基于内点法的多目标OPF计算方法及其在PSAT软件中的应用

介绍了一个把电力系统安全性和市场效益同时作为优化目标的多目标最优潮流计算模型。并用与系统规模关系不大的内点法对模型进行求解。通过IEEE6节点系统和Italian129节点系统2个例子在功能强大的PSAT软件中进行验证,结果表明此多目标模型可以有效地优化不同的目标,且PSAT软件具有一定的实用性。

基于PSAT软件的多目标最优潮流计算

介绍了一个把电力系统安全性和市场效益同时作为优化目标的多目标最优潮流计算模型.通过一个IEEE6节点的实例在功能强大的新型电力系统分析软件工具PSAT中进行验证,结果表明采用所描述的方法既可提高电力系统的安全性,同时还由于电能交易总额的增加和节点边际价格的调整而获得更大的市场效益.

基于资源限额的计算机多目标决策

在新旧体制转换过程中 ,资源重组的科学决策是有效利用资源 ,提高综合效益的关键 ,本文在多目标规划理论的基础上 ,给出了一种后勤优先级变权多目标规划方法 ,并就其技术实现、模型构成和基于后勤保障资源限额的计算机多目标决策进行了设计。

基于参考向量关联估计的离线多目标优化算法

很多实际工程和科学问题都是计算费时的多目标优化问题,这类问题中每个候选解的评价往往都非常费时,因此仅允许使用少量真实评价。论文采用离线数据驱动的进化算法求解计算费时多目标优化问题,以期节省优化时间。论文通过训练代理模型来估计候选解的收敛性,采用最近邻样本估计候选解与参考向量的关联关系,减少了使用目标估值计算候选解与参考向量夹角大小所产生的误差累积。使用DTLZ测试集验证论文算法的有效性,论文算法与离线数据驱动的优化算法MS-RV以及三个经典在线数据驱动优化算法进行对比,实验结果表明论文提出的算法在保证性能的前提下,可以减少使用真实的评价次数。

城市规划中的水资源承载力计算——以惠州市为例

探讨水资源承载力综合评估和多目标计算理论在城市规划中的应用。运用灰色多层次综合评估模型对惠州市水资源承载力进行定性的综合评价,应用多目标规划理论对城市经济发展规划不同方案下的惠州市水资源承载力作了定量计算。

基于改进多种群遗传算法的节能环保多目标优化模型

电力系统多目标经济负荷分配问题是个非线性、高维的复杂优化问题。提出基于交互式的改进多种群遗传算法,通过引入精英策略和移民策略的多种群遗传算法可以有效地克服标准遗传算法容易陷入局部最优解、易早熟的缺陷。针对文中提出的煤耗和排放2个目标函数,提出了基于目标满意度和总体协调度的交互式多目标处理方法,通过寻求向量空间内满足总体协调度的最短"欧氏距离",来贴近决策者满意的理想值,解决了各目标函数之间最优解的相互冲突,达到协调好各个目标函数的目的,充分体现了决策者的意愿。试验算例表明,该算法能够有效地解决电力系统多目标经济负荷分配问题。

航天器性能样机气动一体化多目标优化方法

通过分析复杂航天产品多学科建模过程和性能样机多学科协同优化建模方法,提出了性能样机MDO协同模型。针对复杂航天产品多学科一体化设计要求,利用粒子群优化算法和多目标遗传算法,构建了基于多目标混合软计算模型,实现了性能样机多学科优化算法,并通过多目标混合软计算对高超声速飞行器性能样机外形气动一体化多目标优化设计进行仿真验证。验证结果表明所提出的算法能够在设计候选解中求得Pareto优化解,较好的实现了复杂航天产品多学科中的元模型性能目标综合优化与方案评价。

一种基于分解多目标的三角差分算法

为了解决多目标优化问题,该文提出了一种改进的基于分解的多目标进化算法,算法与基于分解的多目标算法的最大不同是采用了三角差分算子,实验结果表明改进的算法优于原有的基于分解的多目标优化算法。

一种改进的基于分解多目标优化算法

为了解决多目标优化问题,该文提出了一种改进的基于分解的多目标进化算法,算法非平衡的分配计算资源给每个子问题,每个子问题具有相对较大的密度时,能够分配较多的资源,实验结果表明改进的算法优于原有的基于分解的多目标优化算法。

广东省水资源承载力量化研究

运用多目标规划和模糊综合评价的理论与方法,结合广东省水资源特点,建立了水资源承载力多目标计算与综合评估模型。模型选取工业产值、人口、农灌面积和COD排放4个目标,以水资源供需平衡、部门用水限制等约束条件,计算了广东省高、中、低3种水资源承载力方案并与城市规划有关指标作了比较,量化反映了水资源对社会、经济和生态环境协调发展瓶颈作用;选择了水资源数量、水资源质量、可供水量、用水状况和社会经济5方面共20个评价指标,综合评估了21个市现状水资源承载力状况,表明各个市水资源利用与社会经济发展匹配性程度。结果表明,水资源承载力量化研究可为协调广东水资源、经济与可持续发展之间的关系提供决策参考依据。

考虑负荷均衡的水资源承载能力研究——以滦河流域为例

经济的高速发展、人口的快速膨胀,导致水资源短缺、水环境污染、水生态恶化等问题日益加剧。在协调地区社会经济发展与生态环境保护的基础上,明确水资源的承载极限与承载状态是缓解水危机,实现水资源可持续利用的有效途径。对负荷均衡的概念进行探讨,通过引入协调发展度对社会经济、生态环境均衡发展的程度进行度量,将其作为约束条件,构建了水资源承载能力多目标计算模型,并将其应用于滦河流域。结果表明:随着时间的推移,滦河流域水资源承载状态大致呈现出好转的趋势;流域内部的唐山地区的承载状态相对于其他地区较差,需要引起注意。

HYBRID MULTI-OBJECTIVE GRADIENT ALGORITHM FOR INVERSE PLANNING OF IMRT

The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.

Decision making tools for optimal material selection:A review

The present work reviews different decision making tools(material comparing and choosing tools)used for selecting the best material considering different parameters.In this review work,the authors have tried to address the following important enquiries:1)the engineering applications addressed by the different material choosing and ranking methods;2)the predominantly used decision making tools addressing the optimal material selection for the engineering applications;3)merits and demerits of decision making tools used;4)the dominantly used criteria or objectives considered while selecting a suitable alternative material;5)overview of DEA on material selection field.The authors have surveyed literatures from different regions of the globe and considered literatures since 1988.The present review not only stresses the importance of material selection in the early design stage of the product development but also aids the design and material engineers to apply different decision making tools systematically.

Optimization and Control of Extractive Distillation with Heat Integration for Separating Benzene/Cyclohexane Mixtures

In this work, the extractive distillation with heat integration process is extended to separate the pressure-insensitive benzene-cyclohexane azeotrope by using furfural as the entrainer. The optimal design of extractive distillation process is established to achieve minimum energy requirement using the multi-objective genetic algorithm, and the results show that energy saving for this heat integration process is 15.7%. Finally, the control design is performed to investigate the system's dynamic performance, and three control structures are studied. The pressure-compensated temperature control scheme is proposed based on the first two control structures, and the dynamic responses reveal that the feed disturbances in both flow rate and benzene composition can be mitigated well.

Optimal design of structural parameters for shield cutterhead based on fuzzy mathematics and multi-objective genetic algorithm

In order to improve the strength and stiffness of shield cutterhead, the method of fuzzy mathematics theory in combination with the finite element analysis is adopted. An optimal design model of structural parameters for shield cutterhead is formulated,based on the complex engineering technical requirements. In the model, as the objective function of the model is a composite function of the strength and stiffness, the response surface method is applied to formulate the approximate function of objective function in order to reduce the solution scale of optimal problem. A multi-objective genetic algorithm is used to solve the cutterhead structure design problem and the change rule of the stress-strain with various structural parameters as well as their optimal values were researched under specific geological conditions. The results show that compared with original cutterhead structure scheme, the obtained optimal scheme of the cutterhead structure can greatly improve the strength and stiffness of the cutterhead, which can be seen from the reduction of its maximum equivalent stress by 21.2%, that of its maximum deformation by 0.75%, and that of its mass by 1.04%.

Optimization of Multi-Project Multi-Site Location Based on MOPSOs

Multi-project multi-site location problems are multi-objective combinational optimization ones with discrete variables which are hard to solve. To do so, the case of particle swarm optimization is considered due to its useful char- acteristics such as easy implantation, simple parameter settings and fast convergence. First these problems are trans- formed into ones with continuous variables by defining an equivalent probability matrix in this paper, then multi-objective particle swarm optimization based on the minimal particle angle is used to solve them. Methods such as continuation of discrete variables, update of particles for matrix variables, normalization of particle position and evalua- tion of particle fitness are presented. Finally the efficiency of the proposed method is validated by comparing it with other methods on an eight-project-ten-site location problem.

Synergetic Optimization of Missile Shapes for Aerodynamic and Radar Cross-Section Performance Based on Multi-objective Evolutionary Algorithm

A multiple-objective evolutionary algorithm (MOEA) with a new Decision Making (DM) scheme for MOD of conceptual missile shapes was presented, which is contrived to determine suitable tradeoffs from Pareto optimal set using interactive preference articulation. There are two objective functions, to maximize ratio of lift to drag and to minimize radar cross-section (RCS) value. 3D computational electromagnetic solver was used to evaluate RCS, electromagnetic performance. 3D Navier-Stokes flow solver was adopted to evaluate aerodynamic performance. A flight mechanics solver was used to analyze the stability of the missile. Based on the MOEA, a synergetic optimization of missile shapes for aerodynamic and radar cross-section performance is completed. The results show that the proposed approach can be used in more complex optimization case of flight vehicles.

Multi-Objective Genetic Algorithm to Design Manufacturing Process Line Including Feasible and Infeasible Solutions in Neighborhood

This paper treats multi-objective problem for manufacturing process design. A purpose of the process design is to decide combinations of work elements assigned to different work centers. Multiple work elements are ordinarily assigned to each center. Here, infeasible solutions are easily generated by precedence relationship of work elements in process design. The number of infeasible solutions generated is ordinarily larger than that of feasible solutions generated in the process. Therefore, feasible and infeasible solutions are located in any neighborhood in solution space. It is difficult to seek high quality Pareto solutions in this problem by using conventional multi-objective evolutional algorithms. We consider that the problem includes difficulty to seek high quality solutions by the following characteristics： （1） Since infeasible solutions are resemble to good feasible solutions, many infeasible solutions which have good values of objective functions are easily sought in the search process, （2） Infeasible solutions are useful to select new variable conditions generating good feasible solutions in search process. In this study, a multi-objective genetic algorithm including local search is proposed using these characteristics. Maximum value of average operation times and maximum value of dispersion of operation time in all work centers are used as objective functions to promote productivity. The optimal weighted coefficient is introduced to control the ratio of feasible solutions to all solutions selected in crossover and selection process in the algorithm. This paper shows the effectiveness of the proposed algorithm on simple model.

Multi-objectives and Simulation Models for Develop Production Line in Automobiles Manufacturing System

Automotive manufacturing is complex and includes the coordination of design in the manufacturing system. The manufacturing approaches over the past few years, disassembly have been a key issue, and it seems that simulation models are usually tailored to address a narrow set of industrial issues. This paper describes the development of the production line in the automobile manufacturing system through design, operation, and maintenance, based on multi-objectives of Algorithm and Simulation Model called MOA-SM. The both models are being developed at three different levels： the production line in the body and assembly shop, supply chain, and the production plan. The optimization tries to involve more objectives to solve the issues in manufacturing system. A solution that may optimize one performance measure may deteriorate since other performance solutions are difficult. The resulting algorithms are comparable to the simulation and multi-object in terms of success rate, assembly times, peak forces, moments, and have assembly times superior to those of a benchmark blind search algorithm.

A Self-Position Estimation Algorithm for Multiple Mobile Robots Using Two Omnidirectional Cameras and an Accelerometer

This paper proposes a self-position estimate algorithm for the multiple mobile robots; each robot uses two omnidirectional cameras and an accelerometer. In recent years, the Great East Japan Earthquake and large-scale disasters have occurred frequently in Japan. From this, development of the searching robot which supports the rescue team to perform a relief activity at a large-scale disaster is indispensable. Then, this research has developed the searching robot group system with two or more mobile robots. In this research, the searching robot equips with two omnidirectional cameras and an accelerometer. In order to perform distance measurement using two omnidirectional cameras, each parameter of an omnidirectional camera and the position and posture between two omnidirectional cameras have to be calibrated in advance. If there are few mobile robots, the calibration time of each omnidirectional camera does not pose a problem. However, if the calibration is separately performed when using two or more robots in a disaster site, etc., it will take huge calibration time. Then, this paper proposed the algorithm which estimates a mobile robot＇s position and the parameter of the position and posture between two omnidirectional cameras simultaneously. The algorithm proposed in this paper extended Nonlinear Transformation （NLT） Method. This paper conducted the simulation experiment to check the validity of the proposed algorithm. In some simulation experiments, one mobile robot moves and observes the circumference of another mobile robot which has stopped at a certain place. This paper verified whether the mobile robot can estimate position using the measurement value when the number of observation times becomes 10 times in n/18 of observation intervals. The result of the simulation shows the effectiveness of the algorithm.