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A Two-Stage Multi-Objective Design Optimization Model for a 6 MW Direct-Drive Permanent Magnet Synchronous Generator

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ENERGIES 2024年第16期17卷 4147页

作者： Tian, De Wu, Xiaoxuan Meng, Huiwen Su, Yi North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China

The design optimization of a direct-drive permanent magnet synchronous generator (DDPMSG) is of great significance for wind turbines because of its unique advantages. This paper proposes a two-stage model to realize multi-objective design optimization for a 6 MW DDPMSG. In the first stage, a surrogate optimized response surface model based on an improved sparrow search algorithm (ISSA) was established for modeling the cogging torque and generator efficiency. In the second-stage model, a multi-objective optimization model is proposed to optimize the cogging torque and generator efficiency of the DDPMSG. Finally, the proposed two-stage model was used for a 6 MW DDPMSG design optimization, and the simulation results demonstrated the superiority and rationality of the proposed model. In the first-stage model, the proposed surrogate model based on the ISSA had a better modeling accuracy and lower errors. Compared with traditional response surface models and correlation analysis models, the proposed optimized surrogate model reduced errors in the cogging torque by 34.63% and 42.97%, respectively, while the errors in the efficiency models were reduced by 12.92% and 60.78%, respectively, which indicates the superiority of the first-stage model. In the second stage, compared with the single-objective optimization model, the multi-objective optimization model achieved a trade-off optimization between the cogging torque and the efficiency. Compared with the cogging torque optimization model, the proposed model optimized the efficiency by 101.41%. Compared with the efficiency optimization model, the proposed model reduced the cogging torque by 16.67%. These results verified the superiority and rationality of the second-stage model.

关键词： permanent magnet synchronous generators multi-objective design optimization improved sparrow search algorithm non-dominated sorting genetic algorithm ii

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Balancing Cost and Economic Efficiency: Consider the multi-purpose optimization of green energy market's function in Green energy interactive infrastructure

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ENERGY 2024年 309卷

作者： Zhang, Ce Dong, Huijie Xu, Yuanlu Nick, Lucky Univ Portsmouth Sch Business Portsmouth PO1 2UP England Baotou Rura Commercia Bank Co Ltd Baotou Peoples R China Guangzhou Energy Storage Grp Co LTD Guangzhou 510062 Guangdong Peoples R China Univ Western Sydney Sch Econ & Finance Penrith Australia

The increasing reliance on electric micro-mobility vehicles (EMVs) in the delivery industry necessitates efficient battery-swapping infrastructure. This study presents a framework that predicts battery-swapping needs for EMVs using a simulation model that considers the entire travel chain of activities. We propose a multi-objective optimization model to strategically determine battery-swapping station locations, balancing construction and transit costs. Utilizing the non-dominated sorting genetic algorithm ii (NSGA-ii), we identified 35 nondominated solutions within the Pareto front. For Nanjing City, our model indicated that the construction of an optimal network could be achieved with costs ranging from 2.85 to 4.94 million Yuan, corresponding to battery-swapping trip costs between 9700 and 197,000 Yuan. The simulation predicted a daily battery-swapping demand of 675 instances, with peak hours at 11:00 a.m. (301 swaps) and 5:00 p.m. (198 swaps). Sensitivity analysis showed that reducing the charging period from 3 h to 1 h could decrease construction costs by 1.55 million Yuan on average, while maintaining a consistent battery-swapping travel cost of around 23,000 Yuan. Additionally, a 5 % increase in battery-swapping penetration rate led to an average increase of 480,000 Yuan in construction costs and 847 Yuan in travel costs. This study integrates demand forecasting and infrastructure optimization, providing actionable insights for planning and managing battery-swapping stations.

关键词： Electric micro-mobility vehicles Battery-swapping infrastructure non-dominated sorting genetic algorithm ii

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A Study of the Multi-Objective Neighboring Only Quadratic Minimum Spanning Tree Problem in the Context of Uncertainty

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APPLIED SCIENCES-BASEL 2024年第19期14卷 8941页

作者： Pal, Debosree Sharma, Haresh Kumar Prentkovskis, Olegas Chakraborty, Falguni Maskeliunaite, Lijana Boinchee Binapani Balika Vidyalaya Pandua 712135 India Indian Inst Technol Madras Dept Math Chennai 600036 India Birla Inst Management Technol Area Operat & Decis Sci Greater Noida 201306 India Vilnius Gediminas Tech Univ Dept Mobile Machinery & Railway Transport LT-10105 Vilnius Lithuania Dr BC Roy Engn Coll Dept Masters Comp Applicat Durgapur 713206 India

The pursuit of studying the quadratic minimum spanning tree (QMST) problem has captivated numerous academics because of its distinctive characteristic of taking into account the cost of interaction between pairs of edges. A QMST refers to the minimum spanning tree, which is a graph that is both acyclic and minimally connected. It also includes the interaction cost between a pair of edges in the minimum spanning tree. These interaction costs can occur between any pair of edges, whether they are adjacent or non-adjacent. In the QMST problem, our objective is to minimize both the cost of the edges and the cost of interactions. This eventually classifies the task as NP-hard. The interaction costs, sometimes referred to as quadratic costs, inherently exhibit a contradictory relationship with linear edge costs when solving a multi-objective problem that aims to minimize both linear and quadratic costs simultaneously. This study addresses the bi-objective adjacent only quadratic minimum spanning tree problem (AQMSTP) by incorporating the uncertain nature of the linear and quadratic costs associated with the problem. The focus is on the interaction costs between adjacent edges. Consequently, we have introduced a multi-objective problem called the uncertain adjacent only quadratic minimum spanning tree problem (mUAQMSTP) and formulated it using the uncertain chance-constrained programming technique. Afterwards, two MOEAs-non-dominated sorting genetic algorithm ii (NSGAii) and duplicate elimination non-dominated sorting evolutionary algorithm (DENSEA)-and the traditional multi-objective solution approach, the global criterion method, are employed to solve the deterministic transformation of the model. Finally, we provide a suitable numerical illustration to substantiate our suggested framework.

关键词： adjacent only quadratic minimum spanning tree fuzzy chance-constrained programming global criterion method non-dominated sorting genetic algorithm ii duplicate elimination non-dominated sorting evolutionary algorithm

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An improved solution methodology for the urban transit routing problem

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COMPUTERS & OPERATIONS RESEARCH 2024年 163卷

作者： Huesselmann, G. van Vuuren, J. H. Andersen, S. J. Stellenbosch Univ Dept Ind Engn Private Bag 11 ZA-7602 Matieland South Africa Stellenbosch Univ Dept Civil Engn Private Bag 11 ZA-7602 Matieland South Africa

An improved solution methodology is proposed in this paper for the urban transit routing problem (UTRP). This methodology includes a procedure for the generation of improved initial solutions as well as improved metaheuristic search approaches, involving the use of hyperheuristics to manage search operators in both trajectory-based and population-based metaheuristics. The UTRP variant considered in this paper is that of deciding upon efficient bus transit routes. The design criteria embedded in our UTRP model are the simultaneous minimisation of the expected average passenger travel time and minimisation of the system operator's cost (measuring the latter as the sum total of all route lengths in the system). The model takes as input an origin-destination demand matrix for a pre-specified set of bus stops, along with an underlying road network structure, and returns as output a set of bus route trade-off solutions. The decision maker can then select one of these route sets subjectively, based on the desired degree of trade-off between the aforementioned transit system design criteria. This bi-objective minimisation problem is solved approximately in three distinct stages - a solution initialisation stage, an intermediate analysis stage, and an iterative metaheuristic search stage during which high-quality trade-off solutions are sought. A novel procedure is introduced for the solution initialisation stage, aimed at effectively generating high-quality initial feasible solutions. Two metaheuristics are implemented to solve instances of the problem, namely a dominance-based multi-objective simulated annealing algorithm and an improved non-dominated sorting genetic algorithm, each equipped with a hyperheuristic capable of managing the perturbation operators employed. Various novel operators are proposed for these metaheuristics, of which the most noteworthy take into account the demand of passengers.

关键词： Transit network design problem Urban transit routing problem Dominance-based multi-objective simulated annealing non-dominated sorting genetic algorithm ii Metaheuristic Hyperheuristic

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Multi-objective optimization-based medical image watermarking scheme for securing patient records

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期118卷

作者： Singh, Roop Pal, Raju Mittal, Himanshu Joshi, Deepak Indian Inst Technol Delhi Ctr Biomed Engn New Delhi 110016 India Gautam Buddha Univ Sch Informat & Commun Technol Dept Comp Sci & Engn Greater Noida 201312 Uttar Pradesh India Indira Gandhi Delhi Tech Univ Women Dept Artificial Intelligence & Data Sci New Delhi 110006 India Noida Inst Engn & Technol Greater Noida 201306 Uttar Pradesh India All India Inst Med Sci AIIMS Dept Biomed Engn New Delhi 110029 India

The integrity and authenticity of electronic patient records (EPRs) are essential concerns in the healthcare industry. To address these concerns, this paper introduces a novel, robust watermarking scheme using integer wavelet transform -singular value decomposition (IWT-SVD). The proposed scheme is divided into logo embedding and EPR text embedding, aiming to mitigate false -positive issues and maintain EPR integrity and authenticity. In the first part, medical image and watermark logo are encrypted using DMA (Diffused Mandelbrot set -Arnold map) to enhance security. Further, the encrypted medical image is segmented into four subbands (CA, CH, CV, CD) by one -level IWT transform. Then SVD is applied on sub -band CD to extract the principal component (PC), where the encrypted logo is embedded due to its higher magnitudes, thereby mitigating false -positive errors. Embedding the encrypted logo into the hybrid IWT-SVD domain enhances security and robustness. Moreover, for embedding, multiple optimal embedding factors (MOEFs) are determined through non -dominated sorting genetic algorithm ii (NSGA-ii) to balance imperceptibility and robustness. In the second part, EPR text is embedded into watermarked logo using SVD to verify authenticity. The performance of the proposed scheme is validated on standard medical image datasets against various attacks in terms of SSIM, PSNR, BER, and NC. The proposed scheme improves PSNR values by 48.44% and 19.66% for grayscale and color images, respectively. Similarly, NC values are also improved by 10%-12% for grayscale and color images. Therefore, the proposed scheme enhances security, imperceptibility, and robustness while reducing complexity compared to the state-of-the-art schemes. Moreover, it is also false -positive -free and maintain integrity simultaneously.

关键词： Watermarking Medical images Electronic patient record Integer wavelet transform Singular value decomposition non-dominated sorting genetic algorithm ii

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DETAILED DESIGN AND ANALYSIS FOR ADDITIVE MANUFACTURING OF TOPOLOGICALLY OPTIMIZED AND GENERATIVELY DESIGNED TI-6AL-4V HIP JOINT IMPLANT

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INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING 2024年第4期22卷 73-90页

作者： Kishor, Abhishek Burela, Ramesh Gupta Gupta, Ankit Shiv Nadar Inst Eminence DTU Dept Mech Engn Greater Noida Uttar Pradesh India

In this paper, a comprehensive investigation of the design and analysis of Ti-6Al-4V hip joint implants using generative design and topology optimization, along with laser powder bed fusion (LPBF), an additive manufacturing technique, has been presented. The study employed the NSGA-ii genetic algorithm for generative design, enabling the generation of diverse optimized designs and topology optimization with the solid isotropic material penalization approach, efficiently reducing implant mass of the design space by up to 75% while maintaining structural integrity. Finite element analysis revealed comparable von Mises stress and deformation levels between geometries obtained with generative design and topology optimization. However, the combined approach exhibited superior performance, namely, topology optimization followed by generative design, with a 40% reduction in deformation and a 15% reduction in von Mises stress compared to conventional models. LPBF simulations demonstrated the superiority of the optimized geometries, with a 30% reduction in thermal stress and a 66% reduction in deformation compared to conventional designs. It is observed that design input for generative design significantly impacts the output design. Also, geometry has a notable impact on the quality of the printed part.

关键词： hip implant additive manufacturing generative design non-dominated sorting genetic algorithm ii laser powder bed fusion biomedical application design topology optimization finite element analysis solid isotropic material penalization

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Voltage regulation methods for active distribution networks considering the reactive power optimization of substations

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APPLIED ENERGY 2021年 284卷 116347-116347页

作者： Ma, Wei Wang, Wei Chen, Zhe Wu, Xuezhi Hu, Ruonan Tang, Fen Zhang, Weige Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr Beijing Peoples R China Aalborg Univ Dept Energy Technol Aalborg Denmark

Due to the increasing penetration of photovoltaic (PV) power systems in active distribution networks (ADNs), PV power fluctuations may result in significant voltage variations of ADNs. Therefore, this paper proposes a voltage regulation method for ADNs to minimize the operational losses while keeping the nodal voltages within the limit with the reduced PV power curtailment and the reduced switching numbers of on-load tap changers (OLTCs) and capacitor banks (CBs). Meanwhile, the proposed voltage regulation method also aims to minimize the reactive power flowing through OLTCs, and to minimize the switching numbers of substation CBs. In this study, the centralized voltage regulation is performed based on the worst voltage variation scenarios of ADNs, where a multi-objective mixed integer nonlinear programming (MINP) model with time-varying decision variables is established. The MINP model is solved using the non-dominated sorting genetic algorithm ii (NSGA-ii), and a practical decision-making algorithm is developed to select the best solution from the Pareto optimal set. Moreover, the decentralized voltage regulation aims at mitigating real-time nodal voltage variations via adjusting the real-time active and reactive power of each PV plant. Several simulations and comparisons are carried out on a modified IEEE 33-node system to verify the effectiveness of the proposed methods, and to compare with some previous voltage regulation methods. Simulation results show that the proposed voltage regulation methods can not only effectively control voltage variations of ADNs but also improve the economics of ADNs, substations, and PV plants.

关键词： Active distribution network Voltage regulation Photovoltaic power fluctuations Voltage variations Multi-objective mixed integer nonlinear programming non-dominated sorting genetic algorithm ii

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Optimal placement of UAVs of an aerial mesh network in an emergency situation

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第1期12卷 343-358页

作者： Gupta, Manali Varma, Shirshu Indian Inst Informat Technol Allahabad Deptartment Informat Technol Prayagraj Uttar Pradesh India

There has been a rapid increase in usage of unmanned aerial vehicles (UAVs) in different application areas that are unfriendly to humans. These UAVs have been used in Aerial Mesh Networks (AMNs) that act as backbone network to support communication in a post-disaster scenario. However, there may be limited available number of UAV nodes that need to be utilized efficiently to improve the performance of such networks. Here, we consider three important objectives of the network i.e. target coverage, Quality of Service and energy consumption by the network that need to be optimized efficiently to improve the performance. Yet, it is a grueling task to optimize all of these conflicting objectives at the same time, which is affected by the height of UAVs. To optimize more than one conflicting objectives, we used metaheuristic based multi-objective optimization algorithms i.e. Multi-objective Particle Swarm Optimization (MOPSO), non-dominated sorting genetic algorithm ii (NSGA-ii), Strength Pareto Evolutionary algorithm 2 (SPEA2) and Pareto Envelope-based Selection algorithm ii (PESA-ii), which suggest the optimal placement of UAVs. These algorithms are compared based on four performance metrics i.e. generational distance, diversification metric, spread of non-dominant solutions and percentage of domination in three different scenarios. The rigorous experiments are performed by each algorithm in small, medium and large-scale scenarios to compare their results. The ANOVA's validation test suggests that SPEA2 performs better than others in small-scale scenarios while NSGA-ii performs better than others in medium and large-scale scenarios. However, MOPSO has lowest average execution time, after that NSGA-ii, then PESA-ii and then SPEA2.

关键词： Metaheuristic Multi-objective optimization Multi-objective Particle Swarm Optimization non-dominated sorting genetic algorithm ii Strength Pareto Evolutionary algorithm 2 Pareto Envelope-based Selection algorithm ii Optimal non-dominant solutions

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APPLICATION OF THE HEADSTOCK OF CNC BORING MACHINE FOR TRACTOR ENGINE CYLINDER BLOCK BASED ON MULTI-OBJECTIVE genetic algorithm

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APPLIED ENGINEERING IN AGRICULTURE 2021年第2期37卷 343-349页

作者： Wang, Yahui Shi, Ling Dang, Yiqi Sun, Shengkai Zhang, Huipeng North China Univ Water Resources & Elect Power Sch Mech Engn Zhengzhou Henan Peoples R China State Grid Corp China Henan Elect Power Res Inst Zhengzhou Henan Peoples R China

The tractor is one of the most frequently used equipment in agricultural production, and its mass production is the general trend. With the continuous advancement of the global industrialization process, the importance of Computer Numerical Control (CNC) machine in the entire industrial production has become more and more prominent, and the application of CNC machine in tractor manufacturing has greatly improved production efficiency. This article takes the headstock of a single-sided horizontal CNC boring machine dedicated to processing tractor 6-cylinder engine cylinders as the research objective, takes the key parameters of the gear train in the headstock as the optimization design variables, constructs constraints, such as modulus, tooth width, etc., establishes a multi-objective optimization mathematical model, uses the non-dominated sorting genetic algorithm ii (NSGA-ii) to process the model and obtains the Pareto solution set through multiple iterations. The optimization results show that the volume, center distance and the reciprocal of coincidence degree of the main shaft 1 transmission group are reduced in varying degrees. Finally, it is compared with the weighted sum method and genetic algorithm (GA) to highlight the superiority of NSGA-ii.

关键词： Headstock Multi-optimization non-dominated sorting genetic algorithm ii Tractor cylinder

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New flight plan optimisation method utilising a set of alternative final point arrival time targets (RTA constraints)

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AERONAUTICAL JOURNAL 2021年第1292期125卷 1710-1745页

作者： Dancila, R., I Botez, R. M. Univ Quebec Ecole Technol Super Lab Res Act Control Av & Aeroservoelastic LARCASE Montreal PQ H3C 1K3 Canada

This study investigates a new aircraft flight trajectory optimisation method, derived from the non-dominated sorting genetic algorithm ii method used for multi-objective optimisations. The new method determines, in parallel, a set of optimal flight plan solutions for a flight. Each solution is optimal (requires minimum fuel) for a Required Time of Arrival constraint from a set of candidate time constraints selected for the final waypoint of the flight section under optimisation. The set of candidate time constraints is chosen so that their bounds are contiguous, i.e. they completely cover a selected time domain. The proposed flight trajectory optimisation method may be applied in future operational paradigms, such as Trajectory-Based Operations/free flight, where aircraft do not need to follow predetermined routes. The intended application of the proposed method is to support Decision Makers in the planning phase when there is a time constraint or a preferred crossing time at the final point of the flight section under optimisation. The Decision Makers can select, from the set of optimal flight plans, the one that best fits their criteria (minimum fuel burn or observes a selected time constraint). If the Air Traffic Management system rejects the flight plan, then they can choose the next best solution from the set without having to perform another optimisation. The method applies for optimisations performed on lateral and/or vertical flight plan components. Seven proposed method variants were evaluated, and ten test runs were performed for each variant. For five variants, the worst results yielded a fuel burn less than 90kg (0.14%) over the 'global' optimum. The worst variant yielded a maximum of 321kg (0.56%) over the 'global' optimum.

关键词： Flight trajectory optimisation genetic algorithm non-dominated sorting genetic algorithm ii NSGA-ii constrained optimisation multi-objective optimisation required time of arrival aircraft performance model BADA flight plan GRIB navigation air traffic management

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