Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades

The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multi objective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines.

An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.

Planning of DC Electric Spring with Particle Swarm Optimization and Elitist Non-dominated Sorting Genetic Algorithm

This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical load (NCL) and internal storage. It can offer higher power quality to critical load (CL), reduce power imbalance and relieve pressure on energy storage systems (RESs). In this paper, a planning method for parallel DCESs is proposed to maximize stability gain, economic benefits, and penetration of RESs. The planning model is a master optimization with sub-optimization to highlight the priority of objectives. Master optimization is used to improve stability of the network, and sub-optimization aims to improve economic benefit and allowable penetration of RESs. This issue is a multivariable nonlinear mixed integer problem, requiring huge calculations by using common solvers. Therefore, particle Swarm optimization (PSO) and Elitist non-dominated sorting genetic algorithm (NSGA-II) were used to solve this model. Considering uncertainty of RESs, this paper verifies effectiveness of the proposed planning method on IEEE 33-bus system based on deterministic scenarios obtained by scenario analysis.

Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

Satellite constellation design with genetic algorithms based on system performance

Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.

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.

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.

低碳视角下多式联运网络设计优化问题研究

网络设计与低碳补贴激励措施,是推进多式联运可持续发展的重要途径。基于此,从低碳视角研究水陆联运网络设计优化与补贴模式问题,考虑政府管理部门与物流用户的互动博弈行为,构建基于双层规划的水陆联运物流网络优化模型。该模型中上层规划(政府层)确定网络扩容投资决策及其补贴方案,从而最小化扩容投资与低碳补贴总成本,以及系统中碳排放量;下层模型(物流用户)则是基于广义费用的用户均衡分配模型。针对上述双层优化模型的特点,设计了基于相继平均配流算法(MSA)的改进快速非支配排序遗传算法(NSGA-Ⅱ)。以长江经济带中游地区的水陆联运物流网络为例,进行相应的实证研究,验证上述优化模型及算法的有效性;并且,在对网络进行扩容投资的情况下,对比4种补贴方案,即1)对铁路及水运弧段按固定值进行补贴;2)不进行补贴;3)不同地区的铁路及水运弧段的补贴不同;4)补贴值随机连续。研究结果表明:扩容投资可以减少网络中的超载弧段数量,同时提升网络性能;对铁路及水运弧段进行运输补贴能有效降低碳排放量;若政府关注预算限制,则倾向于按固定值补贴的方案;若更重视碳减排效果,则倾向于采取不同地区不同补贴值的方案。

基于改进遗传算法的舾装件托盘多载具协同拣选方法

为提升舾装件托盘的拣选效率,建立拣选过程的数学模型,提出一种基于改进遗传算法(Improved Genetic Algorithm, IGA)的舾装件托盘多载具协同拣选方法。针对遗传算法(Genetic Algorithm, GA)流程与实际拣选过程的差异,改进GA的初始化过程和染色体交叉方式,并对变异过程进行更贴近实际生产的修改。针对GA难以得到全局最优解的问题,采用变邻域搜索(Variable Neighborhood Search, VNS)策略降低陷入局部最优解的可能性。采用实例计算验证该算法的有效性,可优化传统舾装件托盘拣选方法。

基于改进NSGA-Ⅱ算法的RV减速器参数多目标优化研究

旋转矢量(RV)减速器是工业机器人核心部件,对于机器人的性能起到关键作用。针对提升RV减速器综合性能的问题,从优化传动压力角的相关参数出发,对其结构参数(摆线轮齿数、短幅系数、针径系数、摆线轮宽度等)的多目标优化设计进行了研究。首先,研究了摆线轮平均压力角、传动效率和传动机构体积三者的相关参数之间的关系;然后,以此为优化目标,在摆线轮标准齿廓方程的基础上建立了多目标优化数学模型(该模型采用了基于非支配占优排序遗传学算法(NSGA-Ⅱ)改进了交叉算子系数生成的改进NSGA-Ⅱ算法);通过模型求解得到了帕累托最优解集,根据模糊集合理论的相关方法选取了最优解;最后,以某公司220-BX型RV减速器为例,进行了优化设计,建立了3D模型后进行了有限元分析,并加工出实验样机,进行了传动效率对比实验。实验结果表明:摆线轮平均压力角减小了7.19%,体积减小了11.1%,传动效率提高了4.9%。研究结果表明:该模型交互性强,能提高设计效率并节省设计开销,可为实际RV减速器工程优化设计提供参考。

基于改进NSGA-Ⅲ的微电网储能多目标优化配置

为提升微电网中储能配置的可靠性与经济性,提出一种基于改进NSGA-Ⅲ算法的微电网储能系统容量多目标优化配置方法。构建了微电网储能容量配置双层优化模型,外层以储能一次投资成本最小为优化目标,内层以微电网综合运行成本最小、负荷缺电率最小和可再生能源利用率最大为优化目标;在传统NSGA-Ⅲ算法中嵌入Levy理论和一个区域角度量化机制,使其更加适用于所提直流微电网储能容量双层优化配置模型的寻优迭代求解,并结合典型日数据,仿真验证了所提模型及算法的有效性。

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.

Non-dominated sorting based multi-page photo collage

The development of social networking services(SNSs)revealed a surge in image sharing.The sharing mode of multi-page photo collage(MPC),which posts several image collages at a time,can often be observed on many social network platforms,which enables uploading images and arrangement in a logical order.This study focuses on the construction of MPC for an image collection and its formulation as an issue of joint optimization,which involves not only the arrangement in a single collage but also the arrangement among different collages.Novel balance-aware measurements,which merge graphic features and psychological achievements,are introduced.Non-dominated sorting genetic algorithm is adopted to optimize the MPC guided by the measurements.Experiments demonstrate that the proposed method can lead to diverse,visually pleasant,and logically clear MPC results,which are comparable to manually designed MPC results.

Robust Optimization Method of Cylindrical Roller Bearing by Maximizing Dynamic Capacity Using Evolutionary Algorithms

Optimization of cylindrical roller bearings(CRBs)has been performed using a robust design.It ensures that the changes in the objective function,even in the case of variations in design variables during manufacturing,have a minimum possible value and do not exceed the upper limit of a desired range of percentage variation.Also,it checks the feasibility of design outcome in presence of manufacturing tolerances in design variables.For any rolling element bearing,a long life indicates a satisfactory performance.In the present study,the dynamic load carrying capacity C,which relates to fatigue life,has been optimized using the robust design.In roller bearings,boundary dimensions(i.e.,bearing outer diameter,bore diameter and width)are standard.Hence,the performance is mainly affected by the internal dimensions and not the bearing boundary dimensions mentioned formerly.In spite of this,besides internal dimensions and their tolerances,the tolerances in boundary dimensions have also been taken into consideration for the robust optimization.The problem has been solved with the elitist non-dominating sorting genetic algorithm(NSGA-II).Finally,for the visualization and to ensure manufacturability of CRB using obtained values,radial dimensions drawing of one of the optimized CRB has been made.To check the robustness of obtained design after optimization,a sensitivity analysis has also been carried out to find out how much the variation in the objective function will be in case of variation in optimized value of design variables.Optimized bearings have been found to have improved life as compared with standard ones.

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。

基于改进NSGA-II算法的装配式建筑施工调度优化

针对以往装配式建筑调度研究主要基于每项活动只有确定的活动时间和一种执行模式,而实际调度过程中存在不确定的活动时间和多种执行模式,建立多目标多模式资源约束下的模糊工期调度模型,提出一种改进的非支配排序遗传算法(INSGA-II)来求解(时间−成本)双目标优化模型。该算法根据活动的优先级关系进行种群初始化和交叉操作,同时提出新的包含活动列表、模式列表和资源列表的3段编码。最后,通过装配式建筑施工现场实际案例分析和算法性能对比,证明本文构建的调度模型和算法设计能有效地解决多模式资源约束下的模糊工期调度模型,为施工调度计划的设计提供科学的思路和方法。

考虑生物危险源扩散的疫区应急物资调配模型

为解决传染性生物危险源扩散后疫区应急物资选址-分配问题,构建多目标优化应急物资调配模型。该模型以最小化配送时间、最小化疫区物资未满足程度为目标函数,结合考虑潜伏期、重复感染率的易感者-潜伏者-感染者-康复者(SEIRS)传染病动力学模型,建立物资需求方程,预测各疫区实时物资需求;针对应急救援过程中疫情扩散对应急物资调配方案的影响,使用改进非支配排序遗传算法(NSGA)-II求解模型。通过k-means算法预选址,实现适用于现有疫区的配送中心选址方案动态更新,进一步联合决策配送中心各类车辆派遣数、各类物资配送量;并以武汉市2020年疫情数据作为算例对比分析。结果表明:该模型计算效率较高,相较于传统NSGA-Ⅱ算法,在收敛性、多样性和稳定性上具有性能优势,所得应急物资调配方案调配时间更短,疫区未满足程度更小,验证了模型的有效性。

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。

改进NSGA-Ⅱ算法求解考虑运输约束的柔性作业车间节能调度问题

传统柔性作业车间调度通常忽略工件在机器间的运输时间和能耗,针对该问题建立了考虑运输约束与节能的柔性作业车间调度模型,并提出了改进的NSGA-Ⅱ算法求解该模型。首先,在柔性作业车间调度数学模型中设立最大完工时间、总延期、设备总负载、车间总能耗4个目标,并根据运输约束实现了调度模型矩阵编码、解码、交叉与变异,基于子代向最优解学习机制改进NSGA-Ⅱ算法迭代过程中易陷入局部最优解问题。最后,在考虑车间机器之间运输约束的前提下结合Kacem、Brandimarte算例对调度模型进行可行性分析,结果表明该模型与算法求解效率高,能有效解决车间运输约束导致的调度方案与实际加工偏差问题。

基于改进离散蜉蝣算法的双资源柔性车间可持续调度方法

在目前对柔性车间调度问题所进行的研究中,大多忽略了工件运输时间这一因素,并且也很少对可持续发展的经济、环境和社会3个要素进行综合优化。针对这些问题,提出了一种考虑运输时间的双资源柔性车间调度问题(DRCFJSPT)模型。首先,以完工时间、生产成本、能耗和人体工程学风险为优化目标,构建了柔性车间调度数学模型,并结合多目标模型的特点,设计了一种改进离散蜉蝣算法(IDMA),并对模型进行了求解;然后,采用熵值法评价了帕累托解集,基于三层编码并考虑了运输时间的插入式解码方式,设计了混合初始化方法,离散改进了蜉蝣更新方式;最后,为了验证IDMA求解DRCFJSPT的性能,采用MATLAB,对某机床零件加工企业生产数据进行了实验,并将其结果与采用非支配排序遗传算法(NSGA)-Ⅱ得到的结果进行了对比分析。研究结果表明:改进算法的解集质量和收敛性能均显著优于参考算法,通过改进算法求得最优解的最大完工时间为35.94 h,加工成本为6 003.95元,能耗为2 054.54 kW·h,人体工程学风险值为138.16;该结果可为实际复杂的柔性车间调度环境提供清晰准确的调度方案。