Models for Location Inventory Routing Problem of Cold Chain Logistics with NSGA-Ⅱ Algorithm

In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem.

多目标遗传算法NSGA-Ⅱ在某双前桥转向机构优化设计中的应用

针对东风某双前桥转向重型汽车在使用中存在的轮胎异常磨损问题,利用ADAMS/View软件建立了样车双前桥转向机构参数化仿真模型,基于选择的设计变量与目标函数,在iSIGHT软件中通过集成ADAMS/View模型,对设计变量进行了DOE分析,并利用改进的非支配排序遗传算法NSGA-Ⅱ实现了双前桥转向机构的多目标优化,根据Pareto最优解得到仿真结果表明:优化后各车轮转角误差大大减小,可有效解决车轮异常磨损问题。利用多目标遗传算法和计算机仿真集成技术对转向机构进行优化设计,可为今后汽车系统的设计、开发提供新的有效途径。

Suspended sediment load prediction using non-dominated sorting genetic algorithm Ⅱ

Awareness of suspended sediment load (SSL) and its continuous monitoring plays an important role in soil erosion studies and watershed management.Despite the common use of the conventional model of the sediment rating curve (SRC) and the methods proposed to correct it,the results of this model are still not sufficiently accurate.In this study,in order to increase the efficiency of SRC model,a multi-objective optimization approach is proposed using the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) algorithm.The instantaneous flow discharge and SSL data from the Ramian hydrometric station on the Ghorichay River,Iran are used as a case study.In the first part of the study,using self-organizing map (SOM),an unsupervised artificial neural network,the data were clustered and classified as two homogeneous groups as 70% and 30% for use in calibration and evaluation of SRC models,respectively.In the second part of the study,two different groups of SRC model comprised of conventional SRC models and optimized models (single and multi-objective optimization algorithms) were extracted from calibration data set and their performance was evaluated.The comparative analysis of the results revealed that the optimal SRC model achieved through NSGA-Ⅱ algorithm was superior to the SRC models in the daily SSL estimation for the data used in this study.Given that the use of the SRC model is common,the proposed model in this study can increase the efficiency of this regression model.

Parametric optimization of electrochemical machining of Al/15% SiC_p composites using NSGA-Ⅱ

Electrochemical machining（ECM） is one of the important non-traditional machining processes,which is used for machining of difficult-to-machine materials and intricate profiles.Being a complex process,it is very difficult to determine optimal parameters for improving cutting performance.Metal removal rate and surface roughness are the most important output parameters,which decide the cutting performance.There is no single optimal combination of cutting parameters,as their influences on the metal removal rate and the surface roughness are quite opposite.A multiple regression model was used to represent relationship between input and output variables and a multi-objective optimization method based on a non-dominated sorting genetic algorithm-Ⅱ（NSGA-Ⅱ） was used to optimize ECM process.A non-dominated solution set was obtained.

OPTIMIZATION ON ANTENNA PATTERN OF SPACEBORNE SAR WITH IMPROVED NSGA-Ⅱ

Optimization of antenna array pattern used in a spaceborne Synthetic Aperture Radar (SAR) system is considered in this study. A robust evolutionary algorithm, Non-dominated Sorting Genetic Algorithms (the improved NSGA-Ⅱ), is applied on a spaceborne SAR antenna pattern design. The system consists of two objective functions with two constraints. Pareto fronts are generated as a result of multi-objective optimization. After being validated by a test problem ZDT4, the algorithms are used to synthesize spaceborne SAR antenna radiation pattern. The good results with low Ambi- guity-to-Signal Ratio (ASR) and high directivity are obtained in the paper.

Multi-objective Function Optimization for Environmental Control of a Greenhouse Based on a RBF and NSGA-Ⅱ

To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.

NSGAⅡ在供应商选择中的应用

针对传统多目标优化过程中参数难以选择的情况,采用NSGAⅡ解决供应商选择问题,为企业选择供应商提供一套有效的决策方案。首先,建立以质量最大化、售后服务最大化、价格最小化和时间最小化为实现目标,以总需求、供应能力、采购策略、采购量为约束条件的供应商选择模型。其次,供应商选择模型将采用NSGAⅡ对其进行求解。最后,将NSGAⅡ和加权求和法进行实验比较。实验结果表明,与传统的加权求合法方法相比,NSGAⅡ不需要引入权重或约束条件,从而避免了人为干预,只需要一次运算就可以获得一组能同时接近各个目标的Pareto解,为供应商选择提供较好的选择。

智慧交通场景下云边端协同的多目标优化卸载决策

随着智慧交通、云计算网络以及边缘计算网络的快速发展,车载终端与路基单元、中心云服务器之间的信息交互变得越发频繁。针对智慧交通云边端协同计算场景下如何高效地实现车路云一体化融合感知、群体决策以及各级服务器间对资源的合理分配问题,设计了基于云边端与智慧交通全面融合的网络架构。在该架构下,通过对任务类型的合理划分,再由各服务器对其进行选择性的缓存、卸载;在智慧交通云边端协同计算场景下,依次设计了一种对任务自适应的缓存模型、任务卸载时延模型、系统能量损耗模型、车载用户对服务质量不满意度评价模型、多目标优化问题模型,并给出了一种基于改进型非支配遗传算法的任务卸载决策方案。实验结果表明,文中方案能够有效降低任务卸载过程中所带来的时延和能耗,提高了系统资源利用率,给车辆用户带来更好的服务体验。

考虑交货期的双资源柔性作业车间节能调度

为解决含有机器和工人双资源约束的柔性作业车间节能调度问题,在考虑交货期的基础上,建立了以总提前和拖期惩罚值及总能耗最小为目标的双资源柔性作业车间节能调度模型。提出了一种改进的非支配排序遗传算法(improved non-dominated sorting genetic algorithmⅡ,INSGA-Ⅱ)进行求解。针对所优化的目标,设计了一种三阶段解码方法以获得高质量的可行解;利用动态自适应交叉和变异算子以获得更多优良个体;改进拥挤距离以获得收敛性和分布性更优的种群。将INSGA-Ⅱ与多种多目标优化算法进行对比分析,实验结果表明所提算法可行且有效。

基于混合遗传蚁群算法的多目标FJSP问题研究

针对多目标柔性作业车间调度问题求解过程中未综合考虑解集多样性与求解效率的问题,提出了一种混合遗传蚁群算法来求解。首先,通过改进的NSGA-Ⅱ(non-dominated sorting genetic algorithmⅡ)获取问题的较优解,以此来确定蚁群算法的初始信息素分布;其次,根据提出的自适应伪随机比例规则和改进的信息素更新规则来优化蚂蚁的遍历过程;最后,通过邻域搜索,扩大蚂蚁的搜索空间,从而提高解集的多样性。通过Kacem和BRdata算例进行实验验证,证明混合遗传蚁群算法具有更高的求解效率和更好解集多样性。

Multi-objective optimization of the cathode catalyst layer micro-composition of polymer electrolyte membrane fuel cells using a multi-scale,two-phase fuel cell model and data-driven surrogates

Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectiveness of using platinum(Pt)in them.The cathode catalyst layer(CL)is considered a core component in PEMFCs,and its composition often considerably affects the cell performance(V_(cell))also PEMFC fabrication and production(C_(stack))costs.In this study,a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcelland Cstack.Four essential cathode CL parameters,i.e.,platinum loading(L_(Pt)),weight ratio of ionomer to carbon(wt_(I/C)),weight ratio of Pt to carbon(wt_(Pt/c)),and porosity of cathode CL(ε_(cCL)),are considered as the design variables.The simulation results of a three-dimensional,multi-scale,two-phase comprehensive PEMFC model are used to train and test two famous surrogates:multi-layer perceptron(MLP)and response surface analysis(RSA).Their accuracies are verified using root mean square error and adjusted R^(2).MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithmⅡ.Compared to a typical PEMFC stack,the results of the optimal study show that the single-cell voltage,Vcellis improved by 28 m V for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by$5.86/k W for the same stack performance.

油田配电网最优设备选型模型与算法

对油田配电网机采井变配电设备进行优化选型已成为油田节能降耗的重要问题。详细分析设备电气性能、成本效益的基础上,建立了以变配电设备节能与经济性为目标的最优选型的数学模型并确定了优化算法;针对该离散非线性多目标优化模型,采用非支配排序遗传算法NSGA-Ⅱ(non-dominated sorting genetic algorithm)进行优化选型,并结合变配电设备选型特点进行适当改进,从而提高了算法的计算速度和精度。最后,通过实例分析验证了该模型与算法的有效性与实用性,得到分布较好的Pareto最优解集,对决策者在相互关联的多个目标之间进行优选提供指导价值。

Multi-objective Optimization of Industrial Purified Terephthalic Acid Oxidation Process

Multi-objective optimization of a purified terephthalic acid (PTA) oxidation unit is carried out in this paper by using a process modei that has been proved to describe industrial process quite well. The modei is a semi-empirical structured into two series ideal continuously stirred tank reactor (CSTR) models. The optimal objectives include maximizing the yield or inlet rate and minimizing the concentration of 4-carboxy-benzaldhyde, which is the main undesirable intermediate product in the reaction process. The multi-objective optimization algorithra applied in this study is non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ). The performance of NSGA-Ⅱ is further illustrated by application to the title process.

Multi-objective optimization of methane production system from biomass through anaerobic digestion

This work addressed the multi-objective optimization of a biogas production system considering both environmental and economic criteria. A mixed integer non-linear programming(MINLP) model was established and solved with non-dominated sorting genetic algorithm Ⅱ, from which the Pareto fronts, the optimal technology combinations and operation conditions were obtained and analyzed. It's found that the system is feasible in both environmental and economic considerations after optimization. The most expensive processing section is decarbonization; the most expensive equipment is anaerobic digester; the most power-consuming processing section is digestion, followed by decarbonization and waste management. The positive green degree value on the process is attributed to processing section of digestion and waste management. 3:1 chicken feces and corn straw, solar energy, pressure swing adsorption and 3:1 chicken feces and rice straw, solar energy, pressure swing adsorption are turned out to be two robust technology combinations under different prices of methane and electricity by sensitivity analysis. The optimization results provide support for optimal design and operation of biogas production system considering environmental and economic objectives.

Evolutionary genetic optimization of the injector beam dynamics for the ERL test facility at IHEP

The energy recovery linac test facility （ERL-TF）, a compact ERL-FEL （free electron laser） two-purpose machine, has been proposed at the Institute of High Energy Physics, Beijing. As one important component of the ERL-TF, the photo-injector was designed and preliminarily optimized. In this paper an evolutionary genetic method, non-dominated sorting genetic algorithm II, is applied to optimize the injector beam dynamics, especially in the high-charge operation mode. Study shows that using an incident laser with rms transverse size of 1-1.2 ram, the normalized emittance of the electron beam can be kept below 1 mm.mrad at the end of the injector. This work, together with the previous optimization of the low-charge operation mode by using the iterative scan method, provides guidance and confidence for future construction and commissioning of the ERL-TF injector.

Orbit Design for Responsive Space Using Multiple-objective Evolutionary Computation

Responsive orbits have exhibited advantages in emergencies for their excellent responsiveness and coverage to targets.Generally,there are several conflicting metrics to trade in the orbit design for responsive space.A special multiple-objective genetic algorithm,namely the Nondominated Sorting Genetic AlgorithmⅡ(NSGAⅡ),is used to design responsive orbits.This algorithm has considered the conflicting metrics of orbits to achieve the optimal solution,including the orbital elements and launch programs of responsive vehicles.Low-Earth fast access orbits and low-Earth repeat coverage orbits,two subtypes of responsive orbits,can be designed using NSGAI under given metric tradeoffs,number of vehicles,and launch mode.By selecting the optimal solution from the obtained Pareto fronts,a designer can process the metric tradeoffs conveniently in orbit design.Recurring to the flexibility of the algorithm,the NSGAI promotes the responsive orbit design further.

工业无线传感器网络攻击源定位任务分配优化算法

针对工业无线传感器网络中参与攻击源节点定位的任务分配问题,构建和求解多目标优化定位任务分配模型,任务分配模型中设定参考节点组合总能量消耗、距离平均标准偏差目标函数,以及空间约束和剩余能量约束条件;采用循环拥挤排序将非支配排序遗传算法(NSGA-Ⅱ)进行改进后加入基于稀疏度局部搜索的混合优化算法联合求解任务分配模型,将稀疏度最小的解作为稀疏解,再采用极限优化策略在稀疏解周围进行局部搜索使得解拥有更好的分布特性.Matlab仿真结果表明该改进的混合优化算法可以提高算法收敛速度以及降低算法复杂度,在较快的时间内选择出合适的参考节点组合,减少了定位误差,提高了定位精度.

Multi-objective simultaneous optimal planning of electric vehiclefast charging stations and DGs in distribution system

The large-scale construction of fast charging stations(FCSs)for electric vehicles(EVs)is helpful inpromoting the EV.It creates a significant challenge for the distribution system operator to determine the optimal planning,especially the siting and sizing of FCSs in the electrical distribution system.Inappropriate planning of fast EV charging stations(EVCSs)cause a negative impact on the distribution system.This paper presented a multiobjective optimization problem to obtain the simultaneous placement and sizing of FCSs and distributed generations(DGs)with the constraints such as the number of EVs in all zones and possible number of FCSs based on the road and electrical network in the proposed system.The problem is formulated as a mixed integer non-linear problem(MINLP)to optimize the loss of EV user,network power loss(NPL),FCS development cost and improve the voltage profile of the electrical distribution system.Non-dominated sorting genetic algorithmⅡ(NSGA-Ⅱ)is used for solving the MINLP.The performance of the proposed technique is evaluated by the 118-bus electrical distribution system.

Multi-objective Dimensional Optimization of a 3-DOF Translational PKM Considering Transmission Properties

Multi-objective dimensional optimization of parallel kinematic manipulators(PKMs) remains a challenging and worthwhile research endeavor. This paper presents a straightforward and systematic methodology for implementing the structure optimization analysis of a 3-prismatic-universal-universal(PUU) PKM when simultaneously considering motion transmission, velocity transmission and acceleration transmission. Firstly, inspired by a planar four-bar linkage mechanism, the motion transmission index of the spatial parallel manipulator is based on transmission angle which is defined as the pressure angle amongst limbs. Then, the velocity transmission index and acceleration transmission index are derived through the corresponding kinematics model. The multi-objective dimensional optimization under specific constraints is carried out by the improved non-dominated sorting genetic algorithm(NSGA Ⅱ), resulting in a set of Pareto optimal solutions. The final chosen solution shows that the manipulator with the optimized structure parameters can provide excellent motion, velocity and acceleration transmission properties.