共查询到18条相似文献,搜索用时 214 毫秒
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遗传模拟退火和小生境遗传算法在水库优化调度中的比较 总被引:1,自引:0,他引:1
根据溪洛渡水库的具体情况,建立了以发电量最大为目标的水库优化调度非线性数学模型,并利用遗传模拟退火算法(GSA)和小生境遗传算法(NGA)分别求解模型.结果表明,GSA和NGA的收敛速度和计算结果都明显优于基本遗传算法;且两者相比,GSA的收敛性更强,但计算时间较长.而在求解水库长系列优化调度问题时,各遗传算法占用机时太多,且收敛能力较差. 相似文献
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简要介绍粒子群算法的工作原理和水库(群)优化调度模型,然后较全面地阐述粒子群算法在水库(群)优化调度中的应用及存在的问题,最后总结了算法的各种改进,并对粒子群算法在水库(群)中的研究进行了展望。 相似文献
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文中对传统粒子群算法进行改进,提高其优化算法的收敛度,并结合改进粒子群算法对观音阁水库生态调度进行优化计算。研究结果表明,采用改进粒子群算法的水库生态调度优化求解精度得到明显改善,不同月份观音阁水库下游河道最小和适宜生态径流满足度相比于改进前显著提高。研究成果对于水库生态优化调度算法具有重要参考价值。 相似文献
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YANG Juxiang 《水资源与水工程学报》2011,22(2):164-167
建立了九甸峡水库优化调度模型进行实例分析,并应用改进粒子群优化算法(MPSO)对模型求解。经计算,对于中水年情况,九甸峡水库可满足上下游需水要求,并可比原设计情况多发电0.72亿kW.h,比经典动态规划法多发电0.07亿kW.h,从而验证了改进粒子群算法对九甸峡水库优化调度模型的合理性和优越性。 相似文献
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以发电量和保证出力为目标,建立水库优化调度多目标数学模型,将遗传算法的交叉思想应用到微粒群算法中,尝试应用多目标交叉微粒群算法(multi-objective Hybrid Particle swarm Optimization--multi-objective HPSO)来求解水库调度中的多目标优化模型的不劣解集.通过实例研究计算并与其他算法的优化结果进行比较分析,证明交叉微粒群算法具有灵活和有效性能好的特性. 相似文献
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梯级水电站调度图优化的混合模拟退火遗传算法 总被引:1,自引:0,他引:1
为提高水库群联合调度时的水资源利用率,重新审核水库群系统中原有单库调度图的有效性,本文提出了一种解决库群联合调度多目标、多变量的智能优化新方法—混合模拟退火遗传算法。该方法将遗传算法的全局搜索能力和模拟退火算法的局部搜索能力相结合,提高了计算效率和精度,避免了手工修正调度图的随意性。在以实际生产项目为依托的应用与检验中,在满足各类边界条件及保证率要求的前提下,该方法对梯级水电站水库调度图的优化可行、有效,为优化梯级水库调度图提供了一种新的有效算法。 相似文献
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基于改进遗传算法的串联小水电群优化调度 总被引:10,自引:3,他引:7
建立了由沙畈水库和金兰水库组成的串联小水电群优化调度的数学模型,采用了改进遗传算法对该模型进行优化计算。算法设计编程简单、计算工作量小、收敛速度快。利用两个水库的入库径流实测值进行了仿真实验,结果说明优化调度能比常规调度取得更大的经济效益,同时也说明了遗传算法是求解小水电群优化调度的可行而有效的方法。 相似文献
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简要回顾了遗传算法在水库调度中的应用概况,对遗传算法用于水库调度优化时的编码、约束条件处理、早熟与全局收敛性、参数设置、混合遗传算法、多目标遗传算法以及效率评定准则等问题进行了综述。分析遗传算法耗时与全局收敛之间的矛盾后认为,遗传算法适用于传统方法难以求解的优化问题,以及对计算时效性要求不高或者目标函数计算复杂度不高的实时水库调度问题,特别是水库中长期调度以及水资源规划问题。 相似文献
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Mohammad Azizipour Vahid Ghalenoei M. H. Afshar S. S. Solis 《Water Resources Management》2016,30(11):3995-4009
The optimal hydropower operation of reservoir systems is known as a complex nonlinear nonconvex optimization problem. This paper presents the application of invasive weed optimization (IWO) algorithm, which is a novel evolutionary algorithm inspired from colonizing weeds, for optimal operation of hydropower reservoir systems. The IWO algorithm is used to optimally solve the hydropower operation problems for both cases of single reservoir and multi reservoir systems, over short, medium and long term operation periods, and the results are compared with the existing results obtained by the two most commonly used evolutionary algorithms, namely, particle swam optimization (PSO) and genetic algorithm (GA). The results show that the IWO is more efficient and effective than PSO and GA for both single reservoir and multi reservoir hydropower operation problems. 相似文献
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Chun-Tian Cheng Wen-Chuan Wang Dong-Mei Xu K. W. Chau 《Water Resources Management》2008,22(7):895-909
Genetic algorithms (GA) have been widely applied to solve water resources system optimization. With the increase of the complexity
and the larger problem scale of water resources system, GAs are most frequently faced with the problems of premature convergence,
slow iterations to reach the global optimal solution and getting stuck at a local optimum. A novel chaos genetic algorithm
(CGA) based on the chaos optimization algorithm (COA) and genetic algorithm (GA), which makes use of the ergodicity and internal
randomness of chaos iterations, is presented to overcome premature local optimum and increase the convergence speed of genetic
algorithm. CGA integrates powerful global searching capability of the GA with that of powerful local searching capability
of the COA. Two measures are adopted in order to improve the performance of the GA. The first one is the adoption of chaos
optimization of the initialization to improve species quality and to maintain the population diversity. The second is the
utilization of annealing chaotic mutation operation to replace standard mutation operator in order to avoid the search being
trapped in local optimum. The Rosenbrock function and Schaffer function, which are complex and global optimum functions and
often used as benchmarks for contemporary optimization algorithms for GAs and Evolutionary computation, are first employed
to examine the performance of the GA and CGA. The test results indicate that CGA can improve convergence speed and solution
accuracy. Furthermore, the developed model is applied for the monthly operation of a hydropower reservoir with a series of
monthly inflow of 38 years. The results show that the long term average annual energy based CGA is the best and its convergent
speed not only is faster than dynamic programming largely, but also overpasses the standard GA. Thus, the proposed approach
is feasible and effective in optimal operations of complex reservoir systems. 相似文献
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Genetic Algorithms for Optimal Reservoir Dispatching 总被引:4,自引:2,他引:2
The fundamental guidelines for genetic algorithm to optimal reservoir dispatching have been introduced. It is concluded that
with three basic generators selection, crossover and mutation genetic algorithm could search the optimum solution or near-optimal
solution to a complex water resources problem. Alternative formulation schemes of a GA are considered. The real-value coding
is proved significantly faster than binary coding, and can produce better results. Sensitivity of crossover probability and
mutation probability are also analyzed in this paper. Results from genetic algorithm with real-value coding are compared with
those from other optimal methods. The results demonstrate that a genetic algorithm can be satisfactorily used in optimal reservoir
problems, and it has potential in application to complex river systems. 相似文献
14.
基于改进遗传算法的小型水电站短期优化调度 总被引:6,自引:2,他引:4
针对小型水电站在丰水期的短期优化调度问题,提出了短期优化调度的数学模型和基于改进遗传算法的工程实现方法,并通过实例仿真及对仿真结果的详细分析,说明了该算法的有效性,对小型水电站短期优化调度有一定的指导意义。 相似文献
15.
Mohammad Ehteram Hojat Karami Sayed Farhad Mousavi Saaed Farzin Alcigeimes B. Celeste Ahmad-El Shafie 《Water Resources Management》2018,32(14):4681-4706
This article shows an application of a new algorithm, called kidney algorithm, for reservoir operation which employs three different operators, namely filtration, secretion, and excretion that lead to faster convergence and more accurate solutions. The kidney algorithm (KA) was used for generating the optimal operation of a reservoir namely; Aydoghmoush dam in eastern Azerbaijan province in Iran whose purpose was to decrease irrigation deficit downstream of the dam. Results from the algorithm were compared with those by other evolutionary algorithms, including bat (BA), genetic (GA), particle swarm (PSO), shark (SA), and weed algorithms (WA). The results showed that the kidney algorithm provided the best performance against the other evolutionary algorithms. For example, the computational time for the KA was 3 s, 2 s, 4 s, 6 s and 3 s less than BA, SA, GA PSA and WA, respectively. Also, the objective function for the optimization problem was the minimization of the irrigation deficits and its value for the KA was 55%, 28%, 52%, 44 and 54% less than GA, SA, WA, BA and PSA, respectively. Also, the different performance indexes showed the superiority of the KA compared to the other algorithms. For example, the root mean square error for the KA was 74%, 61%, 68%, 33 and 54% less than GA, SA, WA, BA and PSA, respectively. Different multi criteria decision models were used to select the best models. The results showed that the KA achieved the first rank for the optimization problem and thus, it shows a high potential to be applied for different problems in the field of water resources management. 相似文献
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崔东文 《水利水电科技进展》2017,37(3):72-76
为验证鲸鱼优化算法在水库优化调度求解中的可行性和有效性,采用4个典型测试函数对鲸鱼优化算法进行仿真验证,并与布谷鸟搜索算法、差分进化算法、混合蛙跳算法、粒子群优化算法、萤火虫算法和SCE-UA算法共6种算法的仿真结果进行对比分析;将鲸鱼优化算法与6种对比算法应用于某单一水库和某梯级水库中长期优化调度求解。结果表明:鲸鱼优化算法寻优精度高于其他6种算法8个数量级以上,具有收敛速度快、收敛精度高和极值寻优能力强等特点;鲸鱼优化算法单一水库和梯级水库优化调度结果均优于其他6种算法;鲸鱼优化算法应用于水库优化调度求解是可行和有效的。 相似文献
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Water Supply Reservoir Operation by Combined Genetic Algorithm – Linear Programming (GA-LP) Approach
Multi-reservoir operation planning is a complex task involving many variables, objectives, and decisions. This paper applies
a hybrid method using genetic algorithm (GA) and linear programming (LP) developed by the authors to determine operational
decisions for a reservoir system over the optimization period. This method identifies part of the decision variables called
cost reduction factors (CRFs) by GA and operational variables by LP. CRFs are introduced into the formulation to discourage
reservoir depletion in the initial stages of the planning period. These factors are useful parameters that can be employed
to determine operational decisions such as optimal releases and imports, in response to future inflow predictions. A part
of the Roadford Water Supply System, UK, is used to demonstrate the performance of the GA-LP method in comparison to the RELAX
algorithm. The proposed approach obtains comparable results ensuring non zero final storages in the larger reservoirs of the
Roadford Hydrosystem. It shows potential for generating operating policy in the form of hegging rules without a priori imposition
of their form. 相似文献
18.
This paper presents a Genetic Algorithm (GA) model for finding the optimal operating policy of a multi-purpose reservoir, located on the river Pagladia, a major tributary of the river Brahmaputra. A synthetic monthly streamflow series of 100 years is used for deriving the operating policy. The policies derived by the GA model are compared with that of the stochastic dynamic programming (SDP) model on the basis of their performance in reservoir simulation for 20 years of historic monthly streamflow. The simulated result shows that GA-derived policies are promising and competitive and can be effectively used for reservoir operation. 相似文献