Water Science and Engineering 2020, 13(2) 136-144 DOI:   https://doi.org/10.1016/j.wse.2020.06.005  ISSN: 1674-2370 CN: 32-1785/TV

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Particle swarm optimization
Genetic algorithm
Random inertia weight
Multi-objective reservoir operation
Reservoir group
Panjiakou Reservoir

Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights

Hai-tao Chen a, Wen-chuan Wang a, *, Xiao-nan Chen b, Lin Qiu a

a School of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
b Construction and Administration Bureau of South-to-North Water Diversion Middle Route Project, Beijing 100038, China


Based on conventional particle swarm optimization (PSO), this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight (ARIW) strategy, referred to as the ARIW-PSO algorithm, to build a multi-objective optimization model for reservoir operation. Using the triangular probability density function, the inertia weight is randomly generated, and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution, which is suitable for global searches. In the evolution process, the inertia weight gradually decreases, which is beneficial to local searches. The performance of the ARIW-PSO algorithm was investigated with some classical test functions, and the results were compared with those of the genetic algorithm (GA), the conventional PSO, and other improved PSO methods. Then, the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China, including the Panjiakou Reservoir, Daheiting Reservoir, and Taolinkou Reservoir. The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.

Keywords Particle swarm optimization   Genetic algorithm   Random inertia weight   Multi-objective reservoir operation   Reservoir group   Panjiakou Reservoir  
Received 2019-08-25 Revised 2020-02-01 Online: 2020-06-30 
DOI: https://doi.org/10.1016/j.wse.2020.06.005

This work was supported by the Foundation of the Scientific and Technological Innovation Team of Colleges and Universities in Henan Province (Grant No. 181RTSTHN009), and the Foundation of the Key Laboratory of Water Environment Simulation and Treatment in Henan Province (Grant No. 2017016).

Corresponding Authors: Wen-chuan Wang
Email: zzlwxz@126.com
About author:


Bai, T., Wu, L.Z., Chang, J.X., Huang, Q., 2015. Multi-objective optimal operation model of cascade reservoirs and its application on water and sediment regulation. Water Resources Management  29(3), 2751-2770. https://doi.org/10.1007/s11269-015-0968-0.

Chang, L.C., 2008. Guiding rational reservoir flood operation using penalty-type genetic algorithm. Journal of Hydrology 354(1-4), 65-74. https://doi.org/10.1016/j.jhydrol.2008.02.021.

Chang, L.C., Chang, F.J., 2009. Multi-objective evolutionary algorithm for operating parallel reservoir system. Journal of Hydrology. 377(1-2), 12-20. https:// doi.org/10.1061/j.jhydrol.2009.07.061.

Chang, L.C., Chang, F.J., Wang, K.W., Dai, S.Y., 2010. Constrained genetic algorithms for optimizing multi-use reservoir operation. Journal of Hydrology 390(1-2), 66-74. https://doi.org/10.1016/j.jhydrol.2010.06.031.

Chang, J.X., Bai, T., Huang, Q., Yang, D.W., 2013. Optimization of water resources utilization by PSO-GA. Water Resources Management 27(4), 3525-3540. https://doi.org/10.1007/s11269-013-0362-8.

Chaves, P., Chang, L.C., 2008. Intelligent reservoir operation system based on evolving neural networks. Advances in Water Resources 31(6), 926-936. https://doi.org/10.1016/j.advwatres.2008.03.002.

Chen, X.N., Duan, C.Q., Qiu, L., Huang, Q., 2008. Application of large scale system model base on particle swarm optimization to optimal allocation of water resources in irrigation areas. Transactions of the CSAE. 24 (3), 103-106 (in Chinese). 

Cheng, C.T., Wang, W.C., Wu, X.Y., Xu, D.M., Chau, K.W., 2008. Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos. Water Resources Managment 22 (7), 895-909. https://doi.org/10.1007/s11269-007-9200-1.

Dorigo, M., Maniezzo, V., Colorni, A., 1996. Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 26(1), 29-41. https://doi.org/10.1109/3477.484436.

Eberhart, D.E., Kennedy, J., 1995. A new optimizer using particle swarm theory. In: Proceedings of the 6th Symposium on Micro Machine and Human Science. IEEE Service Center, Piscataway.

Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wiley, Reading.

Hadka, D., Reed, P., 2013. Borg: An auto-adaptive many-objective evolutionary computing framework. Evolutionary Computation 21(2), 231-259. https://doi.org/10.1162/EVCO_a_00075.

Huang, W.C., Yuan, L.C., 2004. A drought early warning system on real-time multi-reservoir operation. Water Resources Research 40(6), W06401. https://doi.org/10.1029/2003WR002910.

Jothiprakash, V., Arunkumar, R., 2013. Optimization of hydropower reservoir using evolutionary algorithms coupled with chaos. Water Resources Management 27(7), 1963-1979. https://doi.org/10.1007/s11269-013-0265-8.

Kang, L., Zhou, L.W., Li, Z.H., Hui, L.Y., 2019. Nonlinear safety degree flood control strategy of multi-reservoirs in upper Yangtze River. Advances in Science and Technology of Water Resources 39(3), 1-5 (in Chinese). https://doi.org/10.3880/j.issn.1006-7647.2019.03.001.

Kong, A.L., Liang, S., Li, C.L., Liang, Z.F., Chen, Y., 2017. Optimizing micro-grid operation based on improved PSO. Journal of Hohai University (Natural Sciences) 45(6), 550-555 (in Chinese). https://doi.org/10.3876/j.issn.1000-1980.2017.06.012.

Kumar, D.N., Reddy, M.J., 2007. Multipurpose reservoir operation using particle swarm optimization. Journal of Water Resources Planning and Management. 133(3), 192-201. https://doi.org/10.1061/(ASCE)0733-9496(2007)133:3(192).

Labadie, J.W., 2004. Optimal operation of multi-reservoir systems: State-of-the art-review. Journal of Water Resources Planning and Management 130(2), 93-111.

Li, F.F., Christine, A.S., Qiu, J., Wei, J.H., 2015. Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system. Environmental  Modeling & Software. 69, 319-329. https://doi.org/10.1016/j.envsoft.2014.11.030.

Liu, P., Cai, X.M., Guo, S.L., 2011. Deriving multiple near-optimal solutions to deterministic reservoir operation problems. Water Resources Research 47, W08506. https://doi.org/10.1029/2011WR010998.

Ma, C.H., Li, Y., Huang, Q., Li, F., 2018. Parallel particle swarm optimization algorithm based on Spark multi-objective optimal scheduling of reservoir group. Journal of Xian University of Technology 34(3), 309-313 (in Chinese). https://doi.org/10.19322/j.cnki.issn.1006-4710.2018.03.010.

Mao, J.Q., Tian, M.M., Hu, T.F., Ji, K., Dai, L.Q., Dai, H.C., 2019. Shuffled complex evolution coupled with stochastic ranking for reservoir scheduling problems. Water Science and Engineering 12(4), 307-318. https://doi.org/10.1016/j.wse.2019.12.003.

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.

Oliveira, R., Loucks, D.P., 1997. Operating rules for multi-reservoir systems. Water Resources Research 33(4), 839-852. https://doi.org/10.1029/96WR03745.

Peng, Y., Xue, Z.C., 2011. Generalized ant colony optimization method for optimal operation of cascade reservoirs. Water Resources and Power 29(4), 48-50 (in Chinese).

Salazar, J.Z., Reed, P.M., Quinn, J.D., Giuliani, M., Castelletti, A., 2017. Balancing exploration, uncertainty and computational demands in many objective reservoir optimization. Advances in Water Resources 109, 196-210. https://doi.org/10.1016/j.advwatres.2017.09.014.

Shi, Y., Eberhart, R.C., 1998. A modified particle swarm optimizer. In: Proceedings of the 1998 IEEE International Conference on Evolutionary Computatio. IEEE Press, pp. 69-73. https://doi.org/10.1109/ICEC.1998.699146.

Vrugt, J.A., Robinson, B.A., Hyman, J.M., 2009. Self-adaptive multimethod search for global optimization in real-parameter spaces. IEEE Transactions on Evolutionary Computation 13(2), 243-259. https://doi.org/10.1109/TEVC.2008.924428.

Wardlaw, R, Sharif, M., 1999. Evaluation of genetic algorithms for optimal reservoir system operation. Journal of Water Resources Planning and Management  125(1), 25-33. https://doi.org/10.1061/(ASCE)0733-9496(1999)125:1(25).

Xu, G., Ma, G.W., Liang, W.H., Chen, J.C., Wu S.Y., 2005. Application of ant colony algorithm to reservoir optimal operation. Advanced in Water Science 16(3), 397-400 (in Chinese).

Yang, G., Guo, S.L., Li, L.P., Hong, X.J., Wang, L., 2016a. Multi-objective operating rules for Danjiangkou Reservoir under climate change. Water Resources Management 30, 1183-1202. https://doi.org/10.1007/s11269-015-1220-7.

Yang, G., Guo, S.L., Liu, P., Li, L.P., Liu, Z.J., 2016b. PA-DDS algorithm for multi-objective reservoir operation. Journal of Hydraulic Engineering 47, 789-797 (in Chinese). https://doi.org/10.13243/j.cnki.slxb.20150773.

Yeh, W.W.G., 1985. Reservoir management and operations models: A state-of-the-art review. Water Resources Research 21(12), 1797-1818. https://doi.org/10.1029/WR021i012p01797.

Zhan, Z.H., Zhang, J., Li, Y., Chung, H.S.H., 2009. Adaptive particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39(6),1362-1380. https://doi.org/10.1109/TSMCB.2009.2015956.

Zhang, Z.B., He, X.Y., Geng, S.M., Li, H., Zhang, D.W., Jiang, X.M., 2017. The application of improved particle swarm algorithm to reservoir operation optimization. Journal of China Institute of Water Resources and Hydropower Research 15(5), 338-345 (in Chinese).

Zhao, J.S., Cai, X.M., Wang, Z.J., 2011. Optimality conditions for a two-stage reservoir operation problem. Water Resources Research 47(8), W08503. https://doi.org/10.1029/2010WR009971.

Zhao, T.T.G., Zhao, J.S., 2014. Improved multiple-objective dynamic programming model for reservoir operation optimization. Journal of Hydroinformatics. 16(5), 1142-1157. https://doi.org/10.2166/hydro.2014.004.

Zheng J., Yang, K., Ni, F.Q., Liu, G.S., 2013. Research on overall improved genetic algorithm applied in optimal operation. Journal of Hydraulic Engineering 44(2), 205-211 (in Chinese). 

Zhu, D.G., Sun, H., Zhao, J., Yu, Q., 2014. Particle swarm optimization algorithm based on Gaussian disturbance. Journal of Computer Applications 34(3), 754-759 (in Chinese). https://doi.org/10.11772/j.issn.1001-9081.2014.03.0754.

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