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An EvolutionaryAlgorithm for Deriving Optimal Operating Policy Under Uncertainties for Tehran Multi-reservoir System | ||
| AUT Journal of Civil Engineering | ||
| مقاله 9، دوره 3، شماره 2، اسفند 2019، صفحه 213-224 اصل مقاله (1.47 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajce.2019.15254.5528 | ||
| نویسندگان | ||
| Farzane Karami* ؛ Alireza Borhani Dariane | ||
| K.N. Toosi University of Technology | ||
| چکیده | ||
| Projecting future inflows under climate change and rapidly growing population has large uncertainty and requires serious attention for proper utilization of limited water resources. Existing algorithms can only optimize the operation policy for a specified scenario (e.g., drought, wet, or normal year; decreased or increased demands) and when established, the system would face serious operational difficulty if the expected scenario does not occur. On the other hand, most of water resources systems involve more than three objectives and demand proper techniques to handle computational complexities in so-called many-objective problems. This paper aims at providing a many-objective optimization algorithm using social choice (SC) and melody search (MeS) algorithms that is able to efficiently derive general system operation rules suitable for all possible future scenarios. In other words, the proposed algorithm overcomes uncertainties in the occurrence of future scenarios and works optimally regardless of future conditions; whether it be variable streamflows and/or increased water demands. To evaluate the performance of the proposed algorithm, a system consisting of five reservoirs in the Tehran region with four objective functions. It is shown that in all cases the general multi-scenario rule derived by the proposed method performs as good as each of the operation rules derived for every specific scenario assuming the occurrence of that scenario. Moreover, the proposed many-objective algorithm is able to handle as many objectives as needed without any computational burden and/or algorithm complexity. | ||
| کلیدواژهها | ||
| Many-Objective؛ Uncertainty؛ Scenarios؛ Melody Search؛ Social Choice | ||
| مراجع | ||
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[1] B. Wang, W. Li, G.H. Huang, L. Liu, L. Ji, Y. Li, Urban water resources allocation under the uncertainties of water supply and demand: a case study of Urumqi China, Environmental Earth Sciences, 74 (2015) 3543-3557.
[2] M. Nazeri Tahroudi, F. Ahmadi, K. Khalili, Impact of 30 Years Changing of River Flow on Urmia Lake Basin, AUT Journal of Civil Engineering, 2 (2018) 115-122.
[3] A. Nazemi, H.S. Wheater, How can the uncertainty in the natural inflow regime propagate into the assessment systems?, Advances of Water Resources, 63 (2014) 131-142.
[4] L.C. Chang, F.J. Chang, Multi-objective evolutionary algorithm for operating parallel reservoir system, Journal of Hydrology, 377 (2009) 12–20.
[5] C. Dai, Y. Wang, L. Hu, An improved α-dominance strategy for many-objective optimization problems, Soft Computing, 20 (2016)1105–1111.
[6] K.W. Wang, L.C. Chang, F.J. Chang, Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation, Advances of Water Resources, 34 (2011) 1343–1351.
[7] H. Daellenbach, A. Kluyver, Note on multiple objective dynamic programming, Journal of the Operational Research Society, 31 (1980) 591–594 .
[8] B. Villarreal, M. Karwan, Multicriteria integer programming: a hybrid dynamic programming recursive approach, Mathematical Programming, 21 (1981) 204–223.
[9] B. Talkudar, D. Deb, S. DK, Development of multiobjective stochastic dynamic programming (MOSDP) reservoir operation model, in World Environmental and Water Resources Congress, (2012) 985-997.
[10] A. Serrat-Capdevila, J.B. Valdes, An alternative approach to the operation of multinational reservoir systems: Application to the Amistad and Falcon system (Lower Rio Grande/Rio Bravo), Water Resources Management, 21 (2007) 677–698.
[11] N. Saadouli, C. Edirisinghe, Multi-stage stochastic model for a multipurpose water reservoir with target-priority operation, Water, (2006) 146–151.
[12] J.W. Labadie, Optimal Operation of Multireservoir Systems: State-of-the-Art Review, Journal of Water Resources Planning Management, 130 (2004) 93–111.
[13] P.J van Laarhoven, E.H. Aarts, Simulated Annealing: Theory and Applications, Springer Netherlands, (1987) 7-15.
[14] Z.W. Geem, J.H. Kim, G.V. Loganathan, A New Heuristic Optimization Algorithm: Harmony Search, Simulation, 76 (2001) 60–68.
[15] P.J. Fleming, R.C. Purshouse, R.J. Lygoe, M. Street, S. Sheffield, Many-objective optimization: an engineering design perspective, Lecture Notes in Computer Science, 3410 (2005) 14–32.
[16] S.F. Adra, P.J. Fleming, Diversity management in evolutionary many-objective optimization, IEEE Transactions on Evolutionary Computation, 15 (2011) 183–195.
[17] Z. He, G.G. Yen, J. Zhang, Fuzzy-based pareto optimality for many-objective evolutionary algorithms, IEEE Transactions on Evolutionary Computation, 18 (2014) 269–285.
[18] Y. Zhou, J. Wang, J. Chen, S. Gao, L. Teng, Ensemble of many-objective evolutionary algorithms for many-objective problems, Soft Computing, 21(9) (2015) 2407-2419.
[19] M. Li, S. Yang, X. Liu, Shift-based density estimation for pareto-based algorithms in many-objective optimization, IEEE Transactions on Evolutionary Computation, 18 (2014) 348–365.
[20] X. Ma, Y. Qi, L. Li, F. Liu, L. Jiao, J. Wu, MOEA/D with uniform decomposition measurement for many-objective problems, Soft Computing, 18 (2014) 2541–2564.
[21] E. Zitzler, L. Thiele, Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation, 3 (1999) 257–271.
[22] E. Zitzler, L. Thiele, Multi-objective optimization using evolutionary algorithms- a comparative case study, Parallel Problem Solving from Nature, Springer, Berlin, Heidelberg, (1998) 292–301.
[23] N. Beume, B. Naujoks, M. Emmerich, SMS-EMOA: Multiobjective selection based on dominated hypervolume, European Journal of Operational Research, 181 (2007) 1653–1669.
[24] C. Igel, N. Hansen, S. Roth, Covariance matrix adaptation for multi-objective optimization, Evolutionary Computation, 15 (2007) 1–28.
[25] E. Zitzler, S. Künzli, Indicator-based selection in multiobjective search, Parallel Problem Solving from Nature, Springer, Berlin, Heidelberg, (2004) 832–842.
[26] M. Emmerich, N. Beume, B. Naujoks, An EMO algorithm using the hypervolume measure as selection criterion, Evolutionary Multi-Criterion Optimization, (2000) 32–76.
[27] D. Brockhoff, E. Zitzler, Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods, Congress on Evolutionary Computation (CEC), (2007), 2086–2093.
[28] Y. Yuan, H. Xu, B. Wang, B. Zhang, X. Yao, Balancing convergence and diversity in decomposition-based many- objective optimizers, IEEE Transactions on Evolutionary Computation, 20 (2016) 180–198.
[29] Y. Yuan, H. Xu, B. Wang, X. Yao, A new dominance relation-based evolutionary algorithm for many-objective optimization, IEEE Transactions on Evolutionary Computation, 20 (2016) 16–37.
[30] J. Bader, E. Zitzler, HypE : An algorithm for fast optimization, Evolutionary Computation, 19 (2011) 45–76.
[31] S. Yang, M. Li, X. Liu, J. Zheng, A grid-based evolutionary algorithm for many-objective optimization, IEEE Transactions on Evolutionary Computation, 17 (2013) 721–736.
[32] K. Deb, H. Jain, An evolutionary many-objective optimization algorithm using reference-point based non- dominated sorting approach, Part II: handling constraints and extending to an adaptive approach, IEEE Transactions on Evolutionary Computation, 18 (2013) 602–622.
[33] Y. Li, H. Liu, K. Xie, X. Yu, A method for distributing reference points uniformly along the Pareto front of DTLZ test functions in many-objective evolutionary optimization, 5th International Conference on Information Science and Technology (ICIST), (2015) 541–546.
[34] A.B. Ruiz, R. Saborido, M. Luque, A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm, Journal of Global Optimization, 62 (2015) 101– 129.
[35] B. Srdjevic, Linking analytic hierarchy process and social choice methods to support group decision-making in water management, Decision Support Systems, 42 (2007) 2261– 2273.
[36] U. Ebert, H. Welsch, Meaningful environmental indices: A social choice approach, Journal of Environmental Economics and Management, 47(2004) 270–283.
[37] S.M. Ashrafi, A.B. Dariane, Performance evaluation of an improved harmony search algorithm for numerical optimization: Melody Search (MS), Engineering applications of artificial intelligence, 26 (2013) 1301–1321.
[38] K.S. Lee, Z.W. Geem, A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice, Computer Methods in Applied Mechanics and Engineering, 194 (2005) 3902–3933.
[39] M. Mahdavi, M. Fesanghary, E. Damangir, An improved harmony search algorithm for solving optimization problems, Applied Mathematics and Computing, 188 (2007) 1567– 1579.
[40] M. Fesanghary, M. Mahdavi, M. Minary-Jolandan, Y. Alizadeh, Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems, Computer Methods in Applied Mechanics and Engineering, 197 (2008) 3080–3091.
[41] J.C. de Borda, Mathematical derivation of an election system, Isis, 44(1/2) (1781) 42–51.
[42] K.J. Arrow, Social choice and individual values, John Wiley & Sons, Inc. (1951).
[43] A. D’Angelo, A. Eskandari, F. Szidarovszky, Social choice procedures in water resource management, Journal of Environmental Management, 52 (1998) 203-210.
[44] J.N. Mordeson, D.S. Malik, T.D. Clark, Application of Fuzzy Logic to Social Choice Theory, Chapman and Hall/ CRC, (2015).
[45] K.J. Arrow, A.K. Sen, K. Suzumura, Handbook of Social Choice and Welfare, Elsevier (2010).
[46] D. Black, On the rationale of group decision making, Journal of Political Economy, 56 (1948).
[47] K.I. Inada, Majority rule and rationality, Economic Theory, 2 (1970) 27-40.
[48] S. Kant, S. Lee, A social choice approach to sustainable forest management: An analysis of multiple forest values in Northwestern Ontario, For. Policy Econ., 6 (2004) 215–227.
[49] R.U. Goetz, Y. Martinez, J. Rodrigo, Water allocation by social choice rules: The case of sequential rules, Ecological Economics, 65 (2008) 304–314.
[50] J. Malkevitch, Voting and Elections: Introduction, http:// www.ams.org/samplings/feature-column/fcarc-voting- introduction.
[51] A.D. Taylor, Mathematics and politics, Springer-Verlag, New York (1995).
[52] N. Srinivas, K. Deb, Muiltiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation, 2 (1995) 221-248.
[53] K. Deb, S. Pratab, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NGSA-II, IEEE Transactions on Evolutionary Computation, 6 (2002) 182– 197.
[54] J.L. Cohon, D.H. Marks, A review and evaluation of multiobjective programming techniques, Water Resources Research, 11(1975) 208–220. | ||
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