal power dispatch using opposition-based greedy heuristic search,” Electr. Power Energy Syst., vol. 82, pp. 339–353, 2016. DOI: 10.1016/j.ijepes.2016.03.016.
   Web of Science ®Google Scholar
• W. T. Elsayed, Y. G. Hegazy, and M. S. El-Bages, “Improved random drift particle swarm optimization with self-adaptive mechanism solving power economic dispatch problem,” IEEE Trans. Indus. Inform., vol. 13, no. 3, pp. 1017–1026, 2017.
   Google Scholar
• J. X. V. Neto, G. Reynoso-Meza, T. H. Ruppel, V. C. Mariani, and L. d S. Coelho, “Solving non-smooth economic dispatch by a new combination of continuous GRASP algorithm and differential evolution,” Electr. Power Energy Syst., vol. 84, pp. 13–24, 2017. DOI: 10.1016/j.ijepes.2016.04.012.
   Web of Science ®Google Scholar
• M. A. Al-Betar, M. A. Awadallah, A. T. Khader, A. L. Bolaji, and A. Almomani, “Economic load dispatch problems with valve-point loading using natural updated harmony search,” Neural Comput. Appl., vol. 29, no. 10, pp. 767–781, 2018. DOI: 10.1007/s00521-016-2611-2.
   Web of Science ®Google Scholar
• F. Mohammadi and H. Abdi, “A modified crow search algorithm (MCSA) for solving economic load dispatch problem,” Appl. Soft Comput. J., vol. 71, pp. 51–65, 2018. DOI: 10.1016/j.asoc.2018.06.040.
   Web of Science ®Google Scholar
• R. A. Ibrahim, M. A. Elaziz, D. Oliva, E. Cuevas, and S. Lu, “An opposition-based social spider optimization for feature selection,” Soft Comput., vol. 23, no. 24, pp. 13547–13567, 2019. DOI: 10.1007/s00500-019-03891-x.
   Web of Science ®Google Scholar
• G. Dhiman, “MOSHEPO: A hybrid multi-objective approach to solve economic load dispatch and micro grid problems,” Appl. Intell., vol. 50, no. 1, pp. 119–137, 2020. DOI: 10.1007/s10489-019-01522-4.
   Web of Science ®Google Scholar
• T. T. Nguyen, C.-T. Nguyen, L. Van Dai, and N. Vu Quynh, “Finding optimal load dispatch solutions by using a proposed Cuckoo search algorithm,” Math. Probl. Eng., vol. 2019, pp. 1–29, 2019. DOI: 10.1155/2019/1564693.
   Web of Science ®Google Scholar
• V. Kansal and J. S. Dhillon, “Emended SALP swarm algorithm for multiobjective electric power dispatch problem,” Appl. Soft Comput. J., vol. 90, pp. 106172, 2020. DOI: 10.1016/j.asoc.2020.106172.
   Web of Science ®Google Scholar
• S. Pan, J. Jian, H. Chen, and L. Yang, “A full mixed-integer linear programming formulation for economic dispatch with valve-point effects, transmission loss and prohibited operating zones,” Electr. Power Syst. Res., vol. 180, pp. 106061, 2020. DOI: 10.1016/j.epsr.2019.106061.
   Web of Science ®Google Scholar
• W. Yang, T. Cheng, Y. Guo, Z. Yang, and W. Feng, “A modified social spider optimization for economic dispatch with valve-point effects,” Complexity, vol. 2020, pp. 1–13, 2020. DOI: 10.1155/2020/2865929.
   Web of Science ®Google Scholar
• D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 67–82, 1997. DOI: 10.1109/4235.585893.
   Google Scholar
• S. Li, H. Chen, M. Wang, A. Asghar, and S. Mirjalili, “Slime mould algorithm: A new method for stochastic optimization,” Future Gener. Comput. Syst., vol. 111, pp. 300–323, 2020. DOI: 10.1016/j.future.2020.03.055.
   Web of Science ®Google Scholar
• S. Mirjalili, P. Jangir, and S. Saremi, “Multi-objective ant lion optimizer: A multi-objective optimization algorithm for solving engineering problems,” Appl. Intell., vol. 46, no. 1, pp. 79–95, 2017. DOI: http://dx.doi.org/10.1007/s10489-016-0825-8.
   Web of Science ®Google Scholar
• M. H. Hassan, S. Kamel, L. Abualigah, and A. Eid, “Development and application of slime mould algorithm for optimal economic emission dispatch,” Expert Syst. Appl., vol. 182, pp. 115205, 2021. DOI: 10.1016/j.eswa.2021.115205.
   Web of Science ®Google Scholar
• D. P. Kothari and J. S. Dhillon, Power System Optimization. India: Prentice Hall of India, 2006.
   Google Scholar
• F. Howard, “The life history of Physarum polycephalum,” Am. J. Bot., vol. 18, no. 2, pp. 116–133, 1931. DOI: 10.1002/j.1537-2197.1931.tb09577.x.
   Google Scholar
• S. H. Ling, H. H. Iu, K. Y. Chan, H. K. Lam, B. C. Yeung and F. H. Leung, “Hybrid particle swarm optimization with wavelet mutation and its industrial applications,” IEEE Trans. Syst., Man Cybern. B., vol. 38, no. 3, pp. 743–763, 2008. DOI: 10.1109/TSMCB.2008.921005.
   PubMed Web of Science ®Google Scholar
• S. Rahnamayan, H. R. Tizhoosh, and M. M. A. Salama, “Opposition-based differential evolution,” IEEE Trans. Evol. Comput., vol. 12, no. 1, pp. 64–79, 2008. DOI: 10.1109/TEVC.2007.894200.
   Web of Science ®Google Scholar
• H. Nenavath, R. Kumar, and S. Das, “A synergy of the sine–cosine algorithm and particle swarm optimizer for improved global optimization and object tracking,” Swarm Evol. Comput., vol. 43, no. Aug. 2017, pp. 1–30, 2018. DOI: 10.1016/j.swevo.2018.02.011.
   Google Scholar
• S. Mirjalili, “SCA: A sine cosine algorithm for solving optimization problems,” Knowl Based Syst., vol. 96, pp. 120–133, 2016. DOI: 10.1016/j.knosys.2015.12.022.
   Web of Science ®Google Scholar
• S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili, “Salp swarm algorithm: A bio-inspired optimizer for engineering design problems,” Adv. Eng. Softw., vol. 114, pp. 163–191, 2017. DOI: 10.1016/j.advengsoft.2017.07.002.
   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. Qiao, H. Moayedi, and L. Kok, “Nature-inspired hybrid techniques of IWO, DA, ES, GA, and ICA, validated through a k-fold validation process predicting monthly natural gas consumption,” Energy Build., vol. 217, pp. 110023, 2020. DOI: 10.1016/j.enbuild.2020.110023.
   Web of Science ®Google Scholar
• L. d S. Coelho and C-s Lee, “Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches,” Electr. Power Energy Syst., vol. 30, no. 5, pp. 297–307, 2008.
   Web of Science ®Google Scholar
• G. Xiong and D. Shi, “Orthogonal learning competitive swarm optimizer for economic dispatch problems,” Appl. Soft Comput. J., vol. 66, pp. 134–148, 2018. DOI: 10.1016/j.asoc.2018.02.019.
   Web of Science ®Google Scholar
• N. Amjady and H. Sharifzadeh, “Solution of non-convex economic dispatch problem considering valve loading effect by a new modified differential evolution algorithm,” Int. J. Electr. Power Energy Syst., vol. 32, no. 8, pp. 893–903, 2010. DOI: http://dx.doi.org/10.1016/j.ijepes.2010.01.023.
   Web of Science ®Google Scholar
• A. Kavousi-Fard and A. Khosravi, “An intelligent h-modified bat algorithm to solve the non-convex economic dispatch problem considering practical constraints,” Int. J. Electr. Power Energy Syst., vol. 82, pp. 189–196, 2016. DOI: http://dx.doi.org/10.1016/j.ijepes.2016.03.017.
   Web of Science ®Google Scholar
• X-s Yang, S. Soheil, S. Hosseini, and A. Hossein, “Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect,” Appl. Soft Comput. J., vol. 12, no. 3, pp. 1180–1186, 2012. DOI: http://dx.doi.org/10.1016/j.asoc.2011.09.017.
   Web of Science ®Google Scholar
• G. Xiong, D. Shi and X. Duan, “Enhancing the performance of biogeography-based optimization using polyphyletic migration operator and orthogonal learning,” Comput. Oper. Res., vol. 41, pp. 125–139, 2014. DOI: http://dx.doi.org/10.1016/j.cor.2013.07.021.
   Web of Science ®Google Scholar
• V. S. Aragón, S. C. Esquivel, and C. A. C. Coello, “An immune algorithm with power redistribution for solving economic dispatch problems,” Inform. Sci., vol. 295, pp. 609–632, 2015. DOI: 10.1016/j.ins.2014.10.026.
   Web of Science ®Google Scholar
• Y. Labbi, D. B. Attous, H. A. Gabbar, B. Mahdad, and A. Zidan, “A new rooted tree optimization algorithm for economic dispatch with valve-point effect,” Int. J. Elect. Power Energy Syst., vol. 79, pp. 298–311, 2016. DOI: http://dx.doi.org/10.1016/j.ijepes.2016.01.028.
   Web of Science ®Google Scholar
• M. Modiri-Delshad and N. A. Rahim, “Solving non-convex economic dispatch problem via backtracking search algorithm,” Energy, vol. 77, pp. 372–381, 2014. DOI: http://dx.doi.org/10.1016/j.energy.2014.09.009.
   Web of Science ®Google Scholar
• A. Y. Abdelaziz, S. F. Mekhamer, M. A. L. Badr, and M. Z. Kamh, “Economic dispatch using an enhanced hopfield neural network,” Electr. Power Comp. Syst., vol. 36, no. 7, pp. 719–732, 2008. DOI: 10.1080/15325000701881969.
   Web of Science ®Google Scholar
• A. Bhattacharya and P. K. Chattopadhyay, “Solving complex economic load dispatch problems using biogeography-based optimization,” Exp. Syst. Appl., vol. 37, no. 5, pp. 3605–3615, 2010. DOI: http://dx.doi.org/10.1016/j.eswa.2009.10.031.
   Web of Science ®Google Scholar
• M. Pradhan, P. K. Roy, and T. Pal, “Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system,” Ain Shams Eng. J., vol. 9, no. 4, pp. 2015–2025, 2018. DOI: 10.1016/j.asej.2016.08.023.
   Web of Science ®Google Scholar
• A. K. Barisal and R. C. Prusty, “Large scale economic dispatch of power systems using oppositional invasive weed optimization,” Appl. Soft Comput. J., vol. 29, pp. 122–137, 2015. DOI: http://dx.doi.org/10.1016/j.asoc.2014.12.014.
   Web of Science ®Google Scholar
• K. Bhattacharjee, A. Bhattacharya, and S. H. Nee Dey, “Oppositional real coded chemical reaction optimization for different economic dispatch problems,” Int. J. Elect. Power Energy Syst., vol. 55, pp. 378–391, 2014. DOI: http://dx.doi.org/10.1016/j.ijepes.2013.09.033.
   Web of Science ®Google Scholar
• H. Rezaie, M. H. Kazemi-Rahbar, B. Vahidi, and H. Rastegar, “Solution of combined economic and emission dispatch problem using a novel chaotic improved harmony search algorithm,” J. Comput. Des. Eng., vol. 6, no. 3, pp. 447–467, 2019. DOI: 10.1016/j.jcde.2018.08.001.
   Web of Science ®Google Scholar
• M. Basu, “Economic environmental dispatch using multi-objective differential evolution,” Appl. Soft Comput. J., vol. 11, no. 2, pp. 2845–2853, 2011. DOI: 10.1016/j.asoc.2010.11.014.
   Web of Science ®Google Scholar