Free Access
Issue
RAIRO-Oper. Res.
Volume 54, Number 5, September-October 2020
Page(s) 1269 - 1289
DOI https://doi.org/10.1051/ro/2019043
Published online 12 June 2020
  • M. Abouheaf, S. Haesart, W. Lee and F. Lewis, Q-Learning with eligibility traces to solve non-convex economic dispatch problems, Int. J. Electr. Sci. Eng. 7 (2012) 1390–1396. [Google Scholar]
  • D. Aydin, G. Yavuz, S. Ozyön, C. Yașar and T. Stützle, Artificial bee colony framework to non-convex economic dispatch problem with valve-point effects: a case study. In: GECCO 2017 – Proceedings of the Genetic and Evolutionary Computation Conference Companion (2017) 1311–1318. [Google Scholar]
  • M. Basu, Modified particle swarm optimization for nonconvex economic dispatch problems. Int. J. Electr. Power Energy Syst. 69 (2015) 304–310. [CrossRef] [Google Scholar]
  • D. Bhagwan and C. Patvardhan, Solution of economic load dispatch using real coded hybrid stochastic search. Electr. Power Energy Syst. 21 (1999) 165–170. [CrossRef] [Google Scholar]
  • A. Bhattacharya and P. Chattopadhyay, Biogeography-based optimization for different economic load dispatch problems. IEEE Trans. Power Syst. 25 (2010) 1064–77. [Google Scholar]
  • P.-H. Chen and H.-C. Chang, Large-scale economic dispatch by genetic algorithm. IEEE Trans. Power Syst. 10 (1995) 1919–1926. [Google Scholar]
  • S. Devi and M. Geethanjali, Optimum location and sizing of distribution static synchronous series compensator using particle swarm optimization. Int. J. Electr. Power Energy Syst. 62 (2014) 646–53. [CrossRef] [Google Scholar]
  • M. Dorigo, M. Birattari and T. Stutzle, Ant colony optimization. IEEE Comput. Intell. 1 (2006) 28–39. [CrossRef] [Google Scholar]
  • E. Elattar, A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem. Int. J. Electr. Power Energy Syst. 69 (2015) 18–26. [CrossRef] [Google Scholar]
  • Z.L. Gaing, Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans. Power Syst. 18 (2003) 1187–1195. [Google Scholar]
  • Z.W. Geem, Harmony search optimisation to the pump-included water distribution network design. Civil Eng. Environ. Syst. 26 (2009) 211–221. [CrossRef] [Google Scholar]
  • Z.W. Geem, J.H. Kim and G.V. Loganathan, A new heuristic optimization algorithm: harmony search. SIMULATION 76 (2001) 60–68. [Google Scholar]
  • E.E. George, Intrasystem transmission losses. AIEE Trans. 62 (1943) 153–158. [Google Scholar]
  • A. Ghasemi, M. Gheydi, M.J. Golkar and M. Eslami, Modeling of Wind/Environment/Economic Dispatch in power system and solving via an online learning meta-heuristic method. Appl. Soft Comput. 43 (2016) 454–468. [Google Scholar]
  • F. Glover, Tabu search – Part I. ORSA J. Comput. 1 (1989) 190–206. [CrossRef] [Google Scholar]
  • F. Glover, Tabu search – Part II. ORSA J. Comput. 2 (1990) 4–32. [CrossRef] [Google Scholar]
  • A. Haghrah, M. Nazari-Heris and B. Mohammadi-ivatloo, Solving combined heat and power economic dispatch problem using real coded genetic algorithm with improved Mühlenbein mutation. Appl. Therm. Eng. 99 (2016) 465–475. [Google Scholar]
  • M.T. Hagh, S. Teimourzadeh, M. Alipour and P. Aliasghary, Improved group search optimization method for solving CHPED in large scale power systems. Energy Convers. Manage. 80 (2014) 446–456. [CrossRef] [Google Scholar]
  • H. Hamedi, Solving the combined economic load and emission dispatch problems using new heuristic algorithm. Int. J. Electr. Power Energy Syst. 46 (2013) 10–16. [CrossRef] [Google Scholar]
  • J.H. Holland, Genetic algorithms. Sci. Am. 267 (1992) 66–72. [Google Scholar]
  • P. Hota, A. Barisal and R. Chakrabarti, Economic emission load dispatch through fuzzy based bacterial foraging algorithm. Int. J. Electr. Power Energy Syst. 32 (2010) 794–803. [CrossRef] [Google Scholar]
  • J. Kennedy and R. Eberhart, Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks (1995) 1942–1948. [Google Scholar]
  • S. Khamsawang, C. Boonseng and S. Pothiya, Solving the economic dispatch problem with Tabu search algorithm. Proc. IEEE Int. Conf. Ind. Technol. 1 (2002) 274–278. [Google Scholar]
  • T.H. Khoa, P.M. Vasant, M.S.B. Singh and V.N. Dieu, Swarm based mean-variance mapping optimization for convex and non-convex economic dispatch problems. Memetic Comput. 9 (2017) 91–108. [CrossRef] [Google Scholar]
  • L.K. Kirchmayer, Economic Control of Interconnected Systems. Wiley, New York, NY (1959). [Google Scholar]
  • L.K. Kirchmayer, Economic Operation of Power Systems. Wiley, New York, NY (1993). [Google Scholar]
  • S. Kirkpatrick, C.D. Gelatt and M.P. Vecchi, Optimization by simmulated annealing. Science 220 (1983) 671–680. [Google Scholar]
  • K.S. Lee and Z.W. Geem, A new structural optimization method based on the harmony search algorithm. Comput. Struct. 82 (2004) 781–798. [Google Scholar]
  • W.-M. Lin, F.-S. Cheng and M.-T. Tsay, An improved tabu search for economic dispatch with multiple minima. IEEE Trans. Power Syst. 17 (2002) 108–112. [Google Scholar]
  • X. Lu and Y. Zhou, A novel global convergence algorithm: bee collecting Pollen algorithm. In: Advanced Intelligent Computing Theories and Applications. With Aspects of Articial Intelligence. ICIC 2008. Edited by D.S. Huang, D.C. Wunsch, D.S. Levine and K.H. Jo. In Vol. 5227 of Lecture Notes in Computer Science.Springer, Berlin, Heidelberg (2008). [Google Scholar]
  • P. Lu, J. Zhou, H. Zhang, R. Zhang and C. Wang, Chaotic differential bee colony optimization algorithm for dynamic economic dispatch problem with valve-point effects. Int. J. Electr. Power Energy Syst. 62 (2014) 130–143. [CrossRef] [Google Scholar]
  • M. Madrigal and V.H. Quintana, An analytical solution to the economic dispatch problem. IEEE Power Eng. Rev. 20 (2000) 52e5. [CrossRef] [Google Scholar]
  • F.F. Moghaddam, R.F. Moghaddam and M. Cheriet, Curved space optimization: a random search based on general relativity theory. Preprint arXiv:1208.2214 (2012). [Google Scholar]
  • B. Mohammadi-Ivatloo, M. Moradi-Dalvand and A. Rabiee, Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients. Electr. Power Syst. Res. 95 (2013) 9–18. [CrossRef] [Google Scholar]
  • S. Mirjalili and A. Lewis, The Whale optimization algorithm. Adv. Eng. Softw. 95 (2016) 51–67. [Google Scholar]
  • N. Nahas and M. Abouheaf, Novel heuristic solution for the non-convex economic dispatch problem. In: 13th International Multi-Conference on Systems Signals & Devices (SSD) (2016). [Google Scholar]
  • N. Nahas and M. Nourelfath, Non-linear threshold accepting meta-heuristic for combinatorial optimization problems. Int. J. Metaheuristics 3 (2014) 265–290. [CrossRef] [Google Scholar]
  • N. Nahas and M. Nourelfath, Joint optimization of maintenance, buffers and machines in manufacturing lines. Eng. Optim. 50 (2018) 37–54. [CrossRef] [Google Scholar]
  • M. Nazari-Heris, M. Mehdinejad, B. Mohammadi-Ivatloo and G. Babamalek-Gharehpetian, Combined heat and power economic dispatch problem solution by implementation of whale optimization method. Neural Comput. App. 31 (2019) 421–436. [CrossRef] [Google Scholar]
  • R. Ramanathan, Emission constrained economic dispatch. IEEE Trans. Power Syst. 9 (1994) 1994–2000. [Google Scholar]
  • P.K. Roy, C. Paul and S. Sultana, Oppositional teaching learning-based optimization approach for combined heat and power dispatch. Int. J. Electr. Power Energy Syst. 57 (2014) 392–403. [CrossRef] [Google Scholar]
  • A. Selvakumar and K. Thanushkodi, A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans. Power Syst. 22 (2007) 42–51. [Google Scholar]
  • D. Simon, Biogeography-based optimization. IEEE Trans. Evol. Comput. 12 (2008) 702–13. [Google Scholar]
  • M. Singh and J.S. Dhillon, Multiobjective thermal power dispatch using opposition-based greedy heuristic search. Int. J. Electr. Power Energy Syst. 82 (2016) 339–353. [CrossRef] [Google Scholar]
  • C. Su and C. Lin, New approach with a Hopfield modeling framework to economic dispatch. IEEE Trans. Power Syst. 15 (2000) 541–545. [Google Scholar]
  • A. Vasebi, M. Fesanghary and S.M.T. Bathaee, Combined heat and power economic dispatch by harmony search algorithm. Int. J. Electr. Power Energy Syst. 29 (2007) 713–719. [CrossRef] [Google Scholar]
  • T. Victoire and A. Jeyakumar, Hybrid PSO-SQP for economic dispatch with valve-point effect. Electr. Power Syst. Res. 71 (2004) 51–59. [CrossRef] [Google Scholar]
  • B. Webster and P.J. Bernhard, A local search optimization algorithm based on natural principles of gravitation. In: Proceedings of the 2003 International Conference on Information and Knowledge Engineering (IKE’03) (2003) 255–261. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.