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检索条件"主题词=non-dominated sorting genetic algorithm"

共 239 条记录，以下是151-160 订阅

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Multi-objective Optimization of Peer-to-Peer Transactions in Arizona State University's Microgrid by NSGA II 32

Multi-objective Optimization of Peer-to-Peer Transactions in...

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32nd International Conference on Electrical Engineering (ICEE)

作者： Shirinshahrakfard, Pourya Suratgar, Amir Abolfazl Menhaj, Mohammad Bagher Gharehpetian, Gevork B. Amirkabir Univ Technol Elect Engn Tehran Iran

ISBN: (纸本)9798350376357;9798350376340

This study presents a novel approach to modeling and solving the multi-objective optimization problem of peer-to-peer transactions among agents within microgrids. A key innovation lies in considering each trade, including internal trades, as a decision variable, unlike prior research that focused on optimizing whole internal trades and grid exchanges. Using Arizona State University's microgrid as a case study with varying exchange rates, the study concludes that the proposed non-dominated sorting genetic algorithm II with modified mutation outperforms existing studies by finding superior solutions in select scenarios, showcasing significant advancements in optimizing microgrid transactions.

关键词： Microgrid Multi-objective Optimization non-dominated sorting genetic algorithm

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Development of a Hybrid Simulation Technology to Optimize Parameters of Multiple Power System Stabilizers 5

Development of a Hybrid Simulation Technology to Optimize Pa...

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5th Asia Conference on Power and Electrical Engineering (ACPEE)

作者： Zheng, Xiujie Wu, Ning Yi, Jianbo Univ Elect Sci & Technol China Chengdu Coll Chengdu Peoples R China Lanzhou Inst Technol Coll Comp & Artificial Intelligence Lanzhou Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781728152813

Traditional power system simulation and analysis software cannot easily extend additional application technologies to combine data-intensive and multi-purpose advanced artificial intelligence computing methods. This paper studies the coordinated control of multiple Power System Stabilizers (PSSs) in the stability of complex power systems based on massive Power System Analysis Software Package (PSASP) simulation data. First, this paper proposes a multi-objective comprehensive objective function that considers the robustness and stability of multi-machine PSSs. Second, there employs PSASP transient simulation data to expand the multi-objective optimization algorithm with additional non-dominated sorting genetic algorithm (NSGA-II). Third, this paper develops a PSASP-MATLAB hybrid simulation technology to realize the global parameters optimization of multi-machine PSSs under the system transient simulation data of multi-operating modes.

关键词： hybrid simulation technology parameters optimization power system stabilizer power system analysis software package non-dominated sorting genetic algorithm

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Using Multi-objective Optimization algorithm to Improve the Efficiency and Reliability of Satellite Signal Processing System 7

Using Multi-objective Optimization Algorithm to Improve the ...

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7th International Conference on Automation Electronics and Electrical Engineering

作者： Shao, Yanan Luo, Taojun Dai, Ziyi Xie, Jing Li, Yingjie He, Yifeng China Acad Space Technol Xian Xian Peoples R China

ISBN: (纸本)9798350377040;9798350377033

Traditional satellite signal processing methods sacrifice the performance of other aspects when optimizing one aspect of the satellite signal processing system. This paper constructed a multi-objective optimization model based on the non-dominated sorting genetic algorithm II (NSGA-II) and built a framework based on the improved genetic algorithm. By comparing with the LSM (Least Mean Squares) algorithm, the NSGA-II algorithm has the shortest average processing time of 28.99ms and the minimum error rate of 1.27%, which is better than the LSM algorithm. The research results show that this method can improve the performance of the satellite signal processing system.

关键词： MOO algorithm Satellite Signal Processing non-dominated sorting genetic algorithm Pareto Optimal Solution System Reliability

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A Novel Grid-based Crowding Distance for Multimodal Multi-objective Optimization

A Novel Grid-based Crowding Distance for Multimodal Multi-ob...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Javadi, Mahrokh Ramirez-Atencia, Cristian Mostaghim, Sanaz Otto von Guericke Univ Fac Comp Sci Univ Pl 2 D-39106 Magdeburg Germany

ISBN: (纸本)9781728169293

Preserving diversity in decision space plays an important role in Multimodal Multi-objective Optimization problems (MMOPs). Due to the lack of mechanisms to keep different solutions with the same fitness value, most of the available Multi-objective Evolutionary algorithms (MOEAs) perform poorly when applied to MMOPs. To deal with these problems, this paper proposes a novel method for diversity preserving in the decision space. To this end, the concept of grid-based crowding distance for decision space is introduced. Furthermore, to keep a good diversity of solutions in both decision and objective spaces, we propose different frameworks by combining this method with crowding distance in decision space, crowding distance in objective space, and the weighted sum of both crowding distances. In order to evaluate the performance of these frameworks, we integrate them into the diversity preserving part of the NSGA-II algorithm, and compare them with the NSGA-II (as the baseline algorithm) and the state-of-the-art multimodal multi-objective optimization algorithms on ten different MMOPs with different levels of complexity.

关键词： Grid-based Crowding distance Multi modality Evolutionary algorithms non-dominated sorting genetic algorithm Multi-objective Optimization

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Improved NSGA-II algorithm based on differential evolution mechanism 36

Improved NSGA-II algorithm based on differential evolution m...

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36th Chinese Control Conference (CCC)

作者： Zhang, Wei Zhang, Jiao-long Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454000 Henan Peoples R China

ISBN: (纸本)9789881563934

From simulation experiments of the multi-objective optimization control of wastewater treatment process (WWTP), it can be found that the number of obtained Pareto solutions is less using the normal non-dominated sorting genetic algorithm-II (NSGA-II) sometimes. To achieve a satisfactory optimal performance, an improved NSGA-II algorithm based on differential evolution mechanism is proposed in this paper. The simulation results show that the diversity of the solutions is enhanced and a better homogeneity of non-inferior solutions is kept using the proposed method.

关键词： non-dominated sorting genetic algorithm differential evolution multi-objective optimization uniform wastewater treatment

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Simulation Optimization of Process Parameters in composite drilling process Using Multi-objective Evolutionary algorithm

Simulation Optimization of Process Parameters in composite d...

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International Conference on Advances in Recent Technologies in Communication and Computing

作者： Latha, B. Senthilkumar, V. S. Anna Univ Fac Informat & Commun Engn Madras 600025 Tamil Nadu India Anna Univ Coll Engn Guindy Dept Mech Engn Madras 600025 Tamil Nadu India

ISBN: (纸本)9781424451043

Optimization of process parameters is an important aspect in manufacturing engineering. Multi-objective optimization of process parameters is a complex process. Pareto-optimal solution methodologies are widely used now-a-days. The present investigation focuses on the three objective multiple performance optimization of drilling parameters in drilling of composite drilling process. The objective of this research is to optimize the drilling parameters in drilling such as spindle speed, feed speed and drill diameter on maximizing material removal rate and minimizing thrust force and surface roughness in drilling of composites. The experiments are conducted on computer numeric control (CNC) drilling machine with carbide drill bit. Second order regressions are developed for responses. Based on the equations non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the drilling conditions. A non-dominated solution set has been obtained and presented in this study.

关键词： Multi-objective optimization Pareto-set genetic algorithm modeling non-dominated sorting genetic algorithm

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Modified Crowding Distance and Mutation for Multimodal Multi-Objective Optimization 19

Modified Crowding Distance and Mutation for Multimodal Multi...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Javadi, Mahrokh Zille, Heiner Mostaghim, Sanaz Otto von Guericke Univ Fac Comp Sci Magdeburg Germany

ISBN: (纸本)9781450367486

Many real-world applications contain complex optimization problems with several conflicting objectives. Finding a solution which can satisfy all the objectives is usually a challenging task for optimization algorithms. When dealing with these complex multiobjective problems, decision-makers want to find the best tradeoff between the conflicting objectives. Another challenge occurs in problems where multiple configurations of the input variables might yield the same objective function values. Such problems are called multimodal problems. For a decision maker, it might be of importance to obtain enough information about all the alternative optimal solutions that reach the same objective value. Traditionally, Evolutionary algorithms make use selection processes based only on objective function values, which might be a disadvantage when faced with multimodal problems. In this article, we present two operators to use in multimodal multi-objective algorithms, namely a modified crowding distance operator and a neighbourhood Polynomial mutation, which take into account the distribution of solution in the decision space at run-time. Our experimental results demonstrate that the proposed operators are able to outperform the performance of a state-of-the-art method on six current multimodal benchmark functions.

关键词： Multi-modality Multi-modal problems Multi-objective Optimization Evolutionary algorithms non-dominated sorting genetic algorithm

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Multi-objective Optimization of Geometrical Parameters of Corrugated-Undulated Heat Transfer Surfaces using CFD, Artificial Neural Networks and genetic algorithms

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ASME Power Conference (POWER20160

作者： Wang, Limin Ji, Chenglong Chen, Xun Deng, Lei Chen, Yiping Che, Defu Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Peoples R China Hunan Elect Power Corp Res Inst Changsha 410007 Hunan Peoples R China

ISBN: (纸本)9780791850213

A three dimensional numerical simulation was performed to study the effect of the pitch (P-u) and height (H-u) of undulated plate (U-plate) and the height (He) of the corrugated plate (C-plate) on the heat transfer and fluid flow characteristics in the corrugated-undulated (CU) heat transfer surfaces. The mass, momentum and energy equations were solved using finite volume method by considering the steady state, turbulent and incompressible fluid flow. The numerical results showed a good agreement with the experimental data in reference. The average Nusselt number and friction factor were investigated in CU heat transfer surfaces with different geometrical parameters. To achieve a maximum heat transfer enhancement and a minimum pressure drop, the optimal values of these parameters were calculated using the Pareto optimal strategy. For this purpose, computational fluid dynamics analysis, multi-objective genetic algorithm and artificial neural networks were combined together and used in the optimization process. Finally, the optimal values of these geometrical parameters were presented.

关键词： Heat transfer enhancement Corrugated-undulated surfaces non-dominated sorting genetic algorithm Artificial neural network

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An improved NSGA-II algorithm for multi-satellite observation scheduling 43

An improved NSGA-II algorithm for multi-satellite observatio...

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43rd Chinese Control Conference (CCC)

作者： Huang, Chaozhou Shao, Lizhen Univ Sci & Technol Beijing Sch Automat & Elect Engn Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Innovat Sch Foshan 528399 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

Earth observation satellites (EOS) equipped with high-resolution cameras and infrared sensors are used to observe various types of objects on the Earth's surface. When there are numerous ground targets, the available time windows for multiple EOS to observe the given task should be utilized effectively. This problem is known as the multi-satellite observation scheduling problem (MSOSP). To address the scheduling problem of dense tasks with multiple satellites, this paper establishes a bi-objective mixed integer programming model with minimizing the maximum completion time and the total weighted completion time simultaneously. Then, an improved NSGA-II algorithm (INSGA-II), which incorporates a density-based clustering algorithm called DBSCAN to enhance the diversity of solutions during the population evolution process, is proposed to solve the model. Finally, two observation scenarios are designed, and computational experiments are conducted. The results show that INSGA-II outperforms the NSGA-II algorithm in terms of C-metric and hypervolume metric.

关键词： Earth observation satellites Multi-satellite observation scheduling problem non-dominated sorting genetic algorithm

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A Comprehensive Study of Sequential Simulated Moving Bed: Purification of Xylo-oligosaccharides and fructose-glucose

A Comprehensive Study of Sequential Simulated Moving Bed: Pu...

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作者： Yan Li University of Western Ontario

学位级别：博士

Chromatographicseparation is a promising alternative for separation and purification of sugars in industry. Simulated moving bed (SMB) technique has been proven as an efficient chromatographic separation method due to its enhanced productivity and purity, reduced solvent consumption, convenient operating control, and improved separation performance for some systems with low resolution andselectivity. The sequential simulated moving bed (SSMB) isa modification of the conventional SMB process, which currently has some applications forsugar separation due to its low solvent consumption. This work mainly investigates the design strategy of the innovative SSMB process and explore its advantages and disadvantages over the SMB process based on the xylo-oligosaccharides (XOSs) and fructose-glucose systems. SSMB separation of XOSs, a functionalfood additive in the formof a oligomeric saccharide, was firstly conducted. DOWEX MONOSPHERE TM 99/310 resin ionized with K +, which has better selectivity compared with Ca 2+ and Na + was used as the stationary phase. Breakthrough experiments showed that XOSs and the two majorindustrial impurities, xylose and arabinose, allexhibit linear isotherms. Transport-dispersive (TD) model parameters were determined by pulse experimentscarried out at various flowrates. Finally, both the averaged and individual parameters of XOSs and XOS2-XOS7 were obtained. Lab-scale SSMB experiments and the corresponding simulations were carried out to validate the acquired TD model parameters and adsorption isotherms. After that, in order to investigate the optimal operating conditions of this process, themulti-objective optimizations were carried out for three cases with various objectivesand constraints. It was found that, for a given SSMB unit, there exist a paretocurve for simultaneous maximization of purity and unit throughput. The flowrate ratios (m values), however, exhibit some trends that are different from those of conventional SMB and cannot be exp

关键词： Simulated moving bed sequential simulated moving bed xylo-oligosaccharides transport-dispersive model equilibrium-dispersive model adsorption isotherms multi-objective optimization non-dominated sorting genetic algorithm pareto curve Fortran

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