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.

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。

An Optimization Capacity Design Method of Wind/Photovoltaic/Hydrogen Storage Power System Based on PSO-NSGA-II

The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters.To solve the above problem,the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms.Firstly,an integrated energy system consisting of the photovoltaic,wind turbine,electrolysis cell,hydrogen storage tank,and energy storage is established.Meanwhile,the minimum economic cost,the maximum wind and PV power consumption rate,and the minimum load shortage rate are considered to be the objective functions.Then,a hybrid method combined the particle swarm combined with non-dominated sorting genetic algorithms-II is proposed to solve the optimal allocation problem.According to the optimal result,the economic cost is 6.3 million RMB,and the load shortage rate is 9.83%.Finally,four comparative experiments are conducted to verify the superiority-seeking ability of the proposed method.The comparative results indicate that the proposed method possesses a strongermerit-seeking ability,resulting in a solution satisfaction rate of 87.37%,which is higher than that of the unimproved non-dominated sorting genetic algorithms-II.

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.

NSGA-II算法的改进及其在风火机组多目标动态组合优化中的应用

为了解决风火机组动态组合优化问题,重点针对时间耦合的动态特性及混合整数变量的求解,提出改进的基于非支配排序的遗传算法NSGA-Ⅱ(non-dominated sorting genetic algorithm-Ⅱ),引入节能减排理念,建立以CO2与SO2排放量及机组燃煤、启停费用最低的多目标函数。采用双层优化策略分别对启停离散量和负荷分配连续量进行寻优求解,引入模糊最大满意度决策法对Pareto解集进行决策,并嵌套在每次动态求解过程中。通过对某含风电场的10机组算例进行仿真,其结果表明了该方法的可行性和有效性。

Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodiesel engine using non-dominated sorting genetic algorithm-II

The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition （CI） engine using jatropha biodiesel. Response surface methodology based on Central composite design （CCD） is used to design the experiments. Mathematical models are developed for combustion parameters （Brake specific fuel consumption （BSFC） and peak cylinder pressure （Pmax））, performance parameter brake thermal efficiency （BTE） and emission parameters （CO, NOx, unburnt HC and smoke） using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion （BSFC, Pmax）, performance （BTE） and emission （CO, NOx, HC, smoke） parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NOx, HC, smoke, a multi- objective optimization problem is formulated. Non- dominated sorting genetic algorithm-II is used in predict- ing the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine outputand emission parameters depending upon their own requirements.

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.

Modified NSGA-II for a Bi-Objective Job Sequencing Problem

This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation algorithm and has been applied on a bi-objective job sequencing problem. The objectives are the minimization of total weighted tardiness and the minimization of the deterioration cost. The results of the proposed algorithm have been compared with those of original NSGA-II. The comparison of the results shows that the modified NSGA-II performs better than the original NSGA-II.

考虑截获交通流量与充电行驶距离的电动汽车充电网络规划

为优化电动汽车充电网络布局,提高充电服务能力与效率,提出了同时考虑截获交通流量与充电行驶距离的充电网络规划模型。电动汽车动力电池初始荷电状态的不确定性导致充电网络截获交通流量具有随机特性,采用蒙特卡洛模拟方法对其概率特性进行了分析。为提升充电网络在任何情况下的充电服务能力,所提模型以充电网络截获交通流量最小值最大为优化目标之一。为提升充电服务效率,模型另一个优化目标为平均充电行驶距离最短。此外,模型考虑了充电行驶距离机会约束及充电站建设数目约束,采用非支配遗传算法对所提模型进行求解,获得Pareto最优解集。最后,以25节点交通网络为例进行了仿真实验,验证了所提方法的有效性。并基于仿真结果,分析了机会约束置信度与充电站数目对规划结果的影响。

基于非支配遗传算法的HLA仿真系统数据采集策略

数据采集是仿真执行过程中的重要环节,数据采集的完整性和效率对整个训练仿真活动的最终效果和效率具有重大影响。然而,在现有基于高层体系结构(high level architecture, HLA)的分布式仿真系统中,集中式数据采集在单个步长内读写海量数据,会影响仿真正常推进,而分布式数据采集会造成大量冗余数据,且采集模块的开发不具备通适性。针对上述问题,基于弱分布式数据采集结构,利用多个采集成员实现并行数据采集,并基于非支配排序遗传算法Ⅱ(non-dominated sorting genetic algorithm Ⅱ, NSGA-Ⅱ)制定采集任务在多个成员间的分配策略,实现数据采集负载的均衡分布。仿真结果和真实系统上的实验结果表明,所提方法能显著提升数据采集效率,同时减少数据采集成员执行过程中的中央处理器(central processing unit, CPU)和内存消耗。

考虑碳排放的危险品运输异构车辆路径问题研究

为满足危险品运输异构车辆路径问题(HVRP)的低碳需求,对易爆危险品运输过程中的总风险、总成本和总碳排放量进行最优化处理。首先,在模型构造阶段,改进总成本与总风险的度量方式,包括建立爆炸事故场景下考虑危险品装载量的风险量化模型,并设计一种用于惩罚成本计算的软时间窗函数,该函数可以优先减少装载量较大的车辆在客户处的等待时间;然后,在算法改良阶段从2方面改进非支配排序遗传算法(NSGA-Ⅱ),设计一种带有改良交叉算子的混合交叉方法来提升全局搜索效率,并通过包含2个阶段的变邻域搜索(VNS)算法来提高局部搜索能力;最后,通过算例验证模型和算法的有效性。研究结果表明:相较于原始NSGA-Ⅱ,改进的算法收敛曲线下降更快,使总成本、总风险和总碳排放量3个优化目标的平均值进一步减少3.36%、12.16%和6.96%;在车辆数目有限的车队中,承运人可以通过选择不同的车辆类型对各目标产生不同程度的影响。

分时电价下泊位岸桥联合调度研究

基于全球各地推行分时电价政策以及大力发展港口岸电的背景下,将分时电价引入集装箱码头作业问题中,考虑岸电和分时电价对集装箱码头作业计划的影响,同时兼顾码头和船方的利益,在保证船舶可以按计划离港的前提下,构建以船舶在港时间、岸桥电力成本和船舶使用岸电的电力成本最小化为目标的多目标优化模型,根据所选取问题和模型目标函数的特点,选择带精英策略的非支配排序遗传算法(NSGA II)进行求解。通过不同规模的实例验证,与不考虑电力成本的传统泊位岸桥分配策略相比,所提出的模型算法均能够有效降低码头的电力成本,缓解高峰用电压力,尤其对集装箱吞吐量较少的码头优化效果更加显著。最后,通过对分时电价峰谷电价差的灵敏度分析,为码头相关部门针对不同的电价政策进行决策提供一定的依据。

考虑岸桥作业的集装箱船配载多目标优化

为进一步提高集装箱码头作业效率,降低船舶在港停留时间,将码头前沿负责装卸作业的岸桥设备纳入集装箱船配载决策考虑范围内,通过降低航线上各港口的岸桥作业不均衡量保证船舶在港作业效率。考虑船舶运输安全性、经济性、适航性等需求,以岸桥作业不均衡量、船舶阻塞箱数量、稳心高度、横倾角和纵倾值为目标,构建集装箱船配载多目标优化模型。为有效求解多目标优化问题,采用灰熵并行分析法改进第三代非支配排序遗传算法(non-dominated sorting genetic algorithmⅢ,NSGA-Ⅲ)。实验结果表明:改进算法在求解性能上优于一般的带精英选择策略的算法,对算例参数设置变化具有较好鲁棒性,可为制订岸桥作业量均衡的集装箱船配载计划提供一定决策支持。

中欧集装箱多式联运服务网络设计

中欧运输通道和运输方式不断发展完善,海运、中欧班列及多种运输方式联运等构成了中欧间集装箱运输服务网络。货主在选择运输服务时,一直关注货物运输的费用与时效性,由于中欧间运输距离长,节点多,节点作业时长还存在很大的不确定性。同时,随着全球对碳排放问题的重视,运输服务产生的碳排放也成为货主考虑的因素。综合考虑运输费用、时间和碳排放的影响,并关注节点作业时间的不确定性,解决中欧集装箱多式联运服务网络设计问题具有非常重要的现实意义。建立最小化运输费用、运输时间和运输碳排放量的多目标多式联运服务网络设计模型,并在模型中引入不确定性时间变量。由于节点作业时间样本数据有限,通过Box-Muller变换生成随机数丰富数据,并减少不可观测的误差,运用蒙特卡洛模拟对运输时间进行不确定性统计,描述总运输时间的统计特征。基于多目标的Pareto最优思想,设计了快速非支配排序遗传算法求解最优运输服务方案。以天津至汉堡的中欧集装箱运输为实例,根据实际调研结果确定各项相关参数设定,进行模型和算法验证,求解得到多式联运运输方案的Pareto最优解集。结果显示不同的运输服务方案其运输费用、运输时间、碳排放量各有差异,并且符合Pareto最优解集定义,证明了研究提出的考虑不确定性的多目标服务网络设计建模及算法的正确性和可行性,研究成果可为货主提供选择符合其需求的不同运输服务优化方案。

基于响应面-遗传算法的印制电路板参数识别

印制电路板装配件(Printed Circuit Board Assembly,PCBA)在航空航天设备中有广泛应用,分析和优化航空设备中电子设备振动可靠性的必要前提是建立准确的PCBA有限元模型。针对PCBA有限元模型物理参数难以通过实验获取的问题,本文以某机载电子设备PCBA为案例,首先通过最小分辨率V(Minimum Run with Resolution V,MRRV)的中心复合设计(Central Composite Design,CCD)建立多因素模型响应面函数样本点;然后根据最小二乘法确定响应面函数系数并完成响应面精度检验,构建响应值与模态试验结果误差的多目标函数;再采用快速分类的非支配排序遗传算法(Non-dominated Sorting Genetic Algorithm-Ⅱ,NSGA-Ⅱ)进行多目标参数识别,将识别后的参数代入ABAQUS有限元模型进行仿真分析,最后与模态试验和随机振动试验结果进行对比。结果表明:模态频率平均误差减少到2.70%,随机振动响应平均误差为5.83%,验证了此方法对机载振动环境下PCBA模型参数识别的有效性。

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。

A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

In this study,we introduce a novel multi-objective optimization model tailored for modern manufacturing,aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance.Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel,addressing a crucial gap in the integration of maintenance personnel dispatching and station selection.Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness.The core of our methodology is the NSGA Ⅲ+Dispatch,an advanced adaptation of the Non-Dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ),meticulously designed for the selection of maintenance stations and effective operator dispatching.This method integrates a comprehensive coding process,crossover operator,and mutation operator to efficiently manage multiple objectives.Rigorous empirical testing,including a detailed analysis from a taiwan region electronic equipment manufacturer,validated the effectiveness of our approach across various scenarios of machine failure frequencies and operator configurations.The findings reveal that the proposed model significantly outperforms current practices by reducing response times by up to 23%in low-frequency and 28.23%in high-frequency machine failure scenarios,leading to notable improvements in efficiency and cost reduction.Additionally,it demonstrates significant improvements in oper-ational efficiency,particularly in selective high-frequency failure contexts,while ensuring substantial manpower cost savings without compromising on operational effectiveness.This research significantly advances maintenance strategies in production environments,providing the manufacturing industry with practical,optimized solutions for diverse machine malfunction situations.Furthermore,the methodologies and principles developed in this study have potential applications in various other sectors,including healthcare,transportation,and energy,where maintenance efficiency and resource optimization are equally critical.