Multidisciplinary Design Optimization of A Human Occupied Vehicle Based on Bi-Level Integrated System Collaborative Optimization

The design of Human Occupied Vehicle （HOV） is a typical multidisciplinary problem, but heavily dependent on the experience of naval architects at present engineering design. In order to relieve the experience dependence and improve the design, a new Multidisciplinary Design Optimization （MDO） method ＂Bi-Level Integrated System Collaborative Optimization （BLISCO）＂ is applied to the conceptual design of an HOV, which consists of hull module, resistance module, energy module, structure module, weight module, and the stability module. This design problem is defined by 21 design variables and 23 constraints, and its objective is to maximize the ratio of payload to weight. The results show that the general performance of the HOV can be greatly improved by BLISCO.

COVID-19 and Unemployment: A Novel Bi-Level Optimal Control Model

Since COVID-19 was declared as a pandemic in March 2020,the world’s major preoccupation has been to curb it while preserving the economy and reducing unemployment.This paper uses a novel Bi-Level Dynamic Optimal Control model(BLDOC)to coordinate control between COVID-19 and unemployment.The COVID-19 model is the upper level while the unemployment model is the lower level of the bi-level dynamic optimal control model.The BLDOC model’s main objectives are to minimize the number of individuals infected with COVID-19 and to minimize the unemployed individuals,and at the same time minimizing the cost of the containment strategies.We use the modified approximation Karush–Kuhn–Tucker(KKT)conditions with the Hamiltonian function to handle the bi-level dynamic optimal control model.We consider three control variables:The first control variable relates to government measures to curb the COVID-19 pandemic,i.e.,quarantine,social distancing,and personal protection;and the other two control variables relate to government interventions to reduce the unemployment rate,i.e.,employment,making individuals qualified,creating new jobs reviving the economy,reducing taxes.We investigate four different cases to verify the effect of control variables.Our results indicate that rather than focusing exclusively on only one problem,we need a balanced trade-off between controlling each.

A Lagrange Relaxation Based Approach to Solve a Discrete-Continous Bi-Level Model

In this work we propose a solution method based on Lagrange relaxation for discrete-continuous bi-level problems, with binary variables in the leading problem, considering the optimistic approach in bi-level programming. For the application of the method, the two-level problem is reformulated using the Karush-Kuhn-Tucker conditions. The resulting model is linearized taking advantage of the structure of the leading problem. Using a Lagrange relaxation algorithm, it is possible to find a global solution efficiently. The algorithm was tested to show how it performs.

An Alternative Approach for Solving Bi-Level Programming Problems

An algorithm is proposed in this paper for solving two-dimensional bi-level linear programming problems without making a graph. Based on the classification of constraints, algorithm removes all redundant constraints, which eliminate the possibility of cycling and the solution of the problem is reached in a finite number of steps. Example to illustrate the method is also included in the paper.

Bi-Level Programming for the Optimal Nonlinear Distance-Based Transit Fare Structure Incorporating Principal-Agent Game

The urban transit fare structure and level can largely affect passengers’travel behavior and route choices.The commonly used transit fare policies in the present transit network would lead to the unbalanced transit assignment and improper transit resources distribution.In order to distribute transit passenger flow evenly and efficiently,this paper introduces a new distance-based fare pattern with Euclidean distance.A bi-level programming model is developed for determining the optimal distance-based fare pattern,with the path-based stochastic transit assignment(STA)problem with elastic demand being proposed at the lower level.The upper-level intends to address a principal-agent game between transport authorities and transit enterprises pursing maximization of social welfare and financial interest,respectively.A genetic algorithm(GA)is implemented to solve the bi-level model,which is verified by a numerical example to illustrate that the proposed nonlinear distance-based fare pattern presents a better financial performance and distribution effect than other fare structures.

Bi-Level Energy Management Model of Grid-Connected Microgrid Community

As the proportion of renewable energy power generation continues to increase,the number of grid-connected microgrids is gradually increasing,and geographically adjacent microgrids can be interconnected to form a Micro-Grid Community(MGC).In order to reduce the operation and maintenance costs of a single micro grid and reduce the adverse effects caused by unnecessary energy interaction between the micro grid and the main grid while improving the overall economic benefits of the micro grid community,this paper proposes a bi-level energy management model with the optimization goal of maximizing the social welfare of the micro grid community and minimizing the total electricity cost of a single micro grid.The lower-level model optimizes the output of each equipment unit in the system and the exchange power between the system and the external grid with the goal of minimizing the operating cost of each microgrid.The upper-level model optimizes the goal ofmaximizing the socialwelfare of themicrogrid.Taking amicrogrid community with four microgrids as an example,the simulation analysis shows that the proposed optimization model is beneficial to reduce the operating cost of a single microgrid,improve the overall revenue of the microgrid community,and reduce the power interaction pressure on the main grid.

Clinical Analysis of Early Application of Bi-level Positive Airway Pressure ventilation in the Treatment of COPD with Type II Respiratory Failure

Objective:To analyze the clinical efficacy of early application of bi-level positive airway pressure ventilation in the treatment of COPD with type II respiratory failure.Method:A total of 58 patients with COPD and type II respiratory failure admitted to our hospital from January 2017 to January 2019 were randomly divided into observation group and control group,with 29 cases in each group.Among them,the control group was received routine treatment while the observation group was treated with bi-level positive pressure airway ventilation in addition of conventional treatment.The arterial blood gas analysis,mortality rate and hospitalization time of these two groups before and after treatment were compared.Result:The blood pH,partial pressure of oxygen(PaO2)and arterial oxygen saturation(SaO2)of these two groups were significantly higher after the treatment while PaO2 alone was decreased.The difference was statistically significant(P<0.05).The results of arterial blood gas analysis in the observation group were significantly improved compared with those before treatment.The mortality rate and hospitalization time were significantly less than the control group,and the difference was statistically significant(P<0.05).Conclusion:Early clinical application of bi-level positive airway pressure ventilation in the treatment of COPD with type II respiratory failure has a significant clinical effect in reducing the mortality rate and hospitalization time of patients,and thus it is worthy of clinical application.

Student Performance Prediction Using A Cascaded Bi-level Feature Selection Approach

Features in educational data are ambiguous which leads to noisy features and curse of dimensionality problems.These problems are solved via feature selection.There are existing models for features selection.These models were created using either a single-level embedded,wrapper-based or filter-based methods.However single-level filter-based methods ignore feature dependencies and ignore the interaction with the classifier.The embedded and wrapper based feature selection methods interact with the classifier,but they can only select the optimal subset for a particular classifier.So their selected features may be worse for other classifiers.Hence this research proposes a robust Cascade Bi-Level(CBL)feature selection technique for student performance prediction that will minimize the limitations of using a single-level technique.The proposed CBL feature selection technique consists of the Relief technique at first-level and the Particle Swarm Optimization(PSO)at the second-level.The proposed technique was evaluated using the UCI student performance dataset.In comparison with the performance of the single-level feature selection technique the proposed technique achieved an accuracy of 94.94%which was better than the values achieved by the single-level PSO with an accuracy of 93.67%for the binary classification task.These results show that CBL can effectively predict student performance.

Optimization of the bioconversion of glycerol to ethanol using Escherichia coli by implementing a bi-level programming framework for proposing gene transcription control strategies based on genetic algorithms

In silico approaches for metabolites optimization have been derived from the flood of sequenced and annotated genomes. However, there exist still numerous degrees of freedom in terms of optimization algorithm approaches that can be exploited in order to enhance yield of processes which are based on biological reactions. Here, we propose an evolutionary approach aiming to suggest different mutant for augmenting ethanol yield using glycerol as substrate in Escherichia coli. We found that this algorithm, even though is far from providing the global optimum, is able to uncover genes that a global optimizer would be incapable of. By over-expressing accB, eno, dapE, and accA mutants in ethanol production was augmented up to 2 fold compared to its counterpart E. coli BW25113.

基于bi-level迭代算法的物料循环配送研究

为解决车间物料配送费用高、配送效率低、灵活性差等问题,建立了基于bi-level(双层)规划的车间物料循环配送模型。针对模型设计了基于2-opt改进的最大最小蚁群算法和双层迭代算法,引入线旁库存的限制,求得需求点的配送周期、配送量和配送路径,使库存和运输整合费用最小。通过数值算例求解,说明了该算法是有效的,也说明了该模型的实用价值和有效性。

Bi-level programming model for reconstruction of urban branch road network

Considering the decision-making variables of the capacities of branch roads and the optimization targets of lowering the saturation of arterial roads and the reconstruction expense of branch roads, the bi-level programming model for reconstructing the branch roads was set up. The upper level model was for determining the enlarged capacities of the branch roads, and the lower level model was for calculating the flows of road sections via the user equilibrium traffic assignment method. The genetic algorithm for solving the bi-level model was designed to obtain the reconstruction capacities of the branch roads. The results show that by the bi-level model and its algorithm, the optimum scheme of urban branch roads reconstruction can be gained, which reduces the saturation of arterial roads apparently, and alleviates traffic congestion. In the data analysis the arterial saturation decreases from 1.100 to 0.996, which verifies the micro-circulation transportation's function of urban branch road network.

Algorithm for solving the bi-level decision making problem with continuous variables in the upper level based on genetic algorithm

Based on genetic algorithms, a solution algorithm is presented for the bi-level decision making problem with continuous variables in the upper level in accordance with the bi-level decision making principle. The algorithm is compared with Monte Carlo simulated annealing algorithm, and its feasibility and effectiveness are verified with two calculating examples.

NCPAP和Bi-level NCPAP治疗早产儿呼吸窘迫综合征的疗效评价及对炎症反应的影响

目的:评价经鼻持续气道正压通气(NCPAP)和双水平NCPAP(Bi-level NCPAP)对早产儿中度呼吸窘迫综合征(RDS)的治疗效果及对炎症反应的影响。方法:42例符合标准中度RDS的早产儿,胎龄28～34周,随机分为A、B两组,分别采用NCPAP治疗(压力6 cmH2O)和Bi-level NCPAP治疗(低压4.0 cm H2O,高压7.5 cm H2O)。在出生后第1、7日检测早产儿血清细胞因子(IL-6、IL-8、TNF-α)水平,记录患儿需要呼吸支持和氧依赖的时间以及出院时的胎龄,比较两组上述指标的差异。结果:两组早产儿均存活,无支气管肺发育不良或中枢神经系统疾病的发生。出生后第1、7日B组血清IL-6、IL-8、TNF-α水平均明显低于NCPAP组(P均<0.05)。两组早产儿组内不同时间血清三种细胞因子水平比较差异无统计学意义(P均>0.05)。A组需要呼吸支持的时间、氧依赖时间均长于B组(P均<0.05)、出院时胎龄大于B组(P<0.05)。结论:与NCPAP相比,Bi-level NCPAP能更好地改善通气、缩短呼吸支持和氧依赖的时间,缩短早产儿住院时间,所引起炎症反应程度也较NCPAP低,因此Bi-level NC-PAP对早产儿具有更好的耐受性和安全性。

Gradient-based algorithms for multi-objective bi-level optimization

Multi-objective bi-level optimization(MOBLO)addresses nested multi-objective optimization problems common in a range of applications.However,its multi-objective and hierarchical bi-level nature makes it notably complex.Gradient-based MOBLO algorithms have recently grown in popularity,as they effectively solve crucial machine learning problems like meta-learning,neural architecture search,and reinforcement learning.Unfortunately,these algorithms depend on solving a sequence of approximation subproblems with high accuracy,resulting in adverse time and memory complexity that lowers their numerical efficiency.To address this issue,we propose a gradient-based algorithm for MOBLO,called gMOBA,which has fewer hyperparameters to tune,making it both simple and efficient.Additionally,we demonstrate the theoretical validity by accomplishing the desirable Pareto stationarity.Numerical experiments confirm the practical efficiency of the proposed method and verify the theoretical results.To accelerate the convergence of gMOBA,we introduce a beneficial L2O(learning to optimize)neural network(called L2O-gMOBA)implemented as the initialization phase of our gMOBA algorithm.Comparative results of numerical experiments are presented to illustrate the performance of L2O-gMOBA.

Bi-level Optimal Planning of Voltage Regulator in Distribution Systems Considering Maximization of Incentive-based Photovoltaic Energy Integration

This paper focuses on optimal voltage regulator(VR)planning to maximize the photovoltaic(PV)energy integration in distribution grids.To describe the amount of dynamic PV energy that can be integrated into the power system,the concept of PV accommodation capability(PVAC)is introduced and modeled with optimization.Our proposed planning model is formulated as a Benders decomposition based bi-level stochastic optimization problem.In the upper-level problem,VR planning decisions and PVAC are determined via mixed integer linear programming(MILP)before considering uncertainty.Then in the lower-level problem,the feasibility of first-level results is checked by critical network constraints(e.g.voltage magnitude constraints and line capacity constraints)under uncertainties considered by time-varying loads and PV generations.In this paper,these uncertainties are represented in the form of operational scenarios,which are generated by the Gaussian copula theory and reduced by a well-studied backward-reduction algorithm.The modified IEEE 33-node distribution grid is utilized to verify the effectiveness of the proposed model.The results demonstrate that a PV energy integration can be significantly enhanced after optimal voltage regulator planning.

Deep Reinforcement Learning Enabled Bi-level Robust Parameter Optimization of Hydropower-dominated Systems for Damping Ultra-low Frequency Oscillation

This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations(ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control formulation that can only deal with a limited number of operating conditions, the proposed method reformulates the control problem into a bi-level robust parameter optimization model. This allows us to consider a wide range of system operating conditions. To speed up the bi-level optimization process, the deep deterministic policy gradient(DDPG) based deep reinforcement learning algorithm is developed to train an intelligent agent. This agent can provide very fast lower-level decision variables for the upper-level model, significantly enhancing its computational efficiency. Simulation results demonstrate that the proposed method can achieve much better damping control performance than other alternatives with slightly degraded dynamic response performance of the governor under various types of operating conditions.

考虑柔性负荷整形的含源配电线路升级与开关配置综合优化

针对高比例分布式光伏接入造成配电线路重过载的问题,提出一种基于时序互补线路互联和柔性负荷整形的配电线路升级与开关配置综合优化方法。考虑柔性负荷功率调节意愿、线路负载率与功率反送约束,提出柔性负荷整形潜力和线路分段整形需求计算方法。以此为基础,建立了考虑柔性负荷整形的含源配电线路升级与开关配置综合优化双层模型。其中,上层模型以配电线路年规划成本最低、线路段净负荷功率和柔性资源均衡为目标进行线路选型和开关配置,下层模型以线路年运行成本最低为目标进行网络重构和负荷曲线整形,通过上、下层模型交互迭代获得互联线路改造优化方案。上、下层模型分别采用非支配排序遗传算法、二阶锥与大M法进行求解。算例仿真结果表明,所提方法可以提高互联线路资产利用率、规划方案的经济性和可靠性,对互联线路进行柔性负荷整形有利于突破线路负载率50%的限制。

多品种电力市场交易下负荷聚合商投标策略及市场均衡分析

中国新型电力系统建设正面临灵活资源短缺等问题,亟须发挥负荷侧调节能力。随着电网与负荷双向交互越发频繁,更多负荷聚合商通过整合辖区灵活资源参与市场交易,导致以往由发电侧占主导的市场均衡被打破。为研究多主体负荷聚合商博弈策略,构建了能量-备用电力市场下负荷聚合商双层投标模型,并引入多场景描述非决策者投标不确定性对市场均衡的影响。通过强平稳算法(SSM)将多个双层模型转化为带均衡约束的均衡问题(EPEC)模型,并采用大M法、二进制展开法等线性化方法进行线性化。算例结果验证了模型及求解算法的合理性和有效性,表明基于EPEC模型的负荷聚合商最优投标策略更加契合实际市场规则,有助于系统优化负荷曲线,缓解灵活资源供需紧张。

智能垃圾回收下收集中心选址-路径二层优化

随着技术的进步及环保意识的提升,智能垃圾箱逐渐流行起来,使得垃圾回收工作面临新考验。针对带容量约束的选址-路径问题,引入双商品流公式构建垃圾回收选址-路径多主体优化模型,其中上层智能回收企业以总成本最低为目标确定收集中心选址;下层外包运输公司依据回收阈值选择需访问的智能垃圾箱,规划回收路径并确保运输成本最小化。改进遗传算法求解该问题:上层采用聚类算法处理选址初始化;下层路径为随机生成和节约里程初始化;引入最优成本路线交叉和反向变异算子。选取Prins和Barreto算例集测试,并与BKS、GAPSO和BSA算法对比,结果与BKS的差距均值仅为0.419%;通过模拟实际案例验证智能垃圾收集路径方式可有效降低总成本,为解决智能垃圾回收背景下选址-路径问题提供决策支持。

风光热储互补发电系统容量配置技术研究

针对无常规电源支撑的风光热储互补发电系统,协调规划装机容量对提高发电系统运行经济性和利用率具有重要意义。提出了一种双层优化配置方法,上层以最小度电成本及弃电率为目标,确定系统装机容量;下层以新能源发电消纳最大为目标,解决功率分配问题。通过反复迭代寻优,得到系统容量配置;然后;通过纳什谈判对优化结果进行选择;最后,对甘肃河西地区数据进行仿真分析。结果表明:风光热储互补发电系统最优容量配置下的度电成本为0.3064元/(k W·h);风电场加光伏电站装机容量与光热电站装机容量的最优比为6:1,对比相同装机容量的风光互补发电系统,风光热储互补发电系统具有更高的稳定性。