考虑能耗的再制造系统多目标调度方法

为充分发挥再制造产业的潜力,在保证商业利益的同时提升其对于环境保护的贡献,针对带有流水线型重加工线的再制造系统的特点,在混合流水线调度决策研究中加入对节约能源因素的考虑,提出一种改进的多目标人工蜂群算法,并应用机器闲时判断开关机策略进一步减少能源消耗.以最小化最大完工时间与最小化能源消耗量为优化目标,建立双目标数学模型;修改经典单目标人工蜂群算法为双目标算法后引入了精英策略、双重邻域搜索,保证改编算法的收敛速度,加入局部最优逃脱算子,加强改编算法的局部搜索能力.由于产品组装具有成套要求,产品的分解与重加工工序可发生变动,产生大量机器闲时,通过判断开关机策略节能,并利用带有精英策略的改进模拟退火算法求解能源消耗量计算子问题.对算法进行数值计算并与已有代表性算法比较,结果表明该方法是有效、可行的,在目标系统中应用此方法能够在保证完工时间的同时取得可观的节能效果.     

Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network. Foundation item: Project (70671039) supported by the National Natural Science Foundation of China     

同时考虑发电费用与网络损耗的多目标有功最优潮流

在对传统的单纯以发电费用最少和网络损耗最小为目标的单目标最优潮流分析的基础上,利用权重系数将两者有机结合,建立了多目标的有功最优潮流模型,分别利用二次规划算法和遗传算法进行求解,并对两种计算方法的计算结果和求解效率进行了分析。     

基于改进人工鱼群算法的含风电场电力系统最优潮流计算

将风电场看作PQ节点并考虑风电场输出功率随机性和负荷随机性,提出含风电场的电力系统最优潮流计算方法。分析基本人工鱼群算法原理及其缺陷,提出改进人工鱼群算法(IAFSA);提出人工鱼群的视野和步长的取值方法,对初始种群的生成、行动方式和终止判据进行改进;并基于动态调整罚函数将带约束的有功最优潮流问题转化为无约束优化问题,实现含风电场的电力系统最优潮流计算.实例分析结果表明:基于改进人工鱼群算法比基于基本人工鱼群算法和遗传算法进行含风电场电力系统最优潮流计算具有更优全局收敛性和更快计算速度.     

Design optimization of multilayer perceptron neural network by ant colony optimization applied to engine emissions data

Science China Technological Sciences - A multilayer perceptron (MLP) artificial neural network (ANN) model has been optimized by the multi-objective ant colony optimization (MOACO) algorithm, which...     

求解多目标柔性作业车间调度问题的离散人工蜂群算法

作业车间调度问题是一类典型的组合优化问题,要求多个作业在不同的机器上进行加工,目的是获得最好的作业加工序列,以满足特定的性能指标。柔性作业车间调度问题是对传统的作业车间调度问题的进一步扩展,由于求解的复杂性,使得传统方法很难在有效的时间内获得问题的最优解。人工蜂群算法是近年来提出的一种受生物行为启发的优化算法,该算法主要通过模拟蜜蜂的觅食来实现问题的求解。提出了一种离散的人工蜂群算法于求解柔性作业车间调度问题,算法通过交叉方式来搜索潜在的更好的蜜源,并采用自适应的变异策略来降低早熟收敛的可能性。最后通过对比实验证明算法对于求解多目标柔性作业车间调度问题是有效的。     

基于多目标驱动人工蜂群算法的疏散仿真模型

提出一种基于多目标驱动蜂群算法的疏散仿真模型.在人工蜂群算法的基础上,对跟随蜂设置视野搜索半径,将视野范围内适应值最优个体作为其视觉引领蜂,从而减少跟随蜂选择引领蜂的盲目性.提出多目标驱动人工蜂群算法,即由惯性引领蜂、全局最优蜂、个体历史最优蜂、视觉引领蜂共同对跟随蜂的移动方向进行引导,从而使跟随蜂的移动路径得到进一步...     

An optimization algorithm for locomotive secondary spring load adjustment based on artificial immune

In order to control the locomotive wheel （axle） load distribution, a shimming process to adjust the locomotive secondary spring loads was heretofore developed. An immune dominance clonal selection multi-objective algorithm based on the artificial immune system was presented to further improve the performance of the optimization algorithm for locomotive secondary spring load adjustment, especially to solve the lack of control on the output shim quantity. The algorithm was designed into a two-level optimization structure according to the preferences of the problem, and the priori knowledge of the problem was used as the immune dominance. Experiments on various types of locomotives show that owing to the novel algorithm, the shim quantity is cut down by 30%-60% and the calculation time is about 90% less while the secondary spring load distribution is controlled on the same level as before. The application of this optimization algorithm can significantly improve the availability and efficiency of the secondary spring adjustment process.     

基于改进人工蜂群算法的大型舰船主尺度优化

针对大型舰船方案设计的具体特点,选择了飞行甲板面积最大化、初稳性高最合理化、估算阻力最小化和横摇固有周期最大化等4个优化目标,建立了适用于大型舰船主尺度优化设计的多目标模型,并基于最小偏差法建立了统一的目标函数。采用人工蜂群算法对优化模型进行了求解,并对人工蜂群算法的初始化方法和观察蜂的选择机制进行了改进,通过仿真计算,验证了人工蜂群算法求解复杂问题的优越性和改进策略的合理性,以及该算法在船舶设计中应用的可行性。     

考虑输配交互的含风电场电力系统多目标动态随机模糊最优潮流

提出一种考虑输配交互的含风电场电力系统多目标动态随机模糊最优潮流方法。突破传统输配网最优潮流独立计算且配网一般以负荷计及的现状,考虑主动配电网与输电网的双向潮流交互特性和风电注入功率随机模糊性,兼顾系统经济、低碳、降损多目标优化,考虑输配网静态安全,基于随机模糊机会约束规划建立含风电场电力系统输配交互多目标动态随机模糊潮流模型;提出在随机模糊模拟、牛顿拉夫逊法与前推回代法求解输配系统随机模糊潮流基础上,通过输配网节点功率和电压考虑输配交互特性,基于Look-ahead和NSGA-I获取各时段Pareto解,采用最大满意度法决策,从而提出模型求解算法。改进的IEEE 30节点输电系统与IEEE 33节点配电系统的算例仿真结果表明了文中提出的模型和算法有效性和优越性。     

单播组播共存环境下的多波束卫星功率优化分配

针对多波束卫星系统单播组播共存情况下的功率分配问题,提出了一种多目标优化设计方案。考虑同一组播流在不同波束下的一致性、数据流之间共享关口站上行链路带宽的公平性以及各个业务流的平均速率三项性能指标,构造了具有多重目标函数的数学模型,并采用基于快速分类的非支配排序遗传算法(NSGA-II)进行了最优功率集的求解。仿真结果表明,该方案在组播一致性、带宽公平性以及平均业务速率方面均取得了较好的性能,而且算法的收敛速度较快,可适应网络业务量动态变化的需求。     

基于改进的人工蜂群算法的微电网储能系统容量优化配置

由于风光能源具有间歇性和波动性的特点,对电网的电能质量造成了不良影响,因此提出了一种微电网的储能容量优化配置方法。首先,建立以用户用电费用最低、储能能量损失最小及风光能源的波动性最小为目标的微电网系统模型;然后,提出了一种改进的人工蜂群算法求解模型,通过不同的算法对无储能、单储能及混合储能3种储能方案模型进行求解分析;最后,采用熵权法找出适用于微电网的最佳储能方案。实验结果表明,改进的人工蜂群算法能够求解微电网模型且不易陷入局部最优,并通过熵权法得出了蓄电池和超级电容的组合适合作为微电网储能系统的结论。     

改进人工蜂群算法

针对人工蜂群算法存在的收敛速度慢、易陷入局部最优的缺点,利用自由搜索算法中的信息素、灵敏度模型代替传统的轮盘赌选择模型,并引入OBL策略产生新蜜源取代每次迭代的最差蜜源,提出了一种改进的人工蜂群算法,并结合NIT技术建立一种新的多峰优化方法.对9个标准测试函数仿真表明本文提出的改进算法不仅大大提高了最优解的精度而且缩短了运行时间,改进性能明显优于现有人工蜂群算法.实例测试表明该方法能够有效、精确地搜索各个峰值点.     

Improvement of light uniformity by lighting arrangement for standardized crop production

In a commercialized, fully artificial plant factory, artificial luminaire is arranged in a unified way using a general illumination theory, an actual measurement, or an empirical methodology. However, with these methods, lightings are implemented without considering specific optical characteristics of lighting or material characteristics of each component that constructs a cultivation system, resulting in an amount of light that becomes irregular. The amount of lighting is closely related with the growth and quality of crops, and the deviation between points where cultivated crops are located causes quality difference in the produced crops, thus impairing the economic feasibility of a plant factory. In this regard, a simulation to figure out an optimum lighting layout was performed. Arrangements based on the spectrum distribution of light source and reflector materials were implemented to ascertain the distance between lighting and height of lighting and gather information in the pre-treatment process to improve the uniformity of light in the plant cultivation system. Improvement of around 15% in light uniformity is achieved compared with the existing system after the simulation is carried out. This result would reduce the deviation in crop growth to make uniform quality crop production possible.     

基于改进蝙蝠算法含分布式电源的无功优化

将分布式电源与传统的配电网电压调节方式相结合,分析包含分布式电源的配电网系统无功优化的问题,并建立了有功网络损耗最低以及电压稳定裕度最大的多目标优化数学模型,利用模糊理论将分布式电源的多目标无功优化问题转换成单目标优化问题,进一步减少了有功网损,提高了电压稳定性。鉴于传统蝙蝠群体易于聚集于局部极值,导致早熟,将混沌序列以及自适应调整策略融入到蝙蝠优化算法中,提出了一种改进型多目标蝙蝠优化算法,利用混沌理论以及动态自适应机制调整的特性,对蝙蝠算法参数进行调整。通过IEEE-33测试系统验证,所提算法具有良好的实用性和适应性,并且也证明了所提模型的实际意义。     

基于自适应进化策略的人工蜂群优化算法

提出一种自适应进化策略的人工蜂群优化算法来提高基本人工蜂群优化算法的性能。算法中每个引领蜂拥有4种进化策略,在迭代过程中通过计算每种进化策略的立即价值、未来价值和综合奖励来决定引领蜂个体的进化行为,并通过多策略进化概率变异方式来提升个体寻优速度或避免陷入局部最优解。典型高维复杂函数测试表明,该算法具有很好的收敛精度和计算速度。     

基于GA的电力系统多目标模糊无功优化

针对传统无功优化的目标单一性,建立了以有功网络损耗和节点电压偏差均最小为目标的无功优化模型,采用模糊数学将不同量纲目标进行归一化,并转化为单目标模糊规划模型.鉴于多目标无功优化模型的复杂性,以及连续、离散控制变量并存,采用遗传算法搜索全局最优解.对某21节点系统进行了多目标无功优化分析,验证了该模型的可行性和优越性.     

基于改进自适应权重多目标粒子群算法的分布式电源优化配置

根据配电网和分布式电源相关特性,建立考虑多种约束条件下配电网网损最小、分布式电源成本最低、电网电压稳定性最好的多目标优化数学模型。该文提出一种改进自适应权重多目标粒子群算法对配电网进行分布式电源优化配置,相较于传统的多目标粒子群算法容易陷入局部最优,以及单目标算法只能给出单一配置方案,该方法在保证得到更接近全局最优解的同时提供一系列可供选择的方案(一组Pareto解集)。采用不同算法对IEEE 69节点算例进行求解计算,仿真结果充分证明了算法的优越性,为配电网中分布式电源配置提供了更为灵活的可行性方案。     

Spatial quality evaluation for drinking water based on GIS and ant colony clustering algorithm简

To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system （GIS） and an ant colony clustering algorithm （ACCA） was used. Drinking water samples from 29 wells in Zhenping County, China. were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network （CHNN） to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.     

基于马尔可夫链的人工蜂群算法

分析了人工蜂群算法及部分国内外学者提出的改进算法,针对局部搜索能力差和容易陷入局部最优解的缺点,根据马尔可夫链预测已知解空间的发展趋势,提出了一种基于马尔可夫链的改进人工蜂群算法（MABC）,通过伪代码给出了算法的运行过程,从收敛性能和算法复杂度2个方面分析了人工蜂群算法、一种典型的改进算法和MABC算法的性能.最后以10个典型函数为测试用例,从结果精度、收敛速度、分割参数和运行时间4个方面进行验证,实验结果表明,MABC算法在求解精度和收敛速度上高于ABC算法,但运行时间略长,验证了理论分析的结果.