Multi-objective Optimization Design of Wing Structure with the Model Management Framework

Evolutionary algorithm is time-consuming because of the large number of evolutions and much times of finite element analysis, when it is used to optimize the wing structure of a certain high altitude long endurance unmanned aviation vehicle（UAV）. In order to improve efficiency it is proposed to construct a model management framework to perform the multi-objective optimization design of wing structure. The sufficient accurate approximation models of objective and constraint functions in the wing structure optimization model are built when using the model management framework, therefore in the evolutionary algorithm a number of finite element analyses can he avoided and the satisfactory multi-objective optimization results of the wing structure of the high altitude long endurance UAV are obtained.

Initiative Optimization Operation Strategy and Multi-objective Energy Management Method for Combined Cooling Heating and Power

This paper proposed an initiative optimization operation strategy and multi-objective energy management method for combined cooling heating and power U+0028 CCHP U+0029 with storage systems. Initially, the initiative optimization operation strategy of CCHP system in the cooling season, the heating season and the transition season was formulated. The energy management of CCHP system was optimized by the multi-objective optimization model with maximum daily energy efficiency, minimum daily carbon emissions and minimum daily operation cost based on the proposed initiative optimization operation strategy. Furthermore, the pareto optimal solution set was solved by using the niche particle swarm multi-objective optimization algorithm. Ultimately, the most satisfactory energy management scheme was obtained by using the technique for order preference by similarity to ideal solution U+0028 TOPSIS U+0029 method. A case study of CCHP system used in a hospital in the north of China validated the effectiveness of this method. The results showed that the satisfactory energy management scheme of CCHP system was obtained based on this initiative optimization operation strategy and multi-objective energy management method. The CCHP system has achieved better energy efficiency, environmental protection and economic benefits. © 2014 Chinese Association of Automation.

MODS： A Novel Metaheuristic of Deterministic Swapping for the Multi-Objective Optimization of Combinatorials Problems

This paper states a new metaheuristic based on Deterministic Finite Automata （DFA） for the multi - objective optimization of combinatorial problems. First, a new DFA named Multi - Objective Deterministic Finite Automata （MDFA） is defined. MDFA allows the representation of the feasible solutions space of combinatorial problems. Second, it is defined and implemented a metaheuritic based on MDFA theory. It is named Metaheuristic of Deterministic Swapping （MODS）. MODS is a local search strategy that works using a MDFA. Due to this, MODS never take into account unfeasible solutions. Hence, it is not necessary to verify the problem constraints for a new solution found. Lastly, MODS is tested using well know instances of the Bi-Objective Traveling Salesman Problem （TSP） from TSPLIB. Its results were compared with eight Ant Colony inspired algorithms and two Genetic algorithms taken from the specialized literature. The comparison was made using metrics such as Spacing, Generational Distance, Inverse Generational Distance and No-Dominated Generation Vectors. In every case, the MODS results on the metrics were always better and in some of those cases, the superiority was 100%.

MULTI-OBJECTIVE PROGRAMMING FOR AIRPORT GATE REASSIGNMENT

To improve the efficiency of gate reassignment and optimize the plan of gate reassignment,the concept of disruption management is introduced,and a multi-objective programming model for airport gate reassignment is proposed.Considering the interests of passengers and the airport,the model minimizes the total flight delay,the total passengers′walking distance and the number of flights reassigned to other gates different from the planned ones.According to the characteristics of the gate reassignment,the model is simplified.As the multi-objective programming model is hard to reach the optimal solutions simultaneously,a threshold of satisfactory solutions of the model is set.Then a simulated annealing algorithm is designed for the model.Case studies show that the model decreases the total flight delay to the satisfactory solutions,and minimizes the total passengers′walking distance.The least change of planned assignment is also reached.The results achieve the goals of disruption management.Therefore,the model is verified to be effective.

Multi-features Based Approach for Moving Shadow Detection

In the video-based surveillance application, moving shadows can affect the correct localization and detection of moving objects. This paper aims to present a method for shadow detection and suppression used for moving visual object detection. The major novelty of the shadow suppression is the integration of several features including photometric invariant color feature, motion edge feature, and spatial feature etc. By modifying process for false shadow detected, the averaging detection rate of moving object reaches above 90% in the test of Hall-Monitor sequence.

Multi-objective energy management system for DC microgrids based on maximum membership degree principle

With the increasing quantity of DC electrical equipment, DC microgrids have been paid more and more attention. This paper proposes an approach to multi-objective optimisation of an energy management system(EMS) for a DC microgrid that includes a hybrid energy storage system(HESS). The operating and maintenance cost and the loss of power supply probability(LPSP) of the system are used as optimisation targets. The power flows of all distributed generators(DGs) in the DC microgrid during operating period are optimized. Based on the improved differential evolution(DE) algorithm, and by using the multi-objective non-dominated sorting method and the maximum membership degree principle(MMDP) of fuzzy control, the overall satisfaction degree of Pareto solutions to power flow optimization can be obtained. Simulation results verify the effectiveness of the proposed EMS optimization scheme, which is able to achieve an effective trade-off between the economy and the reliability of microgrid operation.

Which type of forest management provides most ecosystem services？

Background： Forest ecosystems are increasingly seen as multi-functional production systems, which should provide, besides timber and economic benefits, also other ecosystem services related to biological diversity, recreational uses and environmental functions of forests. This study analyzed the performance of even-aged rotation forest management （RFM）, continuous cover forestry （CCF） and any-aged forestry （AAF） in the production of ecosystem services. AAF allows both even-aged and uneven-aged management schedules. The ecosystem services included in the analyses were net present value, volume of harvested timber, cowberry and bilberry yields, scenic value of the forest, carbon balance and suitability of the forest to Siberian jay. Methods： Data envelopment analysis was used to derive numerical efficiency ratios for the three management systems. Efficiency ratio is the sum of weighted outputs （ecosystem services） divided by the sum of weighted inputs. The linear programing model proposed by Charnes, Cooper and Rhodes was used to derive the weights for calculating efficiency scores for the silvicultural systems. Results and conclusions： CCF provided more ecosystem services than RFM, and CCF was more efficient than RFM and AAF in the production of ecosystem services. Multi-objective management provided more ecosystem services （except harvested timber） than single-objective management that maximized economic profitability. The use of low discount rate （resulting in low cutting level and high growing stock volume） led to better supply of most ecosystems services than the use of high discount rate. RFM where NPV was maximized with high discount rate led to particularly poor provision of most ecosystem services. In CCF the provision of ecosystem services was less sensitive to changes in discount rate and management objective than in RFM.

Computer-aided Pattern Design of Multi-bar Warp Knitted Fabrics

To meet the development trend of multi-bar warp knit-ting machine towards high-speed,advanced technologyand computer control and the requirements of variousproducts with small quantity,there are many researcheson the computer-aided pattern design of multi-barwarp knitted fabrics.In terms of the special propertiesof the computer-aided pattern design of multi-barwarp knitted fabrics,the Object Oriented Program(OOP)programming-Object Windows class Library(OWL)programming is selected.According to thecharacters of the OWL programming,various functionsare defined.Pattern design and technical parameters canbe output,which offers a great convenience for the fac-tory.

Multi-object approach and its appfication to adaptive water management under climate change

This paper addresses issues on adaptive water management under the impact of climate change. Based on a set of comprehensive indicators of water system, a decision making approach of multi-objects is developed and applied to quantify water adaptive man- agement for the demands of water sustainable use, water environmental protection and eco-water requirement under the climate change. For this study in China, two key indicators are proposed, namely （1） the water resources vulnerability （V） that was represented by inte- grated sensitivity （S） and resilience （C） of climate change impact on water resources, and （2） the sustainability of socio-economy and water environment, marked by DD, that is integrated scaler of socio-economic development （EG） based on the amount of GDP and the water en- vironment and relative eco-system quality （LI）. To find a reasonable solution for adaptive water management, a multi-objective decision making model of adaptive water management is further developed and the multi-objective model was transformed into an integrated single optimization model through developing an integrated measure function, called as VDD=DD/V. This approach has been applied to adaptive water resources planning and management for case study of China with new policy, called as the strict management of water resources based on three red line controls, i.e., the control of total water use by the total water re- sources allocation, the control of lower water use efficiency by the water demand manage- ment and the control of the total waste water load by water quality management in the East- ern China Monsoon Region that covers major eight big river basins including Yangtze River, Yellow River, Haihe River and Huaihe River. It is shown that the synthetic representation of water resource vulnerability and socio-economic sustainability by the integrated objective function （VDD） and integrated decision making model are workable and practicable. Adaptive management effect of the criterion compliance rate and water use efficiency are more ap- preciable through new water policy of the three red line controls, which can reduce 21.3% of the water resources vulnerability （V） and increase 18.4% of the sustainability of socio- economy and water environment （DD） for the unfavorable scenario of climate change in2030.

包头市地下水-地表水联合调度多目标管理模型

运用管理模型对地下水和地表水资源优化调度 ,解决包头市水资源短缺问题。首先建立双层含水层地下水流数值模拟模型 ,并求得地下水系统单位脉冲响应函数。考虑水资源与社会、经济和环境的关系 ,构建地下水 -地表水联合调度多目标管理模型。决策变量为潜水和承压水开采量、各地表水厂向各管理子区的供水量 ,主要目标为最大限度完成工业总产值、供水费用最小、最优控制地下水位和尽量满足农业灌溉用水。模型归结为求解线性目标规划问题。结果表明 ,优化总供水量为 6663 8 9× 1 0 4 m3·a- 1 ,其中地下水供水量为 1 3 5 0 6 2× 1 0 4 m3·a- 1 ;优化方案最大限度地满足规划需水量 ,使有限的水资源发挥最大的经济效益 ;通过对地下水开采量的控制 ,地下水流场趋于合理。

兼容需求侧资源的“源-网-荷-储”协调优化调度模型

提出通过提高需求侧和供应侧资源的协调可控性来应对当前电力系统双侧随机问题的新思路,在此基础上设计需求侧响应模型、储能设备充放电模型、风电及光伏发电出力预测模型,并构建以系统成本及污染排放最小化为目标函数的"源-网-荷-储"优化调度模型及相应的多目标粒子群优化算法。通过算例分析比较有无需求侧资源情况下的系统成本和污染排放,验证了所提模型和算法的科学性与合理性,以及需求侧资源在提高系统稳定性、节能减排方面的重要作用。

基于Pareto原理的HEV能量控制参数NSGA-Ⅱ多目标优化的研究

以某款并联混合动力汽车为对象,选取8个能量控制参数作为燃油经济性和排放性综合优化参数,提出基于Pareto原理的改进型NSGA-Ⅱ多目标优化算法,并进行仿真优化。结果表明:优化后燃油消耗率最大降低了11.29%,排放物综合指标最大下降8.78%,其中CO排放的优化效果最显著,下降了24.2%;SOC平衡的误差在0.5%以内,满足约束条件,发动机与电机工作点的效率分布明显改进;同时相比传统加权等单目标优化法,所提出的算法能同时得到多组优化解,为能量管理前期设计提供了更多的选择空间。

采用NSGA-Ⅱ算法的混合动力能量管理控制多目标优化方法

综合考虑燃油经济性、排放性与驾驶性对混合动力能量管理控制优化的优点,以某款并联混合动力汽车为研究对象,选取能量管理控制参数与传动系参数作为待优化参数,以动力性作为约束条件,建立混合动力能量管理控制多目标优化评价方法,提出基于NSGA-Ⅱ算法的混合动力系统多目标优化方法,并与优化前控制策略进行仿真对比分析。结果表明:在满足基本约束的前提下,优化后燃油经济性最多提高了7.8%,平均提高了6.38%;驾驶性性能指标最多提高了27.12%,平均提高了21.74%;排放性综合指标平均提高了41.51%。提出的多目标优化算法具有良好的收敛性与分布性,得到的Pareto最优解集能够给混合动力能量管理控制策略提供更多的权衡选择方案,体现了多目标优化的优势。

T-JIT环境下企业供应链中采购管理供应商选择和订单分配

随着经济和社会的迅猛发展,企业之间的竞争主要体现在供应链之间的竞争。供应链可分为计划、采购、制造、配送和退货5大部分,而采购管理是供应链间竞争的关键,是联系供应链企业成员中供应商和销售者的纽带,也是企业保持稳固发展的核心所在。为了对采购管理中供应商选择和订单分配问题进行优化,提高企业采购管理效率,以T-JIT拉式生产方式的企业环境为研究背景,首先,概括出了影响企业供应链管理的多目标因素,即信息共享成本、交货成本、库存成本、采购成本和质量成本。其次,采用数理统计理论和优化算法,借助MATLAB和lingo软件,构建了T-JIT环境下企业供应链中采购管理的两阶段分析法,即利用AHP-EM和TOPSIS法对阶段1进行供应商选择,应用阶段1中的权重采用多目标混合整数规划模型对阶段2进行订单分配研究。最后,依托实例,提出了信息共享率对供应商选择、订单分配和总成本的影响,得出T-JIT环境下对该问题的解决优于一般环境。该问题的研究对扩展供应链系统管理理论、提高企业供应链管理水平和增强企业核心竞争力具有重要的理论和实践指导意义。

基于NSGA-Ⅱ算法的逆向可变车道信号配时优化

以相位有效绿灯时间、饱和度和逆向可变车道长度为约束条件,建立交叉口控制延误时间最小、交叉口通行能力最大的双目标函数控制模型,运用NSGA-Ⅱ双目标优化算法,求解目标函数,获得以通行能力相对占优的信号配时方案,达到交叉口控制效率提高的目标。以重庆某交叉口实际数据为例,用NSGA-Ⅱ算法对设置可变车道后的配时优化方案求解,并与现状配时方案进行对比,设置逆向可变车道和优化信号配时能够提高交叉口通行能力,降低控制延误。

基于NSGA-Ⅱ算法的集雨型绿地低影响开发设施规模优化计算方法及应用——以南阳院士小镇为例

目前,在集雨型绿地设计过程中,进行低影响开发(low impact development,简称LID)设施规模设计时存在目标单一、缺乏可量化规模依据等现象,且布设效率低下,雨洪模拟和设计流程衔接不畅。在参数化设计平台Grasshopper中建立多目标优化平台,以雨洪控制效果和建设成本为目标,运用NSGA-Ⅱ算法对LID设施规模变量进行优化求解,从而在设计前期为LID设施规模设计提供定量依据。在此基础上,将研究成果应用于南阳院士小镇集雨型绿地设计项目,并与传统开发方法进行比较,结果表明该优化计算方法可高效地为LID设施规模设计提供科学的定量依据。根据计算结果,发现在研究区选用的LID设施组合条件下,LID设施的建设成本和雨洪控制效益符合边际递减规律,因此LID设施规模须与海绵规划要求匹配,在一定条件下,适当提升透水铺装和生物滞留设施的比例有利于提升LID设施的性价比。研究成果可以为今后集雨型绿地设计提供新思路。

基于GA-BFO的城镇住宅家庭用能多目标优化调度策略

针对城镇住宅家庭设备等用能的特点,建立一种包含多种可调度负载的家庭用能模型。以最小用户电力成本和最小负荷峰均比为优化目标,提出一种日前优化调度策略,并采用一种改进的细菌觅食优化算法进行求解。考虑用户临时需求变化对日前优化调度结果的影响,提出一种对短时间内的用能安排进行重新调度的实时优化调度策略,并引入0-1背包问题和动态规划方程对上述问题进行求解。算例结果验证了所提策略和改进算法的有效性。

基于Kriging模型的改进型NSGA-Ⅲ解决昂贵优化问题

在许多实际的优化问题中,为了进行适应度评估,其物理实验或数值仿真代价高昂,这给大多数现有的多目标进化算法(EAs)带来了巨大挑战。因此,文中提出了一种基于克里金模型辅助的改进参考点引导进化的优化算法,用于解决昂贵的超多目标优化问题。具体而言,根据种群的空间分布特征,借助关联点的熵差信息筛选参考点引导进化,以达到探索与开发的平衡。所提出的代理辅助进化算法(SAEA)使用克里金法来逼近每个目标函数,而无需进行原始昂贵的函数评估从而降低了计算成本。模型管理中采用一种纯指标填充采样准则,借助收敛性、多样性指标确定适当采样策略并使用昂贵目标函数对采样解进行真实评估以提升种群收敛与算法优化的效率。对具有3个以上目标的80个DTLZ与WFG基准测试问题进行了对比研究,证明了此算法的有效性和可行性。

基于NSGA-Ⅱ优化的船舶串联式混合动力系统能量管理策略

以船舶串联式混合动力系统的能量管理策略为研究对象,建立动力系统数学模型,采用实时性好的基于逻辑规则的能量管理策略,提出基于多目标优化算法NSGA-Ⅱ优化逻辑规则中的逻辑门限值,以降低油耗和碳排放。某内河运输船循环工况的仿真结果表明,比较传统动力系统,采用优化能量管理策略的混合动力系统节油11.09%,减少碳排放4.32%;比较基于经验的逻辑规则,优化的能量管理策略节油1.18%,减少碳排放2.46%。

投资决策中的0-1背包问题的扰动修复

针对投资决策中的0 1背包问题,提出了在应急管理下0 1背包问题的扰动修复的多目标决策模型,此模型反映了多变的经济条件下,公司或企业的项目投资资金分配计划受到扰动时积极应对扰动的决策需要.本文利用线性加权和法将其转化为单目标决策优化模型,并证明这个单目标决策优化模型为0 1背包问题,采用分枝定界方法求解,并对权系数取值进行了分析,同时结合实例验证了模型的合理性和有效性.