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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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Multiobjective optimization of synthesis gas production using non-dominated sorting genetic algorithm

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COMPUTERS & CHEMICAL ENGINEERING 2006年第6-7期30卷 1019-1025页

作者： Mohanty, Swati Reg Res Lab Bhubaneswar 751013 Orissa India

Synthesis gas finds wide applications in various chemical industries. Various routes for manufacture of synthesis gas have been reported such as steam reforming of methane, carbon dioxide reforming of methane, partial oxidation of methane and combination of both carbon dioxide reforming and partial oxidation of methane. However, very few studies have been reported on optimization of process parameters for synthesis gas production. These processes have multiple objective functions that are conflicting in nature and hence use of single objective optimization technique is not suitable. In this study therefore real parameter non-dominated sorting genetic algorithm has been used to obtain a Pareto optimal set of process parameters for production of synthesis gas from combined carbon dioxide reforming and partial oxidation of natural gas over a Pt/-gamma-Al2O3 catalyst. The objectives are to maximize the conversion of methane, maximize the selectivity of carbon monoxide and maintain the hydrogen to carbon monoxide mole ratio at approximately 1. The variables that have been taken are temperature, gas hourly space velocity and the oxygen to methane mole ratio. The results have been compared with that reported by other authors. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： non-dominated sorting genetic algorithm synthesis gas 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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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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Multi-objective optimization of the aiming strategy for the solar power tower with a cavity receiver by using the non-dominated sorting genetic algorithm

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APPLIED ENERGY 2017年 205卷 399-416页

作者： Wang, Kun He, Ya-Ling Xue, Xiao-Dai Du, Bao-Cun Xi An Jiao Tong Univ Minist Educ Sch Energy & Power Engn Key Lab Thermofluid Sci & Engn Xian 710049 Shaanxi Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100084 Peoples R China Qinghai Univ New Energy Photovolta Ind Res Ctr Xining 810016 Qinghai Peoples R China

The extremely non-uniform solar flux distribution in the solar power tower plant can badly cause some crucial problems for the solar receiver such as the local hot spot, the thermal stress, and the thermal deformation. Homogenization of the solar flux distribution is an effective method to avoid these problems, and has become an important research topic. The objective of the present study is to homogenize the solar flux distribution on the inner surfaces within the cavity receiver while keeping the optics loss as low as possible by replacing the conventional single-point aiming strategy with optimal multi-point aiming strategies. Multi-objective optimizations of the aiming strategy for the solar power tower with cavity receivers are performed by using the non-dominated sorting genetic algorithm. The distribution of the aiming points on the cavity aperture and the allocation of the aiming points for each heliostat are optimized simultaneously. The following conclusions can be made: (1) The uniformity of the solar flux distribution on the aperture does not always signify the uniformity of the solar flux distribution on the inner surfaces, where the later one is what we are truly concerned about. Therefore, the optimization of the aiming strategy should take charge of the solar flux distribution on the inner surfaces rather than on the aperture. (2) The multi-objective optimization can provide the trade-off between the non-uniformity of the solar flux distribution and the optics loss in the form of Pareto optimal fronts. (3) The optimal aiming strategies provided by the multi-objective optimization can significantly homogenize the solar flux distribution on the inner surfaces within the cavity at a minimum cost of optics loss. (4) For the optimal aiming strategies at all time except the noon, there exists a west-east asymmetry of the aiming point distribution on the aperture. Moreover, the asymmetry gets less obvious as the time gets closer to the noon.

关键词： Solar power tower Multi-point aiming strategy Solar flux distribution Uniformity Optics loss non-dominated sorting genetic algorithm

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Parallel non-dominated sorting genetic algorithm-II-based image encryption technique

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IMAGING SCIENCE JOURNAL 2018年第8期66卷 453-462页

作者： Kaur, Manjit Kumar, Vijay Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala Punjab India

Chaotic system requires parameters to generate random sequences. Recent studies show that the improper selection of parameter values make secret keys generated from chaotic system vulnerable. Meta-heuristic techniques have been introduced in the area of image encryption to improve the selection of chaotic system parameters. But, these techniques suffer from poor computational speed. To overcome this issue, in this paper, a parallel non-dominated sorting genetic algorithm (NSGA-II)-based intertwining logistic map is proposed to encrypt the images. To implement NSGA-II in parallel fashion, master-slave environment is designed. Initially, the execution time analysis of NSGA-II is done to determine the computationally expensive operations. Thereafter, NSGA-II operators are divided into master and slave jobs. The Message Passing Interface (MPI) is used for intercommunication between master and slave nodes. The simulation results show that the parallel proposed technique provides a significant improvement in computational speed as compared to the existing techniques.

关键词： Image encryption non-dominated sorting genetic algorithm intertwining logistic map security analysis

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Optimizations of turboprop engines using the non-dominated sorting genetic algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2010年第G12期224卷 1271-1283页

作者： Boulkeraa, T. Ghenaiet, A. EMP Dept Appl Mech Thermal Power Syst Lab Algiers 16111 Algeria

This article presents a Pareto approach to design for the optimal performance of four configurations of turboprop engines matching the power requirements of a class of propeller-driven aircrafts. In these hi-objective optimizations of the thermal cycle parameters, the power-specific fuel consumption is minimized and the specific power is maximized while maintaining the power levels and limiting the temperature of the power turbine blades. For this purpose, a multi-objective evolutionary optimization algorithm called non-dominated sorting genetic algorithm is used. To avoid engine performance deterioration and constraint violation at extreme operating conditions, the objective functions and constraints are evaluated at both design and off-design conditions. The trade-off surfaces representing the sets of alternative solutions are obtained based on the Pareto optimality. By considering additional subjective criteria, three design points are proposed for each engine configuration.

关键词： turboprop propulsion cycle performance bi-objective optimization Pareto front non-dominated sorting genetic algorithm

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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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A non-dominated sorting genetic algorithm for the location and districting planning of earthquake shelters

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2014年第7期28卷 1482-1501页

作者： Hu, Fuyu Yang, Saini Xu, Wei Beijing Normal Univ State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China Beijing Normal Univ Acad Disaster Reduct & Emergency Management Minist Civil Affairs Beijing 100875 Peoples R China Beijing Normal Univ Acad Disaster Reduct & Emergency Management Minist Educ Beijing 100875 Peoples R China

The improvement of emergency coping capacity is one of the most efficient measures for mitigating disaster impact. Shelter planning is an important strategy to reduce the number of casualties and injuries and facilitate disaster recovery. This study aims to address earthquake shelter location selection and the districting planning of service areas jointly. A bi-objective model is proposed to minimise the total evacuation distance and the total cost, subject to capacity and contiguity constraints. A non-dominated sorting genetic algorithm is developed to tackle the bi-objective model, which involves a multitude of decision variables. To fit the model, the chromosome structure, initialisation process and genetic operators in the algorithm are specifically designed to maintain the contiguity of the service area. And a hybrid strategy of bidirectional multi-point crossover and bidirectional single-point crossover helps promote the diversity of the solutions and accelerate the convergence. Moreover, the Pareto-optimal strategy and feasibility-based rule are combined to obtain trade-offs between objectives. The model and algorithm are validated in a case study of the earthquake shelter location and districting planning problem in Chaoyang District of Beijing, and the results confirm the effectiveness and efficiency of the method.

关键词： location and districting model earthquake shelter non-dominated sorting genetic algorithm contiguity

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