基于遗传算法的重力坝整体优化设计

针对基本遗传算法中交叉概率与变异概率随进化过程恒定不变的缺点，引用自适应遗传算法，并将其运用于重力坝整体断面体型优化设计。通过实例计算并与复行法的计算结果相比，该算法能得到更好的优化结果；优化的体型可直接用于工程的初步设计。     

基于自适应遗传算法的重力坝体型优化设计

针对简单遗传算法中交叉概率与变异概率随进化过程恒定不变的缺点,提出了自适应遗传算法并将其用于重力坝体型优化设计。实例计算并与复行法优化方法相比,该算法不仅能得到更好的优化结果,而且保持了较快的收敛速度。     

Design of Water Distribution Networks using a Pseudo-Genetic Algorithm and Sensitivity of Genetic Operators

Genetic algorithms (GA) are optimization techniques that are widely used in the design of water distribution networks. One of the main disadvantages of GA is positional bias, which degrades the quality of the solution. In this study, a modified pseudo-genetic algorithm (PGA) is presented. In a PGA, the coding of chromosomes is performed using integer coding; in a traditional GA, binary coding is utilized. Each decision variable is represented by only one gene. This variation entails a series of special characteristics in the definition of mutation and crossover operations. Some benchmark networks have been used to test the suitability of a PGA for designing water distribution networks. More than 50,000 simulations were conducted with different sets of parameters. A statistical analysis of the obtained solutions was also performed. Through this analysis, more suitable values of mutation and crossover probabilities were discovered for each case. The results demonstrate the validity of the method. Optimum solutions are not guaranteed in any heuristic method. Hence, the concept of a “good solution” is introduced. A good solution is a design solution that does not substantially exceed the optimal solution that is obtained from the simulations. This concept may be useful when the computational cost is critical. The main conclusion derived from this study is that a proper combination of population and crossover and mutation probabilities leads to a high probability that good solutions will be obtained.     

改进的遗传算法在水库调度中的应用

为了改善遗传算法在水库优化调度中的应用效果,采用自适应遗传算法和广度变异模块相结合的分层收敛算法:第一层采用广度变异和外部存档的方式改善种群的多样性;第二层嵌套广度变异模块,并采用自适应遗传算法进行全局搜索。通过比较自适应遗传算法和分层进化算法,结果显示:基于遗传算法的分层算法具有高效的全局搜索能力,避免了自适应遗传算法陷入局部最优的缺陷,在一定收敛条件下得到了更接近全局最优的目标值。     

混合智能算法及其在供水水库群优化调度中的应用

将遗传算法中的进化思想和蚁群算法中的群体智能技术有效地耦合,提出了一种基于两者的混合智能算法,应用于供水水库群系统的优化调度研究中。算法利用蚁群算法的并行性、正反馈性以及良好的全局寻优能力,避免搜索陷入局部最优,同时借鉴遗传算法的进化思想,利用杂交、变异算子来进行局部寻优,使其能快速搜索到全局最优点。在种群随机搜索过程中嵌入确定性的模式搜索,使得算法同时具有随机性和确定性。结合模拟退火思想,构造了罚因子处理约束条件,使该算法对水库优化调度问题以及其他优化问题具有一定的通用性。通过实例验证,并与大系统聚合分解经典算法进行比较,结果表明该算法是可行的和有效的。     

水电站水库长期优化调度模型及调度图

为绘制高效可靠的水库运行调度图,以平衡保证出力保证率与发电量矛盾的惩罚系数为优化变量、以保证出力设计保证率满足条件下发电量最大为目标函数,综合集成以黄金分割法为时段决策优选法的随机动态规划核心模型,以及评估调度方案优劣时历法长系列模拟计算模块,利用遗传算法的并行计算能力,结合电站调度方案制定与有效性检验,构建水电站水库长期优化调度模型。应用结果表明:所建模型具有不受年调节和多年调节库容机械划分约束、快速获得满足发电保证率所要求的优化调度图的优秀特性;较之常规调度方法,可增发电量2.0%以上,保证率更高,决策信息更丰富。     

Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos

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.     

考虑风电机组旋转备用的风电场多目标优化

随着新型风电场的容量在并网系统中所占比例不断增加,对传统含风电场电力系统的经济调度问题提出了新的要求。传统的电力系统经济调度还应考虑到风电场功率波动对系统旋转备用和发电成本等方面的影响。因此,针对考虑风电机组旋转备用的风电场优化调度方法,并综合发电成本及购电费用目标,建立了含风电场的经济调度模型;釆用目标加权法和距离函数法相结合以将目标优化问题单一化以及利用实值编码遗传算法,来高效搜索优化方程最优解。仿真结果表明,所提出的方法是有效的。     

An Improved Genetic Algorithm-Simulated Annealing Hybrid Algorithm for the Optimization of Multiple Reservoirs

A hybrid evolutionary search algorithm is developed to optimize the classical single-criterion operation of multi-reservoir systems. The proposed improved genetic algorithm-simulated annealing (IGA-SA) which combines genetic algorithms (GAs) and the simulated annealing (SA) is a new global optimization algorithm. The algorithm is capable of overcoming the premature convergence of GAs and escaping from local optimal solutions. In addition, it is faster than a traditional unimproved GA-SA algorithm. A case study of optimization operation on generation electricity of a 3-reservoir system in series over 41-year (from May 1940 to April 1981) time periods in Wujiang River, one branch of Yangtze River in China, was performed. The objective is to maximize generation output from the system over each 12-month operating periods. Trade-off analyses on binary coding representation and real-value coding representation of GAs are performed. Sensitivity to some parameters of the GA, the SA and the IGA-SA is analyzed, respectively, and the appropriate values of parameters are suggested. The performance of the proposed algorithm is compared with that of the existing genetic algorithm, the simulated annealing and the dynamic programming (DP). Results demonstrate that the GA is better than the DP, the SA performs better than the GA and the IGA-SA is more efficient than SA. The IGA-SA produces higher quality solutions and costs less computation time compared with the traditional GA-SA. The results obtained from these applications have proved that the IGA-SA has the ability of addressing large and complex problems and is a new promising search algorithm for multi-reservoir optimization problems.     

改进蚁群算法及其在水利工程项目管理中的应用

为了更加有效解决水利工程项目管理中的多目标决策问题,提出了一种改进蚁群算法。该算法首先利用遗传算法的全局搜索能力将信息素初始化,然后在算法进行遍历过程中引入变异操作和交叉操作,提高算法的鲁棒性和有效性。水利工程项目多目标优化案例分析表明,较传统遗传算法和蚁群算法,本文提出的方法对于解的寻找速度更快,解的质量更高,该算法具有较高的全局寻优能力。该研究为水利工程项目管理多目标决策问题的解决提供了一种新的思路和方法。     

对流-扩散方程源项识别反问题的遗传算法

给出了利用遗传算法求解对流一扩散方程源项识别反问题的一种新方法。该方法把源项反问题转化为优化问题，用遗传算法求解。它的特点在于：从多个初始点开始寻优．并借助交叉，变异算子来获得全局最优解。实例模拟结果表明，该方法具有精度高，收敛速度快且易于计算机实现等特点。     

基于整数编码遗传算法的树状灌溉管网优化设计方法

针对树状管网布置中较多依赖设计人员经验的特点,提出了一种基于整数编码遗传算法的树状管网两级优化方法.第一级优化是根据树状管网单点供水的原则,建立了融合工程设计经验的树状管网优化布置整数编码遗传算法模型,克服了传统二进制编码方法易产生不可行解的问题,可快速寻找出一组符合工程实际情况的管网布置方案.第二级优化在确定管网布置方案组的基础上采用整数编码的遗传算法,以投资最小为目标,建立了管径优化模型与算法.编制了灌溉管网两级优化设计Matlab程序,进行了工程实例验证.与单亲遗传算法(SPGA)和管网布置经验设计方法进行了比较,表明本文提出的基于整数编码的管网优化设计方法可方便地将设计经验融合到优化计算过程中.能降低管网优化设汁的复杂性和求解难度,快速有效求解符合工程实际的管网优化方案.     

Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm

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.     

遗传算法在暴雨强度公式参数优化中的应用及Matlab实现

介绍了遗传算法在实现暴雨强度公式优化问题中的应用原理，给出了在Matlab语言环境下实现编码、译码、选择、重组和变异各算子的编程方法，通过比较说明遗传算法在寻求最优解的优越性。     

改进风驱动优化算法在水库调度中的应用

风驱动算法是一种新兴的基于群体迭代启发式的全局优化算法,与遗传算法、布谷鸟算法等相比,具有明确的物理背景,但该算法避免不了易陷入早熟和收敛效率慢的问题。针对早熟,本文提出了扰动策略,对当前最优适应度值对应的任一元素进行扰动,且随着迭代次数的增加,扰动量逐渐减少。针对收敛效率不高,提出了空间压缩策略,采用奇偶相间的方式,通过计算约束更新解的上下限以保证该解是可行解。将改进的风驱动优化算法运用到某水库的优化调度中,并与粒子群算法和标准风驱动算法进行比较。结果表明改进的风驱动优化算法更为可靠、高效,能以较快速度收敛于最优解,且最优解值更大,为水库优化调度模型求解提出新的解决方案。     

长江上游巨型水库群联合蓄水调度研究

以长江上游30座水库巨型水库群为研究对象,建立提前蓄水多目标联合优化调度模型,采用分区策略、大系统聚合分解、参数模拟优化方法和并行逐次逼近寻优算法求解。研究结果表明:所提模型框架可较好地解决巨型水库群联合蓄水优化调度问题;智能算法对于复杂约束的多目标优化问题可产生大量非劣解;Pareto前沿分布范围均匀且广泛,可供决策者灵活调度。与原设计方案相比,在防洪风险得到控制的前提下,通过水库群提前蓄水联合优化调度,水库总蓄满率由90.40%增加到94.42%,年均增发电量76.5亿kW·h(+3.76%),经济社会效益显著。     

乌鲁瓦提水库调度模型及算法研究

针对乌鲁瓦提水库调度中存在的问题及水库的主要功能,建立了多目标模型,运用遗传优化算法对目标进行排序,产生了多目标模型的非劣解集,不同代表年优化调度结果比常规调度结果有较大进步,能更好地利用水资源,充分发挥水库的综合功能。     

遗传算法在水库调度中的应用综述

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。     

Optimization of Hydropower Reservoir Using Evolutionary Algorithms Coupled with Chaos

Over the past decade, several conventional optimization techniques had been developed for the optimization of complex water resources system. To overcome some of the drawbacks of conventional techniques, soft computing techniques were developed based on the principles of natural evolution. The major difference between the conventional optimization techniques and soft computing is that in the former case, the optimal solution is derived where as in the soft computing techniques, it is searched from a randomly generated population of possible solutions. The results of the evolutionary algorithm mainly depend on the randomly generated initial population that is arrived based on the probabilistic theory. Recent research findings proved that most of the water resources variables exhibit chaotic behavior, which is a projection depends upon the initial condition. In the present study, the chaos algorithm is coupled with evolutionary optimization algorithms such as genetic algorithm (GA) and differential evolution (DE) algorithm for generating the initial population and applied for maximizing the hydropower production from a reservoir. The results are then compared with conventional genetic algorithm and differential evolution algorithm. The results show that the chaotic differential evolution (CDE) algorithm performs better than other techniques in terms of total annual power production. This study also shows that the chaos algorithm has enriched the search of general optimization algorithm and thus may be used for optimizing complex non-linear water resources systems.     

改进遗传算法及其在水库群优化调度中的应用

根据梯级水电站优化调度特点,建立遗传算法(GA)求解多阶段最优化问题的数学模型.针对标准遗传算法(sGA)局部寻优能力较差、易早熟等不足之处,从编码方法、遗传算子和混合算法方面对其进行改进,提出了采用超立方体浮点数编码自适应遗传算法(AGA)和超立方体浮点数编码遗传模拟退火算法(SA-GA).通过16种不同策略的GA在雅砻江梯级优化调度中的应用,其结果表明了改进策略在解决水库群优化问题方面的有效性和优越性.最后将GA与动态规划(DP)算法的性能进行比较分析,充分体现了GA的优点.