浅析爽岛水库的优化调度与管理

在水库调度中,由于天然径流的随机特性,调度决策过程的多阶段性,水库结构的复杂性以及应用的多目标性,对水库调度工作提出了严格的要求。可以说,水库优化调度是一项高度复杂性的问题。本文简单探讨了爽岛水库调度的基本内容、特点以及基本目标要求,希望对从事相关工作人员提供一些参考价值。     

从乌拉泊水库蓄泄调度来探索水库优化调度的问题与思路

乌鲁木齐河上游现有乌拉泊、红雁池两座水库,共同承担着乌鲁木齐市的防洪任务问题,从乌拉泊水库近几年来的蓄泄供水调度情况来分析水库的利用效能,由此展开来探索水库优化调度的问题及思路。上游大西沟水库建成后,将对大西沟、乌拉泊以及红雁池三库形成的梯级水库进行优化调度,才能获得尽可能大的综合效益。     

梯级水电站优化调度模型与算法研究

进入二十一世纪以来,科技大发展,经济大发展。人们的生活越来越舒适、便捷的同时,随之而来的一系列问题也十分明显。环境的污染、能源的短缺,促进了我国水电企业模型的改革,因为只有改革才能适应时代的变化,才能解决日益严峻的能源形势。下面,我们将主要分析一下目前我国梯级水电站优化调度模型与算法。     

遗传算法的改进及其在水库优化调度中的应用研究

遗传算法是通过对样本中个体的不断改进来寻找各类问题的最优解。由于标准遗传算法（SGA）存在收敛性及个体适应度求解方面的困难，在研究中，通过对SGA中遗传算子改进，特别是对选择算子的改进，提出了一种改进遗传算法（AGA），并将它应用于水库优化调度中。改变通常以水位变化序列为基础的遗传算法编码方案，通过数组存储水库库容状态，并以各库容状态对应的数组下标为基础进行遗传算法编码，通过实例，表明AGA对水库优化调度问题具有良好的适应性，同时结合数组存储理论的遗传算法编码方法简化了水库优化调度遗传算法的实现过程。     

浅谈综合利用水库的调度

水库调度也称水库控制运用,是根据水库承担任务的主次及规定的调度规则,运用水库的调蓄能力,在保证大坝安全的前提下,有计划地对入库的天然径流进行蓄泄,达到除害兴利,综合利用水资源,最大限度地满足国民经各部门需要的目的。     

一类资源负荷均衡问题的优化调度算法

针对一类n个独立任务在m个不完全同等的处理机上处理,使处理机的最大负荷为最小的非抢先调度问题,提出了一种启发式算法－－最小平衡算法,并分析了它的时间复杂性,在此基础上,又将最小平衡算法和遗传算法结合起来,提出了基于遗传的最小平衡算法,并用实例证实了该算法的有效性。     

采用Pareto人工鱼群算法的结构健康监测传感器位置多目标优化EI北大核心CSCD

发展基于Pareto多目标人工鱼群算法(Multi⁃Objective Artificial Fish Swarm Algorithm,MO⁃AFSA),解决结构健康监测中传感器位置多目标优化的问题。构建与观测模态线性独立性、结构损伤灵敏度和损伤信息冗余性有关的传感器位置多目标优化目标函数;改进人工鱼群算法的追尾和觅食行为,并引入外部档案集以处理寻优过程中的互不支配解,结合Pareto概念选取与理想点欧式距离最近的Pareto解为最优解;以三层平面钢框架结构为数值算例,用基于Pareto人工鱼群算法求解传感器位置多目标优化方案,并进行结构损伤识别。研究结果表明:用所提方法得到的传感器测点在结构中均匀分布,获取的结构损伤信息更为全面,冗余性低,振型独立性好,能够较精确地识别损伤位置和损伤程度,并且抗噪性能好。     

运输能力柔性约束的供应链Pareto优化策略

针对单位产品运输成本对批量敏感并由生产商负责产品运输的情况,建立了供需双方在分散决策和集中决策情形下的最佳批量模型.分析结果表明运输能力柔性越强,生产商的最佳生产批量越小,与批发商要求的短周期、小批量订货越接近,从而在不增加成本的情况下,生产商能够对批发商的需求快速响应.且当运输能力非完全柔性时,生产商可以通过降低产品转让价格改让批发商负责产品运输,以使批发商在分散决策情形下的最佳订货批量更接近于生产商的最佳生产批量和供应链在集中决策情形下的订货批量,从而在供需方双赢的前提下实现整个供应链的利益优化.     

基于蚁群算法的多值属性系统测试序列优化

针对系统故障诊断中的多值属性系统测试序列优化问题,该文提出一种改进的蚁群算法,将成功运用在二值属性系统中的蚁群算法扩展到多值属性系统中,根据多值属性系统特点,设计相应的状态转移规则和信息素更新机制,并采用蚁群算法和遗传算法相融合的联合优化策略,解决了多值属性系统的序列优化问题,为多值属性系统的测试优化问题提供了一条新的解决途径。     

梯级水电站水库联合优化调度研究与应用

本文作者通过对K判别式法基本原理及水电站水库调度图相结合的K判别式方法进行了分析。主要就水电站水库优化调度中,利用判别式法求解梯级水电系统优化调度的运行规律进行了详细的研究。同时以实例应用说明了该求解模型和方法在梯级水电站调度系统中是行之有效的。     

循环水泵优化运行计算模型的优化及应用实例

基于试验的循环水泵优化运行计算模型是以试验数据作为基础,对已完成的试验工况下的循环水泵运行方式进行优化计算,而不能对未开展的试验工况进行经济性评价,本文介绍了一种计算方法,可以对此类计算模型进行优化,并用实例进行验证。     

车辆悬挂参数优化的频域等效最优控制算法

文章提出了一种适合于车辆悬挂参数优化的频域等效控制算法。该算法建立在线性最优化控制理论基础上,能大大提高优化的计算效率。由于该算法建立在频域上,所以便于处理激励中的时间滞后和噪声问题,对车辆悬挂参数的优化很有实用价值。文章还给出了该算法在二自由度车辆悬挂系统设计中的应用。     

A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation

The safety hazards existing in the process of disassembling waste products pose potential harms to the physical and mental health of the workers. In this article, these hazards involved in the disassembly operations are evaluated and taken into consideration in a disassembly line balancing problem. A multi-objective mathematical model is constructed to minimise the number of workstations, maximise the smoothing rate and minimise the average maximum hazard involved in the disassembly line. Subsequently, a Pareto firefly algorithm is proposed to solve the problem. The random key encoding method based on the smallest position rule is used to adapt the firefly algorithm to tackle the discrete optimisation problem of the disassembly line balancing. To avoid the search being trapped in a local optimum, a random perturbation strategy based on a swap operation is performed on the non-inferior solutions. The validity of the proposed algorithm is tested by comparing with two other algorithms in the existing literature using a 25-task phone disassembly case. Finally, the proposed algorithm is applied to solve a refrigerator disassembly line problem based on the field investigation and a comparison of the proposed Pareto firefly algorithm with another multi-objective firefly algorithm in the existing literature is performed to further identify the superior performance of the proposed Pareto firefly algorithm, and eight Pareto optimal solutions are obtained for decision makers to make a decision.     

Rock Hyraxes Swarm Optimization: A New Nature-Inspired Metaheuristic Optimization Algorithm

This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization (RHSO) inspired by the behavior of rock hyraxes swarms in nature. The RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this food. Rock hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the group. Forty-eight (22 unimodal and 26 multimodal) test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm. A comparative efficiency analysis also checks RHSO with Particle Swarm Optimization (PSO), Artificial-Bee-Colony (ABC), Gravitational Search Algorithm (GSA), and Grey Wolf Optimization (GWO). The obtained results showed the superiority of the RHSO algorithm over the selected algorithms; also, the obtained results demonstrated the ability of the RHSO in convergence towards the global optimal through optimization as it performs well in both exploitation and exploration tests. Further, RHSO is very effective in solving real issues with constraints and new search space. It is worth mentioning that the RHSO algorithm has a few variables, and it can achieve better performance than the selected algorithms in many test functions.     

Dark Forest Algorithm: A Novel Metaheuristic Algorithm for Global Optimization Problems

Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science and engineering fields. They are popular and have broad applications owing to their high efficiency and low complexity. These algorithms are generally based on the behaviors observed in nature, physical sciences, or humans. This study proposes a novel metaheuristic algorithm called dark forest algorithm (DFA), which can yield improved optimization results for global optimization problems. In DFA, the population is divided into four groups: highest civilization, advanced civilization, normal civilization, and low civilization. Each civilization has a unique way of iteration. To verify DFA’s capability, the performance of DFA on 35 well-known benchmark functions is compared with that of six other metaheuristic algorithms, including artificial bee colony algorithm, firefly algorithm, grey wolf optimizer, harmony search algorithm, grasshopper optimization algorithm, and whale optimization algorithm. The results show that DFA provides solutions with improved efficiency for problems with low dimensions and outperforms most other algorithms when solving high dimensional problems. DFA is applied to five engineering projects to demonstrate its applicability. The results show that the performance of DFA is competitive to that of current well-known metaheuristic algorithms. Finally, potential upgrading routes for DFA are proposed as possible future developments.     

造船厂钢板出库作业计划的建模及优化研究

对于造船厂钢板堆场出库作业计划问题,在分析堆场出库作业流程的基础上,建立了以出库作业时间最短为目标的优化模型.该模型可归结为多层组合优化问题.针对本问题寻优过程的阶段性和复杂性的特点,提出了基于改进遗传算法的多层嵌套启发式优化算法.与传统人工决策的作业方案相比,该优化结果可以缩短24%～67%的作业时间.     

Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer. The AA is mathematically described, and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions. Furthermore, the proposed algorithm's performance is compared vs. eight approaches, including teaching-learning based optimization, marine predators algorithm, genetic algorithm, grey wolf optimization, particle swarm optimization, whale optimization algorithm, gravitational search algorithm, and tunicate swarm algorithm. According to the simulation findings, the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios, and it can give adequate quasi-optimal solutions to these problems. The analysis and comparison of competing algorithms’ performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.     

Sensitivity analysis for decision-making using the MORE method—A Pareto approach

Integrated Assessment Modelling (IAM) incorporates knowledge from different disciplines to provide an overarching assessment of the impact of different management decisions. The complex nature of these models, which often include non-linearities and feedback loops, requires special attention for sensitivity analysis. This is especially true when the models are used to form the basis of management decisions, where it is important to assess how sensitive the decisions being made are to changes in model parameters. This research proposes an extension to the Management Option Rank Equivalence (MORE) method of sensitivity analysis; a new method of sensitivity analysis developed specifically for use in IAM and decision-making. The extension proposes using a multi-objective Pareto optimal search to locate minimum combined parameter changes that result in a change in the preferred management option. It is demonstrated through a case study of the Namoi River, where results show that the extension to MORE is able to provide sensitivity information for individual parameters that takes into account simultaneous variations in all parameters. Furthermore, the increased sensitivities to individual parameters that are discovered when joint parameter variation is taken into account shows the importance of ensuring that any sensitivity analysis accounts for these changes.     

矿物质粉体对砂浆及混凝土Cl-渗透性的影响

研究了不同水胶比、不同矿物质粉体掺量的砂浆和混凝土，经标准养护至56天、90天时的导电量。在相同水胶比和相同矿物质粉体掺量下，混凝土的导电量远低于砂浆的导电量。含矿物质粉体的砂浆及混凝土的导电量均低于基准砂浆及混凝土的导电量。导电量随水胶比的降低而降低，也随龄期的增长而降低。