Penalty-Free Feasibility Boundary Convergent Multi-Objective Evolutionary Algorithm for the Optimization of Water Distribution Systems

This paper presents a new penalty-free multi-objective evolutionary approach (PFMOEA) for the optimization of water distribution systems (WDSs). The proposed approach utilizes pressure dependent analysis (PDA) to develop a multi-objective evolutionary search. PDA is able to simulate both normal and pressure deficient networks and provides the means to accurately and rapidly identify the feasible region of the solution space, effectively locating global or near global optimal solutions along its active constraint boundary. The significant advantage of this method over previous methods is that it eliminates the need for ad-hoc penalty functions, additional ??boundary search?? parameters, or special constraint handling procedures. Conceptually, the approach is downright straightforward and probably the simplest hitherto. The PFMOEA has been applied to several WDS benchmarks and its performance examined. It is demonstrated that the approach is highly robust and efficient in locating optimal solutions. Superior results in terms of the initial network construction cost and number of hydraulic simulations required were obtained. The improvements are demonstrated through comparisons with previously published solutions from the literature.     

蜜蜂进化型遗传算法在水库优化调度中的应用

提出了一种基于蜜蜂进化型遗传算法的水库优化调度问题的求解方法,并通过实例对蜜蜂进化型遗传算法和标准遗传算法的性能做了比较.结果表明,在进化代数相同的条件下,由于蜜蜂进化型遗传算法在配种选择算子上使用种群的最优个体作为蜂王,提高了种群收敛速度;再者,在代进化过程中引入一个随机种群,保持了群体的多样性,提高了算法的勘测能力.     

Self-Adaptive Solution-Space Reduction Algorithm for Multi-Objective Evolutionary Design Optimization of Water Distribution Networks

An effective way to improve the computational efficiency of evolutionary algorithms is to make the solution space of the optimization problem under consideration smaller. A new reliability-based algorithm that does this was developed for water distribution networks. The objectives considered in the formulation of the optimization problem were minimization of the initial construction cost and maximization of the flow entropy as a resilience surrogate. After achieving feasible solutions, the active solution space of the optimization problem was re-set for each pipe in each generation until the end of the optimization. The algorithm re-sets the active solution space by reducing the number of pipe diameter options for each pipe, based on the most likely flow distribution. The main components of the methodology include an optimizer, a hydraulic simulator and an algorithm that calculates the flow entropy for any given network configuration. The methodology developed is generic and self-adaptive, and prior setting of the reduced solution space is not required. A benchmark network in the literature was investigated, and the results showed that the algorithm improved the computational efficiency and quality of the solutions achieved by a considerable margin.     

Optimization of Reservoir Operation with a New Approach in Evolutionary Computation Using TLBO Algorithm and Jaya Algorithm

Reservoir operation and management are complex engineering problems, due to the stochastic nature of inflow, various demands and as well as tailwater in the downstream. The complexity increases when the number of reservoirs gets increased such as multi-reservoir system or chain system. To obtain optimal operation in such condition become more difficult. It requires powerful optimization algorithm to solve aforesaid problems. Teaching Learning Based Optimization (TLBO) algorithm and Jaya Algorithm (JA) are recently developed advanced optimization techniques a novel approach comparatively simple, easy, and robust. The main advantages of these algorithms are it only requires the common control parameters such as number of iterations and population size. In the present study, three different benchmark problems were evaluated to check the applicability and performance of TLBO and JA in multi-reservoir operation problems. The benchmark problems are the discrete time four-reservoir operation (DFRO), the continuous time four-reservoir operation (CFRO), and the ten-reservoir operation (TRO). The results from the TLBO and JA are compared with different approaches from the literature. The optimal net benefits obtained from JA for DFRO, CFRO and TRO problems are 401.44, 308.40 and 1194.59, respectively, and that of TLBO algorithm are 401.33, 308.30 and 1194.44, respectively. It is found that both JA and TLBO algorithms provided a satisfactory solution as other optimization techniques, from literature. In conclusion, JA outperformed over TLBO.     

An Improved Continuous Ant Algorithm for Optimization of Water Resources Problems

Ant colony optimization was initially proposed for discrete search spaces while in continuous domains, discretization of the search space has been widely practiced. Attempts for direct extension of ant algorithms to continuous decision spaces are rapidly growing. This paper briefly reviews the central idea and mathematical representation of a recently proposed algorithm for continuous domains followed by further improvements in order to make the algorithm adaptive and more efficient in locating near optimal solutions. Performance of the proposed improved algorithm has been tested on few well-known benchmark problems as well as a real-world water resource optimization problem. The comparison of the results obtained by the present method with those of other ant-based algorithms emphasizes the robustness of the proposed algorithm in searching the continuous space more efficiently as locating the closest, among other ant methods, to the global optimal solution.     

基于改进定权法的多目标模糊优选模型及其应用

分析了防洪调度决策的多目标性、模糊性,并针对传统的相对优属度规格化公式存在的不足,采用基于影响度因子的改进优属度计算公式,同时以改进熵权法与二元对比定权法将目标的客观属性与决策者的主观偏好相结合确定综合权重,建立了多级模糊优选防洪调度决策模型,应用于大伙房水库防洪调度决策中,取得满意结果.     

A Parallel Multi-objective Optimization Algorithm Based on Coarse-to-Fine Decomposition for Real-time Large-scale Reservoir Flood Control Operation

Reservoir flood control operation (RFCO) is a multi-objective optimization problem with a long sequence of correlated decision variables. It brings big challenges to large-scale multi-objective optimizers which were generally developed based on the divide-and-conquer strategy. For solving large-scale RFCO problem, a novel coarse-to-fine decomposition method is developed and combined with the algorithmic framework of multi-objective evolutionary algorithm based on decomposition (MOEA/D), giving rise to the proposed pCFD-MOEA/D algorithm. The pCFD-MOEA/D algorithm first divides the original RFCO problem into a sequence of sub-problems from coarse to fine scale with different scheduling time intervals. Then all sub-problems are optimized simultaneously and communicate at set intervals. Experimental results on three typical floods at Ankang reservoir have demonstrated that the proposed pCFD-MOEA/D can successfully obtain the elaborate hourly schedule schemes in real time and outperforms the compared algorithms.

     

Reservoir Optimization for Energy Production Using a New Evolutionary Algorithm Based on Multi-Criteria Decision-Making Models

The study applies kidney algorithm for the optimization of reservoir operation for hydropower generation. The objective function defined for optimization is to minimize the hydroelectric power deficiency. Results of kidney algorithm are compared with those of bat algorithm (BA), water cycle algorithm (WCA), biogeography-based optimization algorithm (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSOA), and scatter matters search algorithm (SMSA). All algorithms are evaluated by Complex proportional assessment (COPRAS), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), modified TOPSIS, and Weighted Aggregated Sum Product Assessment (WASPAS), as well as Borda count social choice theory. Then, vulnerability, time and volumetric reliability, as well as resiliency indices are used for comparison and multi-criteria decision-making indicators for selecting the best algorithm. It is found that no algorithm is ranked uniformly the best. Results indicate that kidney and particle swarm algorithms are ranked higher than other algorithms by most indices. Results of 10 random implementations of the objective function indicate that KA has a lower coefficient of variation and is computationally moe efficient. Further, most of the multi-criteria decision making models allocate the first rank to KA.     

互联区域多目标发电调度的协同优化策略

探讨了考虑环保目标的互联区域发电调度,提出一种新的多目标协同优化策略。在若干相对独立的互联区域中,依据该区域的客观情况,分区设置多目标偏好系数,改进了传统的各区域采用相同偏好系数的方法。公式推导证明,各区域的偏好系数与本区内机组的生产费用系数、污染排放系数以及网络参数密切相关。算例仿真表明,分区设置偏好系数能够优化整个系统的计算结果,以尽量小的经济损失达到环境保护的效果。     

锅炉钢框架抗震结构的多目标多级优化设计

电站锅炉钢架是一种大型复杂的钢结构。将多目标和多级优化设计理论应用于锅炉钢框架的抗震设计中,从安全性和经济性的角度对锅炉钢框架的优化设计进行了研究,通过优化得到一个既经济又安全的合理结构。用两级优化来完成锅炉钢框架抗震结构的优化过程,第一级优化考虑到承载力要求,根据规范提出了强度、刚度、稳定性等方面的约束条件,采用复形法以构件截面尺寸作为设计变量,将结构的整体位移目标函数作为第一级优化目标。第二级优化在第一级优化结论的基础上进行选优,参考规范截面最小尺寸、长细比等要求提出约束条件,利用罚函数法,以截面尺寸作为设计变量,将用钢量作为目标函数进行最优计算,得到一个在满足最大侧移情况下用钢量最小的结构设计方案。最后以某锅炉钢框架实例进行优化分析,并从工程造价,结构抗侧移能力等角度对优化前后的锅炉钢框架结构进行了对比分析,研究结果表明本文提出的理论和方法可以计算锅炉钢框架在最大侧移情况下的最优用钢量,研究结果可供工程设计参考。     

多目标免疫遗传算法在梯级水库优化调度中的应用研究

为了提高遗传算法在多目标梯级水库优化调度中的应用效果,在标准遗传算法的基础上引入了免疫机制,并将其应用到周公宅-皎口水梯级水库优化调度中。计算结果表明,免疫遗传算法较好地克服了标准遗传算法收敛速度慢、易陷入局部极值、早熟等弱点。     

Optimization and Simulation Modelling for Managing the Problems of Water Resources

Waterlogging and secondary salinization have become a serious problem in the canal irrigated areas of arid and semi–arid regions worldwide. In this study, a unique and simple technique was evolved in which a linear programming (LP) optimization model was first developed that allocates available land and water resources in order to maximize net annual returns by mitigating the waterlogging problems. A finite–difference two–dimensional simulation model was then used to evaluate the long–term impacts of various water management strategies on the groundwater table with the optimal land and water use parameters which were obtained through the optimization model. The model was used to combat the waterlogging and salinity problem of an area located in Haryana State of India. The calibration, validation, sensitivity analysis, and error analysis of the model was performed before it was used to study the impact of various water management scenarios on the long-term groundwater level. Based on the model results a change in cropping pattern with reduced rice area is suggested. Groundwater withdrawal should be increased by 1–7 % in the various nodes. It is concluded from the analysis of various scenarios that implementing multiple approaches simultaneously are more effective in controlling waterlogging problems as compared to individual interventions.     

Assessment of Penalty-Free Multi-Objective Evolutionary Optimization Approach for the Design and Rehabilitation of Water Distribution Systems

This paper describes a penalty-free multi-objective evolutionary optimization approach for the phased whole-life design and rehabilitation of water distribution systems. The optimization model considers the initial construction, rehabilitation and upgrading costs. Repairs and pipe failure costs are included. The model also takes into consideration the deterioration over time of both the structural integrity and hydraulic capacity of every pipe. The fitness of each solution is determined from the trade-off between its lifetime costs and its actual hydraulic properties. The hydraulic analysis approach used, known as pressure-dependent modelling, considers explicitly the pressure dependency of the water supply consumers receive. Results for two sample networks in the literature are included that show the algorithm is stable and finds optimal and near-optimal solutions reliably and efficiently. The results also suggest that the evolutionary sampling efficiency is very high. In other words, the number of solutions evolved and analysed on average before finding a near-optimal solution is small in comparison to the total number of feasible and infeasible solutions. We found better solutions than those reported previously in the literature for the two networks considered. For the Kadu network, for example, the new best solution costs Rs125,460,980—a significant improvement. Additional statistics that are based on extensive testing are included.     

Multi-objective Planning for Conjunctive Use of Surface and Subsurface Water Using Genetic Algorithm and Dynamics Programming

The consideration of fixed cost and time-varying operating cost associated with the simultaneous conjunctive use of surface and subsurface water should be treated as a multi-objective problem due to the conflicting characteristics of these two objectives. In order to solve this multi-objective problem, a novel approach is developed herein by integrating the multi-objective genetic algorithm (MOGA), constrained differential dynamic programming (CDDP) and the groundwater simulation model ISOQUAD. A MOGA is used to generate the various fixed costs of reservoirs’ scale, generate a pattern of pumping/recharge, and estimate the non-inferior solutions set. A groundwater simulation model ISOQUAD is directly embedded to handle the complex dynamic relationship between the groundwater level and the generated pumping/recharge pattern. The CDDP optimization model is then adopted to distribute the optimal releases among reservoirs provided that reservoir capacities are known. Finally, the effectiveness of our proposed integrated model is verified by solving a water resources planning problem for the conjunctive use of surface and subsurface water in southern Taiwan.     

华北轮作农田灌溉制度多目标优化模型及应用

针对华北地区冬小麦-夏玉米轮作种植模式,构建了轮作农田灌溉制度多目标模拟-优化模型,该模型利用水量平衡模型模拟不同灌溉制度下的农田耗水过程,利用水分生产函数估算不同耗水条件下的作物产量,以2种作物每次灌溉的灌水时间及灌水定额为决策变量,以两种作物产量最大为目标函数。引入动态罚函数对灌溉定额进行约束,采用改进非支配排序遗传算法进行求解,最终用理想点法对所得Pareto非劣解进行决策。不同灌溉定额下北京地区冬小麦-夏玉米轮作农田灌溉制度多目标优化结果表明:轮作模式下冬小麦的灌水关键时期为抽穗与灌浆期,与水分敏感期基本一致;夏玉米的灌水关键时期为拔节与抽穗期,受前期墒情及降水影响比水分敏感期有所提前。在最优灌溉制度下,随着灌水量的增加,两种作物产量均增加;产值呈抛物线增加,但边际产值递减;单位灌水量产值呈幂函数减小;同时作物耗水量呈对数增加,而土壤水利用量呈抛物线减少。     

乌弄龙水电站泄洪闸门远程控制精度优化

无人值班,少人值守是水电站建设追求的目标。尤其是许多新建的大中型水电站,在建设初期就以这个目标去建设,很多系统都实现了远程控制。以乌弄龙水电站泄洪闸门远程控制系统为例,介绍了该系统的远程控制原理,详细讲解了对闸门远程控制系统精度进行优化的方法,通过试验得到的数据去分析优化后的实际效果。     

Genetic Algorithms for Multi-objective Optimization: Application to a Multi-reservoir System in the Chi River Basin, Thailand

The multi-objective genetic algorithm is applied to determine the optimal operation of a multi-reservoir system in the Chi River Basin, Thailand. Two competing objective functions are considered; dam release and dam storage. The predicted values for the release and storage needed are mostly lower than in current established management practice.     

黄河流域干流河段水资源调配多目标优化模型

目前黄河水资源的分配手段仍存在不少问题,用水量同比例丰增枯减不利于黄河水量的合理利用,传统的调度方式也无法使资源、社会达到协调发展。根据黄河水资源的实际情况,考虑各地区用水的公平合理性,建立黄河干流河段多目标优化配水模型,采用改进遗传算法来求解河段优化配水问题。结果表明:经过改进遗传算法的优化求解,可以将黄河干流水资源量科学合理地分配到各河段,全区缺水率控制在10%以下,并且满足河道断面的生态流量需求,符合社会、生态对于水资源开发利用的要求,最大程度地发挥了水资源的综合效益。     

潮汐能发电站水轮机调速设备若干问题的探讨

结合了江厦潮汐电站水轮机调速器儿次技术改造的情况以及作者研发的心得体会,论述了潮汐电站水轮发电机组及其调速系统运行的特点,介绍了一种适合江厦潮汐电站水轮发电机组控制的调速模式,提出了为潮汐电站配置调速器的几点建议.