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Planning of DC Electric Spring with Particle Swarm Optimization and Elitist non-dominated sorting genetic algorithm

CSEE Journal of Power and Energy Systems 2024年第2期10卷 574-583页

作者： Qingsong Wang Siwei Li Hao Ding Ming Cheng Giuseppe Buja School of Electrical Engineering and Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment Southeast UniversityNanjing 210096China School of Software Engineering Southeast universitySuzhou 215123China

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.

关键词： DC distribution network DC electric spring non-dominated sorting genetic algorithm particle swarm optimization renewable energy source

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Multi-objective optimisation method of electric vehicle charging station based on non-dominated sorting genetic algorithm

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2022年第5-6期44卷 413-426页

作者： Liu, Jia Huang, Jin Hu, Jinzhi Taiyuan Inst Technol Dept Mech Engn Taiyuan Shanxi Peoples R China

There are some problems in the existing objective optimisation planning methods of electric vehicle charging station, such as low accuracy and long optimisation time. By calculating the input cost, combined closure flow and minimum node voltage of the charging station through the objective function, the optimisation objective was determined. According to the determined optimisation objective, the multi-objective comprehensive planning model of the electric vehicle charging station is constructed. After the initial solution setting, coding, decoding and other iterative operations, the multi-objective comprehensive planning model of the electric vehicle charging station is solved and the optimisation result is obtained. The multi-objective optimisation of electric vehicle charging station is realised. The results show that the highest accuracy is about 95%.

关键词： non-dominated sorting genetic algorithm electric vehicle charging station multi-objective optimisation decoding

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LEO Satellite Downlink Distributed Jamming Optimization Method Using a non-dominated sorting genetic algorithm

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REMOTE SENSING 2024年第6期16卷 1006页

作者： Tang, Chengkai Ding, Jiawei Zhang, Lingling Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518063 Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

Due to their low orbit, low-Earth-orbit (LEO) satellites possess advantages such as minimal transmission delay, low link loss, flexible deployment, diverse application scenarios, and low manufacturing costs. Moreover, by increasing the number of satellites, the system capacity can be enhanced, making them the core of future communication systems. However, there have been instances where malicious actors used LEO satellite communication equipment to illegally broadcast events in large sports stadiums or engage in unauthorized leakage of military secrets in sensitive military areas. This has become an urgent issue in the field of communication security. To combat and prevent abnormal and illegal communication activities using LEO satellites, this study proposes a LEO satellite downlink distributed jamming optimization method using a non-dominated sorting genetic algorithm. Firstly, a distributed jamming system model for the LEO satellite downlink is established. Then, using a non-dominated sorting genetic algorithm, the jamming parameters are optimized in the power, time, and frequency domains. Field jamming experiments were conducted in the southwest outskirts of Xi'an, China, targeting the LEO constellation of the China Satellite Network. The results indicate that under the condition that the jamming coverage rate is no less than 90%, the proposed method maximizes jamming power, minimizes time delay, and minimizes frequency compensation compared to existing jamming optimization methods, effectively improving the real-time jamming performance and success rate.

关键词： LEO satellite downlink distributed jamming non-dominated sorting genetic algorithm

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ICDNSGA: Identification of Potential circRNA-Disease Associations Based on Improved non-dominated sorting genetic algorithm

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2025年第2期22卷 557-569页

作者： Wang, Yuehao Lu, Pengli Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Gansu Peoples R China

Increasing biological research indicates that the expression levels of circRNAs fluctuate during the onset of various diseases, making them potential biomarkers for multiple conditions. Although numerous artificial intelligence-based computational methods are currently employed for circRNA-disease associations prediction, these methods often rely on a single objective function, which can lead to suboptimal prediction accuracy. To date, no method has designed a set of multi-objective functions specifically for the circRNA-disease prediction problem and optimized it using a non-dominated sorting genetic algorithm. This paper introduces a novel approach by utilizing multi-objective functions and an improved non-dominated sorting genetic algorithm (ICDNSGA) to identify potential associations of circRNA-disease. The method constructs a solution space through matrix factorization and network community characteristics, designing four distinct objective functions optimized via the enhanced multi-objective non-dominated sorting genetic algorithm. ICDNSGA incorporates a population-based adaptive normalization strategy, improving algorithm convergence and solution diversity. Experimental results show that ICDNSGA outperforms pure matrix factorization methods, non-dominated sorting genetic algorithms and other machine learning techniques in predictive performance. Additionally, the prediction results can be validated through existing research and biological analyses, underscoring ICDNSGA's potential as a valuable tool for biomedical experimentation.

关键词： Diseases RNA sorting Linear programming genetic algorithms Biology Semantics Feature extraction Matrix decomposition Proteins CircRNA-disease connections multi-objective functions non-dominated sorting genetic algorithm

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A non-dominated sorting genetic algorithm III using competition crossover and opposition-based learning for the optimal dispatch of the combined cooling, heating, and power system with photovoltaic thermal collector

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 128卷

作者： Zou, Dexuan Li, Mengdi Ouyang, Haibin Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Jiangsu Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Guangdong Peoples R China

A photovoltaic thermal collector (PV/T) is incorporated into the combined cooling, heating, and power (CCHP) system to decrease primary energy consumption, operation cost and carbon dioxide emission. Six CCHP scenarios based on three strategies are investigated for the energy utilization of the CCHP system. Also, a non-dominated sorting genetic algorithm III using competition crossover and opposition-based learning (NSGA-III-CO) is proposed for the six scenarios. The competition crossover determines the starting point of search in the decision space, and more potential regions can be exploited sufficiently. The opposition-based learning (OBL) carries out random searches and facilitates the convergence of the population. In addition, a constraint handling approach (CHA) is presented to decrease the constraint violations of the infeasible individuals and turn them into feasible individuals, and it guarantees the feasibility of the population. Experimental results suggest that each CCHP scenario with PV/T is more efficient than the one with photovoltaic panel (PV) and the one neglecting PV/T and PV, and it achieves the lowest primary energy consumption, operation cost and carbon dioxide emission. Also, NSGA-III-CO and the other three algorithms are applied to the six CCHP scenarios with PV/T, and it obtains the highest hypervolume and coverage rate for each scenario.

关键词： Photovoltaic thermal collector Combined cooling heating and power system Energy utilization non-dominated sorting genetic algorithm Constraint handling approach

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Operative generative design using non-dominated sorting genetic algorithm II (NSGA-II)

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AUTOMATION IN CONSTRUCTION 2023年第1期155卷

作者： Bailey, Elnaz Tafrihi Caldas, Luisa Univ Calif Berkeley Dept Architecture Berkeley CA USA Univ Calif Berkeley XR Lab Berkeley CA USA Lawrence Berkeley Natl Lab Berkeley CA USA Univ Calif Berkeley Coll Environm Design Dept Architecture Berkeley CA 94720 USA

Massing studies during the early stages of architectural design play an essential role in determining the final building's performance across design objectives. This paper aims to answer the question: How can early-stage architectural design workflows be translated into a generative design process to create valuable massing solutions? In response, a new application of the non-dominated sorting genetic algorithm II (NSGA-II) using the Pymoo framework is proposed for the field of Operative Design. Nine experiments are discussed that test the algorithm's geometry optimization capabilities based on objective functions reflecting common architectural design goals, including Floor Area Ratio (FAR), non-Passive Zone (NPZ), Roofs and Best Oriented Surfaces (RBOS), and Usable Open Space (UOS). Selected cases are visualized among non-dominated solutions in each experiment demonstrating the trade-offs between different objectives while programmatically generating successful building designs. In the future, the proposed generative design workflow can be implemented to run optimizations independently from other software within immersive environments.

关键词： Generative design non-dominated sorting genetic algorithm (NSGA-II) Operative design Early-stage architecture design Building massing Building performance rules-of-thumb

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The Multi-Objective Design of Laminated Structure with non-dominated sorting genetic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第10期13卷 901-906页

作者： Zhang, Huiyao Wang, Yuxiao Zeng, Fangmeng Wuxi Taihu Univ Jiangsu Key Construct IoT Applicat Technol Wuxi Peoples R China Donghua Univ Coll Text Shanghai Peoples R China Zhejiang Sci Tech Univ Coll Text Sci & Engn Hangzhou Peoples R China

non-dominated sorting genetic algorithm has shown excellent advantages in solving complicated optimization problems with discrete variables in a variety of domains. In this paper, we implement a multi-objective genetic algorithm to guide the design of the laminated structure with two objectives: minimizing the mass and maximizing the strength of a specified structure simultaneously, classical lamination theory and failure theory are adopted to compute the strength of a laminate. The simulation results have shown that a non-dominated genetic algorithm has great advantages in the design of laminated composite material. Experiment results also suggest that optimal run times are from 16 to 32 for the design of glass-epoxy laminate with non-dominated sorting genetic algorithm. We also observed that two stages involve the optimization process in which the number of individuals in the first frontier first increases, and then decreases. These simulation results are helpful to decide the proper run times of genetic algorithms for glass-epoxy design and reduce computation costs.

关键词： non-dominated sorting genetic algorithm optimization failure theory laminated composite material classical lamination theory

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Multi-objective optimization of fiber laser cutting based on generalized regression neural network and non-dominated sorting genetic algorithm

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INFRARED PHYSICS & TECHNOLOGY 2020年 108卷 103337-103337页

作者： Ding, Hua Wang, Zongcheng Guo, Yicheng Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Jiangsu Peoples R China

An integrated model based on generalized regression neural network (GRNN) and non-dominated sorting genetic algorithm (NSGAII) with elite strategy is proposed to predict and optimize the quality characteristics of fiber laser cutting stainless steel. An orthogonal experiment has been conducted where laser power, cutting speed, gas pressure, defocus are considered as controllable input parameters with kerf width and surface roughness as output to generate the dataset for the model. In GRNN-NSGAII model, the cross-validation method was performed to train the network to obtain the optimal GRNN. Significance of controllable parameters of laser on outputs is also discussed. The GRNN model is determined as the fitness function for prediction and calculation during the NSGAII optimization process. NSGAII generates complete optimal solution set with Pareto optimal front for outputs. The prediction relative error of GRNN model is within +/- 5%. Experimental verification error of optimized output less than 5%. Characterization of the process parameters in Pareto optimal region has been described in detail.

关键词： Fiber laser cutting Generalized regression neural network non-dominated sorting genetic algorithm Multi-objective optimization

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Optimizing thermophysical properties of nanofluids using response surface methodology and particle swarm optimization in a non-dominated sorting genetic algorithm

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2019年 103卷 7-19页

作者： Hemmat Esfe, Mohammad Amiri, Mahmoud Kiannejad Bahiraei, Mehdi Islamic Azad Univ Khomeinishahr Branch Dept Mech Engn Khomeinishahr Iran Univ Sci & Technol Mazandaran Behshahr Iran Razi Univ Fac Engn Kermanshah Iran

The purpose of this study is to optimize the thermal conductivity and viscosity of the Al2O3/water, CuO/water, SiO2/water, and ZnO/water nanofluids. Both thermophysical properties are modeled using the experimental data via Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The thermal conductivities of the Al2O3/water and CuO/water nanofluids demonstrate maximum increment at all the temperatures and volume fractions. However, the viscosity variations of various nanofluids have no noticeable difference. The models of the ZnO/water and CuO/water nanofluids indicate the highest accuracy among the proposed models of relative viscosity and relative thermal conductivity, respectively. The deviation values of the RSM model are greater than those of the ANN model for predicting the relative viscosity, and the minimum error of the ANN for prediction of this output is related to the ZnO/water nanofluid. The results show that the most appropriate models for predicting the relative thermal conductivity and relative viscosity are the RSM model and ANN model, respectively. The multi-objective optimization based on RSM and Multi-Objective Particle Swarm Optimization (MOPSO) is performed by the non-dominated sorting genetic algorithm (NSGA-II), and the optimal points for both thermophysical properties are presented. Based on the results, the highest temperature provides simultaneous optimization of both thermophysical properties. (C) 2019 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Nanofluid Thermophysical properties Simultaneous optimization non-dominated sorting genetic algorithm Artificial neural network Response surface methodology

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MULTI-OBJECTIVE OPTIMIZATION OF OPERATING PARAMETERS OF A PEMFC UNDER FLOODING CONDITIONS USING THE non-dominated sorting genetic algorithm

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THERMAL SCIENCE 2019年第6期23卷 3525-3537页

作者： Abdi, Hamid Ait Messaoudene, Noureddine Kolsi, Lioua Belazzoug, Messaoud Univ Blida 1 Dept Mech Blida Algeria Univ Hail Dept Mech Engn Hail Saudi Arabia Univ Blida 1 Lab Appl Hydrogen Energet Blida Algeria Univ Monastir Natl Sch Engn Monastir Unity Metrol & Energy Syst Monastir Tunisia Univ Blida 1 Lab Elect Syst & Telecommand LABSET Blida Algeria

In the present study, the performance of a proton exchange membrane fuel cells is studied under cathode flooding conditions. A 2-D model of water and heat management based on the laws of conservation and electrochemical equations is used. The performance of the proton exchange membrane cell is evaluated on the basis of the computed average current density and its distribution along the channels. Operating parameters are optimized with the objective of maximizing average current density while minimizing its variations. The problem is formulated into a multi-objective form that is solved by the non-dominated sorting genetic algorithm-II to find the optimal Pareto front. The results of the base case are compared to those of the optimized cell. A 38.94% increase in average current density and a 38.8% decrease in standard deviation are obtained.

关键词： proton exchange membrane fuel cells current-voltage characteristic non-dominated sorting genetic algorithm cathode flooding

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