水库单目标优化调度技术比较研究
Comparative Study on Single-objective Optimization Algorithms for Reservoir Operation
DOI: 10.12677/JWRR.2013.25040, PDF, HTML,    国家科技经费支持
作者: 刘心愿*, 朱勇辉, 郭小虎：长江水利委员会长江科学院，武汉
关键词: 水库调度；逐次优化算法；遗传算法；差分进化算法；约束处理；Reservoir Operation; POA; GA; DE; Constraint Handling
摘要: 优化算法的选择是水库优化调度中的一个难点。本文选取了传统优化算法POA、智能优化算法GA和DE等，从变量规模、算子选择、参数选定、约束处理方法等方面对算法在水库优化调度中的性能进行了深入的比较研究。结果表明：智能优化算法在水库优化调度中具有一定的适用性，不同算法的搜索效率差异较大，需要根据问题和算法特点对算子和参数进行精心设计和选择。对于变量较少的小规模水库优化调度问题，通过选取合适的算子和参数，智能优化算法GA和DE从耗时等方面会优于传统优化技术POA；但对于变量较多的复杂水库优化调度问题，POA在耗时和解的质量等方面仍然具有显著的优势。研究成果可为水库优化调度优化算法的选择提供参考。
Abstract: Selection of optimal algorithms is one of the most complex problems for reservoir operation. Progressive optimality algorithm (POA), genetic algorithm (GA) and differential evolution algorithm (DE) were selected in this paper, and the performance of these algorithms were compared from the aspects of the number of decision variables, selection of arithmetic operators, determination of parameter values, constraint handling, etc. Results show that modern intelligent algorithms were applicable to reservoir operation optimization with big differences in performance for different intelligent algorithms. So it is necessary to select appropriate operators and parameters when using modern intelligent algorithms in reservoir operation. GA and DE with proper operators and parameters may have an advantage over POA in performance for reservoir operation problems with less decision variables, but POA is still superior to GA and DE for complex reservoir operation problems with large number of decision variables. This study helps to select proper optimization algorithms and parameter values for reservoir operation.
文章引用：刘心愿, 朱勇辉, 郭小虎. 水库单目标优化调度技术比较研究[J]. 水资源研究, 2013, 2(5): 281-287. http://dx.doi.org/10.12677/JWRR.2013.25040

参考文献

[1] 刘攀, 郭生练, 李玮, 易松松. 遗传算法在水库调度中的应用综述[J]. 水利水电科技进展, 2006, 26(4): 78-83. LIU Pan, GUO Shenglian, LI Wei and YI Songsong. A review of application of genetic algorithm to reservoir operation. Ad- vances in Science and Technology of Water Resources, 2006, 26(4): 78-83. (in Chinese)
[2] 刘攀, 郭生练, 雒征, 等. 求解水库优化调度问题的动态规划–遗传算法[J]. 武汉大学学报(工学版), 2007, 40(5): 1-6. LIU Pan, GUO Shenglian, LUO Zheng, et al. Optimization of reservoir operation by using dynamic programming & genetic algorithm. Engineering Journal of Wuhan University, 2007, 40(5): 1-6. (in Chinese)
[3] HOWSON, H.R., SANCHO, N.G.F. A new algorithm for the solution of multi-state dynamic programming problems. Mathe- matical Programming, 1975, 8(1): 104-116.
[4] 周佳, 马光文, 张志刚. 基于改进POA算法的雅砻江梯级水电站群中长期优化调度研究[J]. 水力发电学报, 2010, 29(3): 18-22. ZHOU Jia, MA Guangwen and ZHANG Zhigang. Study on the mid-long term optimal dispatching of cascaded hydropower sta- tions on Yalong River based on POA modified adaptive algo- rithm. Journal of Hydroelectric Engineering, 2010, 29(3): 18-22. (in Chinese)
[5] 宗航, 周建中, 张勇传. POA改进算法在梯级电站优化调度中的研究和应用[J]. 计算机工程, 2003, 29(17): 105-109. ZONG Hang, ZHOU Jianzhong and ZHANG Yongchuan. Re- search and application for cascaded hydroelectric optimized scheduling based on modified adaptive POA. Computer Engi- neering, 2003, 29(17): 105-109. (in Chinese)
[6] SPLENDY, W., HEXT, G.R. and HIMSWORTH, F.R. Sequential application of simplex design in optimization and evolutionary design. Technometrics, 1962, 4: 441-461.
[7] NELDER, J.A., MEAD, R. A simplex method for function mini- mization. Computer Journal, 1965, 7: 308-313.
[8] HOLLAND, J. Adaptation in natural and artificial systems. Ann Arbour: The University of Michigan Press, 1975.
[9] TAKAHASHI, M., KITA, H. A crossover operator using inde- pendent component analysis for real-coded genetic algorithm. Proceedings of the 2001 Congress on Evolutionary Computation, 2001: 643-649.
[10] DEB, K., AGRAWAL, R.B. Simulated binary crossover for continuous search space. Complex Systems, 1995, 9(6): 115- 148.
[11] ONO, I., KITA, H. and KOBAYASHI, S. A robust real-coded genetic algorithm using unimodal normal distribution crossover augmented by uniform crossover: effects of self-adaptation of crossover probabilities. Proceedings of the Genetic and Evolu- tionary Computation Conference, San Mateo: Morgan Kaufmann Publishers, 1999: 496-503.
[12] TSUTSUI, S., YAMAMURA, M. and HIGUCHI, T. Multi- parent recombination with simplex crossover in real coded ge- netic algorithms. Proceedings of the Genetic and Evolutionary Computation Conference, San Mateo: Morgan Kaufmann Pub- lishers, 1999: 657-664.
[13] MICHALEWICZ, Z. Genetic Algorithms + Data Structures = Evolution Programs. New York: Springer-Verlag, 1996.
[14] PRICE, K.V., STORN, R. Differential evolution—A simple evolution strategy for fast optimization. Dr. Dobb’s Journal, 1997, 22: 18-24.
[15] HOMAIFAR, A., LAI, S. and QI, X. Constrained optimization via genetic algorithms. Simulation, 1994, 62(4): 242-254.