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Water Resources Management

Erschienen in:

21.06.2018

Multi-Objective Differential Evolution-Chaos Shuffled Frog Leaping Algorithm for Water Resources System Optimization

verfasst von: Guohua Fang, Yuxue Guo, Xin Wen, Xiaomin Fu, Xiaohui Lei, Yu Tian, Ting Wang

Erschienen in: Water Resources Management | Ausgabe 12/2018

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Abstract

A multi-objective differential evolution-chaos shuffled frog leaping algorithm (MODE-CSFLA) is proposed for water resources system optimization to overcome the shortcomings of easily falling into local minima and premature convergence in SFLA. The performance of MODE-CSFLA in solving benchmark problems is compared with that of non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO). At last, the proposed MODE-CSFLA is used to optimize the water resources allocation plan of the East Route of the South-to-North Water Transfer Project in the normal, dry, and extremely dry years. The results reveal that MODE-CSFLA performs better than NSGA-II and MOPSO under all conditions. Compared with shuffled frog leaping algorithm (SFLA), MODE-CSFLA can result in a 29.39, 27.47 and 22.55% increase in water supply when the single objective is to minimize the water pumpage; and a 41.01, 39.63 and 30.94% decrease in total pumpage when the single objective is to maximize the water supply in the normal, dry, and extremely dry conditions, respectively. Thus, MODE-CSFLA has the potential to be used for solving complex optimization problems of water resources systems.

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Ahmad A, El-Shafie A, Razali SFM, Mohamad ZS (2014) Reservoir optimization in water resources: a review. Water Resour Manag 28:3391–3405. https://doi.org/10.1007/s11269-014-0700-5 CrossRef

Angira R, Santosh A (2007) Optimization of dynamic systems: A trigonometric differential evolution approach. Comput Chem Eng 31:1055–1063CrossRef

Arshi SS, Zolfaghari A, Mirvakili S (2014) A multi-objective shuffled frog leaping algorithm for in-core fuel management optimization. Comput Phys Commun 185:2622–2628CrossRef

Cheng C-T, Wang W-C, Xu D-M, Chau KW (2008) Optimizing hydropower reservoir operation using hybrid genetic algorithm and Chaos. Water Resour Manag 22:895–909. https://doi.org/10.1007/s11269-007-9200-1 CrossRef

Ding Z, Fang G, Wen X, Tan Q, Huang X, Lei X, Tian Y, Quan J (2018) A novel operation chart for cascade hydropower system to alleviate ecological degradation in hydrological extremes. Ecol Model 384:10–22

Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optimiz 38:129–154. https://doi.org/10.1080/03052150500384759 CrossRef

Fallah-Mehdipour E, Haddad OB, Mariño M (2011) MOPSO algorithm and its application in multipurpose multireservoir operations. J Hydroinf 13:794–811CrossRef

Fonseca CM, Fleming PJ, Zitzler E, Deb K, Thiele L (2003) Evolutionary multi-criterion Optimization In: Second International Conference, EMO 2003. Springer

Gao H, Cao J (2012) Membrane-inspired quantum shuffled frog leaping algorithm for spectrum allocation. J Syst Eng Electro 23:679–688CrossRef

Guo Y, Fang G, Wen X, Lei X, Yuan Y, Fu X (2018) Hydrological responses and adaptive potential of cascaded reservoirs under climate change in yuan river basin. Hydrol Res. https://doi.org/10.2166/nh.2018.165 (in press)

Hossain MS, El-Shafie A (2013) Intelligent systems in optimizing reservoir operation policy: a review. Water Resour Manag 27:3387–3407CrossRef

Ilonen J, Kamarainen J-K, Lampinen J (2003) Differential evolution training algorithm for feed-forward neural networks. Neural Process Lett 17:93–105. https://doi.org/10.1023/a:1022995128597 CrossRef

Karamouz M, Tabari MMR, Kerachian R (2007) Application of genetic algorithms and artificial neural networks in conjunctive use of surface and groundwater resources. Water Int 32:163–176CrossRef

Khosroshahi MT, Kazemi FM, Oskuee MRJ, Najafi-Ravadanegh S (2015) Coordinated and uncoordinated design of LFO damping controllers with IPFC and PSS using ICA and SFLA. J Cent South Univ 22:3418–3426CrossRef

Le-Anh L, Nguyen-Thoi T, Ho-Huu V, Dang-Trung H, Bui-Xuan T (2015) Static and frequency optimization of folded laminated composite plates using an adjusted differential evolution algorithm and a smoothed triangular plate element. Compos Struct 127:382–394. https://doi.org/10.1016/j.compstruct.2015.02.069 CrossRef

Li Y, Zhou J, Yang J, Liu L, Qin H, Yang L (2008) The Chaos-based shuffled frog leaping algorithm and its application. In: 2008 fourth international conference on natural computation, 18–20 Oct. 2008 . pp 481–485. doi:https://doi.org/10.1109/ICNC.2008.242

Li Y, Zhou J, Zhang Y, Qin H, Liu L (2010) Novel multiobjective shuffled frog leaping algorithm with application to reservoir flood control operation. J Water Resour Plan Manag 136:217–226CrossRef

Liu D, Guo S, Chen X, Shao Q, Ran Q, Song X, Wang Z (2012) A macro-evolutionary multi-objective immune algorithm with application to optimal allocation of water resources in Dongjiang River basins, South China. Stoch Env Res Risk A 26:491–507. https://doi.org/10.1007/s00477-011-0505-5 CrossRef

Lopez-Ibanez M, Prasad TD, Paechter B (2005) Multi-objective optimisation of the pump scheduling problem using SPEA2. In: Evol Comput. The 2005 IEEE Congress on, 2005. IEEE, pp 435–442

Luo J, Liu J (2014) An MILP model and clustering heuristics for LED assembly optimisation on high-speed hybrid pick-and-place machines. Int J Prod Res 52:1016–1031. https://doi.org/10.1080/00207543.2013.828173 CrossRef

Maier HR et al. (2014) Evolutionary algorithms and other metaheuristics in water resources: current status, research challenges and future directions Environm Model Software 62:271–299

Makaremi Y, Haghighi A, Ghafouri HR (2017) Optimization of pump scheduling program in water supply systems using a self-adaptive NSGA-II; a review of theory to real application. Water Resour Manag 31:1283–1304. https://doi.org/10.1007/s11269-017-1577-x CrossRef

Modiri-Delshad M, Rahim NA (2016) Multi-objective backtracking search algorithm for economic emission dispatch problem. Appl Soft Comput 40:479–494CrossRef

Orouji H, Haddad OB, Fallah-Mehdipour E, Mariño M (2013) Extraction of decision alternatives in project management: application of hybrid PSO-SFLA. J Manag Eng 30:50–59CrossRef

Pazoki M, Orouji H, Fallah-Mehdipour E, Biswas A, Mahmoudi N (2016) Shuffled frog-leaping algorithm for optimal design of open channels doi:https://doi.org/10.1061/(ASCE)IR.1943-4774.0001059

Rahimi-Vahed A, Dangchi M, Rafiei H, Salimi E (2009) A novel hybrid multi-objective shuffled frog-leaping algorithm for a bi-criteria permutation flow shop scheduling problem. Int J Adv Manuf Technol 41:1227–1239CrossRef

Scola LA, Takahashi RH, Cerqueira SA (2014) Multipurpose water reservoir management: an evolutionary multiobjective optimization approach Mathematical Problems in Engineering 2014

Siew C, Tanyimboh TT, Seyoum AG (2016) Penalty-free multi-objective evolutionary approach to optimization of Anytown water distribution network. Water Resour Manag 30:3671–3688. https://doi.org/10.1007/s11269-016-1371-1 CrossRef

Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359CrossRef

Sun P, Z-q J, T-t W, Y-k Z (2016) Research and application of parallel normal cloud mutation shuffled frog leaping algorithm in Cascade reservoirs optimal operation. Water Resour Manag 30:1019–1035. https://doi.org/10.1007/s11269-015-1208-3 CrossRef

Tayfur G (2017) Modern optimization methods in water resources planning, engineering and management. Water Resour Manag 31:3205–3233. https://doi.org/10.1007/s11269-017-1694-6 CrossRef

Wen X, Fang G, Guo Y, Zhou L (2016) Adapting the operation of cascaded reservoirs on Yuan River for fish habitat conservation. Ecological Modelling, 337:221-230. http://dx.doi.org/10.1016/j.ecolmodel.2016.06.018

Wen X, Liu Z, Lei X, Lin R, Fang G, Tan Q, Wang C, Tian Y, Quan J (2018) Future changes in Yuan River ecohydrology: Individual and cumulative impacts of climates change and cascade hydropower development on runoff and aquatic habitat quality. Sci Total Environ 633:1403–1417

Xiang Y, Zhou Y (2015) A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization. Appl Soft Comput 35:766–785CrossRef

Yenisey MM, Yagmahan B (2014) Multi-objective permutation flow shop scheduling problem: literature review, classification and current trends. Omega 45:119–135. https://doi.org/10.1016/j.omega.2013.07.004 CrossRef

Yu R-F, Wang W, Cheng W-P, Chen M-M (2015) Erratum to: on-line evaluating the SS removals for chemical coagulation using digital image analysis and artificial neural networks. Int J Environ Sci Technol 12:421–421. https://doi.org/10.1007/s13762-014-0676-y CrossRef

Zhang W, Hu Y, He H, Liu Y, Chen A (2017) Linear and dynamic programming algorithms for real-time task scheduling with task duplication. J Supercomput. https://doi.org/10.1007/s11227-017-2076-9

Zhao Z, Yang J, Hu Z, Che H (2016) A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric Latin hypercube design for unconstrained optimization problems. Eur J Oper Res 250:30–45CrossRef

Zhu X, Zhang C, Fu G, Li Y, Ding W (2017) Bi-level optimization for determining operating strategies for Inter-Basin water transfer-supply reservoirs. Water Resour Manag 31:4415–4432. https://doi.org/10.1007/s11269-017-1756-9 CrossRef

Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8:173–195CrossRef

Titel: Multi-Objective Differential Evolution-Chaos Shuffled Frog Leaping Algorithm for Water Resources System Optimization
verfasst von: Guohua Fang
Yuxue Guo
Xin Wen
Xiaomin Fu
Xiaohui Lei
Yu Tian
Ting Wang
Publikationsdatum: 21.06.2018
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 12/2018
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-018-2021-6

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