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Improved Adaptive multi-objective particle swarm optimization of Sensor Layout for Shape Sensing with Inverse Finite Element Method

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SENSORS 2022年第14期22卷 5203页

作者： Li, Xiaohan Niu, Shengtao Bao, Hong Hu, Naigang Xidian Univ Key Lab Elect Equipment Struct Design Minist Educ Xian 710071 Peoples R China

The inverse finite element method (iFEM) is one of the most effective deformation reconstruction techniques for shape sensing, which is widely applied in structural health monitoring. The distribution of strain sensors affects the reconstruction accuracy of the structure in iFEM. This paper proposes a method to optimize the layout of sensors rationally. Firstly, this paper constructs a dual-objective model based on the accuracy and robustness indexes. Then, an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm is developed for this model, which introduces initialization strategy, the adaptive inertia weight strategy, the guided particle selection strategy and the external candidate solution (ECS) set maintenance strategy to multi-objective particle swarm optimization (MOPSO). Afterwards, the performance of IAMOPSO is verified by comparing with MOPSO applied on the existing inverse beam model. Finally, the IAMOPSO is employed to the deformation reconstruction of complex plate-beam model. The numerical and experimental results demonstrate that the IAMOPSO is an excellent tool for sensor layout in iFEM.

关键词： multi-objective particle swarm optimization external candidate solution set inverse finite element method deformation reconstruction exploitation exploration

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Coal blending optimization in thermal power plants based on multi-strategy fusion multi-objective particle swarm optimization

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INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION 2024年第10期44卷 1679-1709页

作者： Li, Jun Yi, Fulong Ma, Yuhua Wang, Yongfu Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China HUANENG Power Intl Inc Prod Management Dept Dandong Power Plant Dandong Peoples R China

Rising coal price and increasingly stringent emission policy highlight the importance of balancing economic benefit and sustainable development of coal-fired power plants. In order to make the power plant operate economically and safely, and reduce the emission of pollutants as much as possible, this paper proposed a coal blending optimization framework for coal-fired power plants. This framework constructs a set of mathematical model based on the minimization objective function, including the economy, safety and environmental protection of coal. And a multi-strategy fusion multi-objective particle swarm optimization (MSF-MOPSO) method is proposed to optimize the coal blending model. The feasibility, effectiveness and superiority of the method are theoretically verified by convergence analysis and several sets of simulation experiments. And the practical application of this framework shows that under the coal blending strategy based on different weight combinations, the algorithm proposed in this paper can produce high-quality Pareto spatial distribution. Comparing with other state-of-the-art coal blending algorithms, this method can reduce the coal purchase cost by 4.42% and ${\rm{S}}{{\rm{O}}_{\rmmulti-objective particle swarm optimization}}$SO2 emission by 5.11% at least under the same condition. It has significant environmental protection and economic benefit.

关键词： Coal blending multi-objective particle swarm optimization adaptive strategy convergence pareto optimal

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A multi-energy scheduling strategy for orderly charging and discharging of electric vehicles based on multi-objective particle swarm optimization

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 44卷

作者： Wang, Ning Li, Bo Duan, Yan Jia, Shengling Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China

In this paper, a multi-energy scheduling model based on the ordered charging and discharging of EVs in typical urban residential areas is established, and the photovoltaic system is introduced into the model. To improve models with single objective in previous research, the minimum variance of total power load and the minimum scheduling cost are taken as optimization objectives of the model, and the multi-objective particle swarm optimization algorithm is used to solve the problem. To reduce the gap between theoretical results and practical application, the actual power load data of a distribution area in Shanghai in spring and summer is taken, and in the case of different number of EVs and weather conditions, the multi-energy scheduling system is simulated. The results show that: 1) The total power load of the area adopting the scheduling algorithm is significantly less than that of the area with disordered charging, where the growth rate of peak electricity consumption can be reduced by up to 127.2%;2) With the increase of the number of EVs participating in the scheduling, the profitability of the system increases significantly. When 70 EVs participate in dispatching, the profit is up to 150% higher than that of 10 EVs.

关键词： Electric vehicle Ordered charging multi-energy scheduling Photovoltaic multi-objective particle swarm optimization

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Optimal sizing of hybrid renewable energy systems in presence of electric vehicles using multi-objective particle swarm optimization

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ENERGY 2020年 209卷 118471-118471页

作者： Sadeghi, Delnia Naghshbandy, Ali Hesami Bahramara, Salah Univ Kurdistan Dept Elect Engn Kurdistan Sanandaj Iran Islamic Azad Univ Dept Elect Engn Sanandaj Branch Sanandaj Iran

In this paper, the optimal sizing problem of the micro-grid's resources in two different modes in the presence of the electric vehicle using the multi-objective particle swarm optimization algorithm is investigated. In this regard, the uncertain behavior of the electric vehicle is modeled using the Monte Carlo Simulation. In the first case named as PV/wind/battery, the optimum number of components and the amount of cost at different levels of reliability are determined. Then, the electric vehicle is added to the system regarding which the loss of power supply probability is recalculated in the both deterministic and stochastic states. The results show that the electric vehicle increases the system reliability. In the second system named as PV/wind/battery/EV, the effect of deterministic and stochastic behavior of electric vehicle on the number of components and the loss of power supply probability was investigated for the first time. The results demonstrated that the design of both systems is feasible, but the first system was more efficient than the second, because the latter used more winds in a number of identical LPSPs. Moreover, sensitivity analysis has been performed to show the effect of wind speed and load parameters on decision variables. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Renewable energy sources Micro-grid Electric vehicle Monte Carlo simulation multi-objective particle swarm optimization

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Optimal scheduling of power systems considering carbon markets: Based on blockchain theory and multi-objective particle swarm optimization algorithm

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Wang, Dongfa Lan, Fei Shen, Huaqiang Liu, Minghui Sun, Zhenhua State Grid Zhejiang Elect Power Co Ltd Hangzhou Peoples R China Fuzhou Univ Sch Econ & Management Fuzhou Peoples R China

In the context of double carbon, it is an inevitable requirement for the low-carbon power industry to take economic efficiency and low carbon into consideration. This article introduces the carbon emission constraint into the economic dispatching of the power system. Then, combined with the blockchain theories, the methods of particle swarm optimization and multi-objective particle swarm optimization (MOPSO) are employed to simulate the economic and environmental scheduling of a power generation system based on six thermal power units. Research shows that the constraint processing approach is practical and effective, and it can firmly adhere to equality requirements, which is superior to other algorithms' constraint processing methods;the algorithm is stable, and the global optimal solution can be determined under different initial solutions. In the process of multi-objective optimization, the solutions of POF obtained by using the slope method are evenly distributed.

关键词： optimal scheduling of power systems environmental and economic dispatching simulations analyse multi-objective particle swarm optimization

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Design of miniaturized FSS using equivalent circuit model and multi-objective particle swarm optimization

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JOURNAL OF PHYSICS D-APPLIED PHYSICS 2021年第40期54卷 405001-405001页

作者： Sheng, Xianjun Zhao, Xia Liu, Ning Gao, Xiang Dalian Univ Technol Sch Elect Engn Dalian 116024 Peoples R China Lianyungang JARI Elect Co Ltd Lianyungang 222000 Peoples R China

A method for designing miniaturized frequency selective surface (FSS) using equivalent circuit model (ECM) and multi-objective particle swarm optimization (MOPSO) algorithm is proposed in this paper. In the proposed method, ECM describing the desired frequency response is established, and parameters of the ECM are determined using the MOPSO algorithm. Combining the ECM analysis and the MOPSO algorithm, the requirements of desired frequency response can be converted into the constraints of equivalent circuit parameters, which simplifies the optimization process of FSS design. For verification, a single-layer bandstop-bandpass FSS composed of convoluted elements with the unit cell size of 0.073 lambda (0) x 0.073 lambda (0) is designed. Simulation results show that the -15 dB bandwidth is from 2.62 to 2.83 GHz, and the -0.5 dB bandwidth is from 3.84 to 5 GHz. Meanwhile, the resonant frequency is stable at different incident angles (up to 80 degrees) under TE and TM polarization. Finally, the proposed FSS prototype is fabricated and measured. The measured results agree well with the simulated ones.

关键词： frequency selective surface equivalent circuit model multi-objective particle swarm optimization bandstop-bandpass

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Application of adaptive grid-based multi-objective particle swarm optimization algorithm for directional drilling trajectory design

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 222卷

作者： Chen, Bihai Wen, Guojun He, Xin Liu, Xingyue Liu, Haojie Cheng, Siyi China Univ Geosci Sch Mech & Elect Informat Wuhan Hubei Peoples R China

The parameters optimization is the key issue for directional drilling trajectory design in oil and gas fields development, and there are three main challenges in multi-objective and multi-constraint optimization: (1) how to establish a multi-objective optimization model based on geological constraints;(2) how to design an appropriate optimization algorithm and solve the optimization model effectively;(3) how to select the desired result from the obtained Pareto solution and meet the engineering requirements. To build a safe and cost-efficient directional drilling trajectory, a new multi-objective optimization model is established in this paper. The effective objective functions to evaluate the drilling trajectory are summarized as the minimum trajectory length, torque, and strain energy. Moreover, the new model takes the wellbore stability based on Mohr-Coulomb criterion as constraint to prevent the borehole from collapsing. A novel adaptive grid -based multi-objective particle swarm optimization(AGMOPSO) is presented to achieve a set of Pareto optimal solutions of the established optimization model. In this algorithm, a new particle flight mode based on arcsine function of inertia weight and Gaussian mutation strategy are introduced to further improve the global searching ability and obtain more non-inferior solutions. To ensure the uniformity of non-inferior solutions, the adaptive grid based on density control factor is designed to map the space of objective functions to the grid space and adaptively adjust the density of non-inferior solutions in the external archive. Besides, a linear weighted summation function is developed to realize leader selection and archive maintenance of non -inferior solutions. The optimization results on the Pareto front indicate that AGMOPSO has better convergence and uniformity than the unmodified algorithm and reported results. To be concluded, AGMOPSO achieves a better optimization performance and obtain a better trajectory for dr

关键词： Directional drilling trajectory optimization Geological constraint multi-objective particle swarm optimization Adaptive grid

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Optimizing Broadband Access and Network Design in Wireless Mesh Networks using multi-objective particle swarm optimization

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Procedia Computer Science 2023年 230卷 275-286页

作者： M. Neema E.S. Gopi Pulakunta Sandeep Reddy Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli Department of Electronics and Communication Engineering Adi Shankara Institute of Engineering and Technology Kalady

Wireless Mesh Networks (WMNs) are becoming increasingly recognized as a promising solution for delivering broadband internet access, owing to their versatility, rapid deployment capabilities, and self-configuring characteristics. This work focuses on optimizing the gateway placement problem in WMNs taking into account objectives like minimizing the number of gateways, reducing hop count, and minimizing the path delay. We employ multi-objective particle swarm optimization (MOPSO) to simultaneously optimize these objectives and find an optimal gateway placement. The results demonstrate its superiority over traditional particle swarm optimization (PSO) in finding better solutions with conflicting objectives. The work provides a practical and effective approach to optimizing broadband access, network design, and gateway placement in WMNs, contributing to the advancement of wireless communication technology.

关键词： Wireless Broadband Wireless Mesh Network particle swarm optimization multi-objective particle swarm optimization

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multiple swarms multi-objective particle swarm optimization Based on Decomposition

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Procedia Engineering 2011年 15卷 3371-3375页

作者： Peng Hu Li Rong Cao Liang-lin Li Li-xian School of Information Science and Technology Jiujiang University Jiujiang 332005China

particle swarm optimization is a very competitive swarm intelligence algorithm for multi-objective optimization problems, but because of it is easy to fall into local optimum solution, and the convergence and accuracy of Pareto solution set is not satisfactory. So we proposed a multi-swarm multi-objective particle swarm optimization based on decomposition (MOPSO_MS), in the algorithm each sub-swarm corresponding to a sub-problem which decomposed by multi-objective decomposition method, and we constructed a new updates strategy for the velocity. Finally, through simulation experiments and compare with the state-of-the-art multi-objective particle swarm algorithm on ZDT test function proved the convergence and the accuracy of the algorithm.

关键词： multi-objective particle swarm optimization multi-swarm particle swarm optimization multi-objective decomposition Performance indicator

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Integrating Energy Smart Grid's ontologies through multi-objective particle swarm optimization algorithm with competitive mechanism

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartA期53卷

作者： Xue, Xingsi Tsai, Pei-Wei Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou 350118 Fujian Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn John St Hawthorn Vic 3122 Australia

Smart city seeks safer, resource-oriented, environment friendly, and cost-effective energy solutions, and due to the distributed and independent nature of energy management systems, Energy Smart Grid (ESG) quickly evolves from a widely accepted concept to an industrial reality. To monitor the energy consumption across various domains and assist efficient energy distribution and storage, it is necessary to integrate various ESG agents' ontologies. To this end, this work models ESG's Ontology Matching Problem (ESG-OMP) as a discrete multi-objective optimization Problem (MOOP), which simultaneously optimizes the alignment's completeness and correctness through determining an optimal entity corresponding set. After that, a multi-objective particle swarm optimization Algorithm With Competitive Mechanism (MOPSO-CM) is proposed to address this issue by trading off the alignment's completeness and correctness, which introduces the CM to update the particles with the pairwise competition that performed in the swarm of current generation. Moreover, an instance-based hybrid similarity measure is used to distinguish the heterogeneous ontology entities. The experiment uses OAEI' s testing datasets, 13 smart grid's ontology matching tasks and 5 energy smart grid's ontology matching tasks to test MOPSO-CM's performance. The experimental results show that MOPSO-CM can effectively address various heterogeneous ontology matching problems and determine high-quality ESG's ontology alignments.

关键词： Energy smart grid Ontology matching multi-objective particle swarm optimization algorithm Competitive mechanism

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