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Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodiesel engine using non-dominated sorting genetic algorithm-II 被引量:3
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作者 Sunil Dhingra Gian Bhushan Kashyap Kumar Dubey 《Frontiers of Mechanical Engineering》 SCIE CSCD 2014年第1期81-94,共14页
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 su... 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. 展开更多
关键词 jatropha biodiesel fuel properties responsesurface methodology multi-objective optimization non-dominated sorting genetic algorithm-ii
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An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ
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作者 Afia Zafar Muhammad Aamir +6 位作者 Nazri Mohd Nawi Ali Arshad Saman Riaz Abdulrahman Alruban Ashit Kumar Dutta Badr Almutairi Sultan Almotairi 《Computers, Materials & Continua》 SCIE EI 2023年第3期5641-5661,共21页
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 ne... 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. 展开更多
关键词 non-dominated sorted genetic algorithm convolutional neural network hyper-parameter OPTIMIZATION
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Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades 被引量:27
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作者 王珑 王同光 罗源 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2011年第6期739-748,共10页
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 exa... 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. 展开更多
关键词 wind turbine multi-objective optimization Pareto-optimal solution non-dominated sorting genetic algorithm (NSGA)-II
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GREEDY NON-DOMINATED SORTING IN GENETIC ALGORITHM-ⅡFOR VEHICLE ROUTING PROBLEM IN DISTRIBUTION 被引量:4
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作者 WEI Tian FAN Wenhui XU Huayu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2008年第6期18-24,共7页
Vehicle routing problem in distribution (VRPD) is a widely used type of vehicle routing problem (VRP), which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when mode... Vehicle routing problem in distribution (VRPD) is a widely used type of vehicle routing problem (VRP), which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when modeling. For multi-objective optimization model, most researches consider two objectives. A multi-objective mathematical model for VRP is proposed, which considers the number of vehicles used, the length of route and the time arrived at each client. Genetic algorithm is one of the most widely used algorithms to solve VRP. As a type of genetic algorithm (GA), non-dominated sorting in genetic algorithm-Ⅱ (NSGA-Ⅱ) also suffers from premature convergence and enclosure competition. In order to avoid these kinds of shortage, a greedy NSGA-Ⅱ (GNSGA-Ⅱ) is proposed for VRP problem. Greedy algorithm is implemented in generating the initial population, cross-over and mutation. All these procedures ensure that NSGA-Ⅱ is prevented from premature convergence and refine the performance of NSGA-Ⅱ at each step. In the distribution problem of a distribution center in Michigan, US, the GNSGA-Ⅱ is compared with NSGA-Ⅱ. As a result, the GNSGA-Ⅱ is the most efficient one and can get the most optimized solution to VRP problem. Also, in GNSGA-Ⅱ, premature convergence is better avoided and search efficiency has been improved sharply. 展开更多
关键词 Greedy non-dominated sorting in genetic algorithm-Ⅱ (GNSGA-Ⅱ) Vehicle routing problem (VRP) Multi-objective optimization
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Planning of DC Electric Spring with Particle Swarm Optimization and Elitist Non-dominated Sorting Genetic Algorithm
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作者 Qingsong Wang Siwei Li +2 位作者 Hao Ding Ming Cheng Giuseppe Buja 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2024年第2期574-583,共10页
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... 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. 展开更多
关键词 DC distribution network DC electric spring non-dominated sorting genetic algorithm particle swarm optimization renewable energy source
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Optimization of dynamic aperture by using non-dominated sorting genetic algorithm-Ⅱ in a diffraction-limited storage ring with solenoids for generating round beam
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作者 Chongchong Du Sheng Wang +2 位作者 Jiuqing Wang Saike Tian Jinyu Wan 《Radiation Detection Technology and Methods》 CSCD 2023年第2期271-278,共8页
Purpose Round beam,i.e.,with equal horizontal and vertical emittance,is preferable than a horizontally flat one for some beamline applications in Diffraction-limited storage rings(DLSRs),for the purposes of reducing t... Purpose Round beam,i.e.,with equal horizontal and vertical emittance,is preferable than a horizontally flat one for some beamline applications in Diffraction-limited storage rings(DLSRs),for the purposes of reducing the number of photons getting discarded and better phase space match between photon and electron beam.Conventional methods of obtaining round beam inescapably results in a reduction of dynamic aperture(DA).In order to recover the DA as much as possible for improving the injection efficiency,the DA optimization by using Non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)to generate round beam,particularly to one of the designed lattice of the High Energy Photon Source(HEPS)storage ring,are presented.Method According to the general unconstrained model of NSGA-Ⅱ,we modified the standard model by using parallel computing to optimize round beam lattices with errors,especially for a strong coupling,such as solenoid scheme.Results and conclusion The results of numerical tracking verify the correction of the theory framework of solenoids with fringe fields and demonstrates the feasibility on the HEPS storage ring with errors to operate in round beam mode after optimizing DA. 展开更多
关键词 Diffraction-limited storage rings Round beam non-dominated sorting genetic Algorithm-Ⅱ High energy photon source
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Suspended sediment load prediction using non-dominated sorting genetic algorithm Ⅱ 被引量:3
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作者 Mahmoudreza Tabatabaei Amin Salehpour Jam Seyed Ahmad Hosseini 《International Soil and Water Conservation Research》 SCIE CSCD 2019年第2期119-129,共11页
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... 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. 展开更多
关键词 Clustering Neural network non-dominated sorting genetic algorithm (NSGA-Ⅱ) SEDIMENT RATING CURVE SELF-ORGANIZING map
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Satellite constellation design with genetic algorithms based on system performance
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作者 Xueying Wang Jun Li +2 位作者 Tiebing Wang Wei An Weidong Sheng 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2016年第2期379-385,共7页
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 optic... 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. 展开更多
关键词 space optical system non-dominated sorting genetic algorithm(NSGA) Pareto optimal set satellite constellation design surveillance performance
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A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance
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作者 Chao-Lung Yang Melkamu Mengistnew Teshome +1 位作者 Yu-Zhen Yeh Tamrat Yifter Meles 《Computers, Materials & Continua》 SCIE EI 2024年第6期3519-3547,共29页
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 t... 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 Residents 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. 展开更多
关键词 Corrective maintenance multi-objective optimization non-dominated sorting genetic algorithmⅢ operator allocation maintenance station location
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Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem
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作者 Liang Zeng Junyang Shi +2 位作者 Yanyan Li Shanshan Wang Weigang Li 《Computers, Materials & Continua》 SCIE EI 2024年第1期375-392,共18页
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 ... 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. 展开更多
关键词 Multi-objective job shop scheduling non-dominated sorting genetic algorithm differential evolution simulated binary crossover
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Non-dominated sorting based multi-page photo collage
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作者 Yu Song Fan Tang +1 位作者 Weiming Dong Changsheng Xu 《Computational Visual Media》 SCIE EI CSCD 2022年第2期199-212,共14页
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 ... 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. 展开更多
关键词 multi-page photo collage balance-aware measurements non-dominated sorting genetic algorithm
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考虑交货期的双资源柔性作业车间节能调度 被引量:1
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作者 张洪亮 徐静茹 +1 位作者 谈波 徐公杰 《系统仿真学报》 CAS CSCD 北大核心 2023年第4期734-746,共13页
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 sor... 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。 展开更多
关键词 双资源约束 柔性作业车间 提前/拖期惩罚 能耗 INSGA-Ⅱ(improved non-dominated sorting genetic algorithmⅡ)
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基于混合遗传蚁群算法的多目标FJSP问题研究
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作者 赵小惠 卫艳芳 +3 位作者 赵雯 胡胜 王凯峰 倪奕棋 《组合机床与自动化加工技术》 北大核心 2023年第1期188-192,共5页
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... 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。 展开更多
关键词 柔性作业车间调度 多目标优化 NSGA-Ⅱ(non-dominated sorting genetic algorithmⅡ) 蚁群算法
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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
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作者 Neil Vaz Jaeyoo Choi +3 位作者 Yohan Cha Jihoon Kong Yooseong Park Hyunchul Ju 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2023年第6期28-41,I0003,共15页
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 effectivenes... 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. 展开更多
关键词 Polymer electrolyte membrane fuel cell Surrogate modeling Multi-layer perceptron(MLP) Response surface analysis(RSA) non-dominated sorting genetic algorithmⅡ(NSGAⅡ)
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Multi-objective optimization of process parameters for ultra-narrow gap welding based on Universal Kriging and NSGA Ⅱ
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作者 马生明 张爱华 +3 位作者 顾建军 漆宇晟 马晶 王平 《China Welding》 CAS 2023年第3期28-35,共8页
The successful confinement of the arc by the flux band depends on the welding process parameters for achieving single-pass,multi-layer, and ultra-narrow gap welding. The sidewall fusion depth, the width of the heat-af... The successful confinement of the arc by the flux band depends on the welding process parameters for achieving single-pass,multi-layer, and ultra-narrow gap welding. The sidewall fusion depth, the width of the heat-affected zone, and the line energy are utilized as comprehensive indications of the quality of the welded joint. In order to achieve well fusion and reduce the heat input to the base metal.Three welding process characteristics were chosen as the primary determinants, including welding voltage, welding speed, and wire feeding speed. The metamodel of the welding quality index was built by the orthogonal experiments. The metamodel and NSGA-Ⅱ(Non-dominated sorting genetic algorithm Ⅱ) were combined to develop a multi-objective optimization model of ultra-narrow gap welding process parameters. The results showed that the optimized welding process parameters can increase the sidewall fusion depth, reduce the width of the heataffected zone and the line energy, and to some extent improve the overall quality of the ultra-narrow gap welding process. 展开更多
关键词 ultra-narrow gap optimization of process parameters non-dominated sorting genetic algorithm II the sidewall fusion depth
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An Optimization Capacity Design Method of Wind/Photovoltaic/Hydrogen Storage Power System Based on PSO-NSGA-II
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作者 Lei Xing Yakui Liu 《Energy Engineering》 EI 2023年第4期1023-1043,共21页
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,th... 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. 展开更多
关键词 Multi-objective optimization wind/photovoltaic/hydrogen power system particle swarm algorithm non-dominated sorting genetic algorithms-II
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Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing
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作者 Caiyun Liu Peng Liu 《Intelligent Automation & Soft Computing》 SCIE 2023年第6期3221-3242,共22页
Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tas... Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tasks and resources.Compared with the traditional mode,shared manufacturing offers more abundant manufacturing resources and flexible configuration options.This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment,and the characteristics of shared manufacturing resource allocation.The execution of manufacturing tasks,in which candidate manufacturing resources enter or exit at various time nodes,enables the dynamic allocation of manufacturing tasks and resources.Then non-dominated sorting genetic algorithm(NSGA-II)and multi-objective particle swarm optimization(MOPSO)algorithms are designed to solve the model.The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers.In addition,the proposed model’s efficiency,which considers the entries and exits of manufacturing resources in the shared manufacturing environment,is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation. 展开更多
关键词 Shared manufacturing dynamic allocation variation of resources non-dominated sorting genetic algorithm(NSGA-II) multi-objective particle swarm optimization(MOPSO)algorithm
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Multi-Objective Cold Chain Path Optimization Based on Customer Satisfaction
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作者 Jing Zhang Baocheng Ding 《Journal of Applied Mathematics and Physics》 2023年第6期1806-1815,共10页
To improve customer satisfaction of cold chain logistics of fresh agricultural goods enterprises and reduce the comprehensive distribution cost composed of fixed cost, transportation cost, cargo damage cost, refrigera... To improve customer satisfaction of cold chain logistics of fresh agricultural goods enterprises and reduce the comprehensive distribution cost composed of fixed cost, transportation cost, cargo damage cost, refrigeration cost, and time penalty cost, a multi-objective path optimization model of fresh agricultural products distribution considering client satisfaction is constructed. The model is solved using an enhanced Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II), and differential evolution is incorporated to the evolution operator. The algorithm produced by the revised algorithm produces a better Pareto optimum solution set, efficiently balances the relationship between customer pleasure and cost, and serves as a reference for the long-term growth of organizations. . 展开更多
关键词 Cold Chain Logistics Customer Satisfaction Elitist non-dominated sorting genetic Algorithm Multi-Objective Optimization
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Selection of Machining Datum and Allocation of Tolerance through Tolerance Charting Technique 被引量:2
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作者 THILAK Manoharan SIVAKUMAR Karuppan JAYAPRAKASH Govindharajalu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2012年第4期697-705,共9页
Tolerance charting is an effective tool to determine the optimal allocation of working dimensions and working tolerances such that the blueprint dimensions and tolerances can be achieved to accomplish the cost objecti... Tolerance charting is an effective tool to determine the optimal allocation of working dimensions and working tolerances such that the blueprint dimensions and tolerances can be achieved to accomplish the cost objectives.The selection of machining datum and allocation of tolerances are critical in any machining process planning as they directly affect any setup methods/machine tools selection and machining time.This paper mainly focuses on the selection of optimum machining datums and machining tolerances simultaneously in process planning.A dynamic tolerance charting constraint scheme is developed and implemented in the optimization procedure.An optimization model is formulated for selecting machining datum and tolerances and implemented with an algorithm namely Elitist Non-Dominated Sorting Genetic Algorithm(NSGA-II).The computational results indicate that the proposed methodology is capable and robust in finding the optimal machining datum set and tolerances. 展开更多
关键词 tolerance allocation tolerance charting elitist non-dominated sorting genetic algorithm-ii.
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Multiobjective optimization scheme for industrial synthesis gas sweetening plant in GTL process 被引量:4
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作者 Alireza Behroozsarand Akbar Zamaniyan 《Journal of Natural Gas Chemistry》 EI CAS CSCD 2011年第1期99-109,共11页
In industrial amine plants the optimized operating conditions are obtained from the conclusion of occurred events and challenges that are normal in the working units. For the sake of reducing the costs, time consuming... In industrial amine plants the optimized operating conditions are obtained from the conclusion of occurred events and challenges that are normal in the working units. For the sake of reducing the costs, time consuming, and preventing unsuitable accidents, the optimization could be performed by a computer program. In this paper, simulation and parameter analysis of amine plant is performed at first. The optimization of this unit is studied using Non-Dominated Sorting Genetic Algorithm-II in order to produce sweet gas with CO 2 mole percentage less than 2.0% and H 2 S concentration less than 10 ppm for application in Fischer-Tropsch synthesis. The simulation of the plant in HYSYS v.3.1 software has been linked with MATLAB code for real-parameter NSGA-II to simulate and optimize the amine process. Three scenarios are selected to cover the effect of (DEA/MDEA) mass composition percent ratio at amine solution on objective functions. Results show that sour gas temperature and pressure of 33.98 ? C and 14.96 bar, DEA/CO 2 molar flow ratio of 12.58, regeneration gas temperature and pressure of 94.92 ? C and 3.0 bar, regenerator pressure of 1.53 bar, and ratio of DEA/MDEA = 20%/10% are the best values for minimizing plant energy consumption, amine circulation rate, and carbon dioxide recovery. 展开更多
关键词 amine plant multiobjective optimization non-dominated sorting genetic Algorithm amine circulation rate
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