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Pareto frontier for multi-objective wind farm layout optimization balancing power production and turbine fatigue life

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RENEWABLE ENERGY 2025年 252卷

作者： Yang, Kun Zhang, Mingming Yang, Shanghui Song, Yuwei Dong, Xinhui Deng, Yanfei Deng, Xiaowei Univ Hong Kong Dept Civil Engn Hong Kong Peoples R China Harbin Inst Technol Shenzhen Inst Green Low Carbon Energy Innovat Technol Shenzhen Peoples R China

Layout optimization is a promising design process that will enhance the power efficiency of wind farms in the design stage. Single optimization targeting maximizing power production for wind farms has been studied for decades. Recently, multiple-objective layout optimization, considering more practical factors, has become urgently required with the rapid growth of wind energy. In this paper, we established a multiple-objective layout optimization framework that considers power performance and turbine fatigue life. Efficient power and fatigue life assessment approaches are adopted. Different scales of wind farms and typical wind roses are investigated. Pareto frontiers are drawn for each case to show the optimal solutions for multiple-objective optimizations. Notably, the deduction of wind speed and increase of turbulence intensity in the wake region have contrasting influences on the fatigue life. The turbines of optimal layouts are distant for higher overall power output but prefer to be closer for longer fatigue lives. Characteristic spacing describing the turbine density and the utilized area is suggested to be larger than 3.65D for any case and no less than 4.85D if the wind farm cannot afford the 85% power performance of the theoretical upper limit without wake loss.

关键词： Wind farm layout optimization multiple-objective optimization Fatigue life assessment Energy efficiency Pareto frontier

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Reliability-based design optimization of a cradle-type double rotary table for 5-axis machining center

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2025年

作者： Ma, Mingfei Huang, Xianzhen Liu, Hongqiang Wu, Xiaojie Hua, Chunlei Zhang, Yimin Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China Genertec Machine Tool Engn Res Inst CO LTD Shenyang Peoples R China Zhaoqing Univ Coll Mech & Automot Engn Zhaoqing Peoples R China

In practical engineering, the inevitable presence of certain uncertainties in target systems due to differences in material properties, inaccurate characterization of load conditions, and manufacturing errors. Therefore, this paper considers the influence of random factors and defines the ability of the rotary table to resist deformation under load as the reliability of the rotary table system. We propose a reliability-based design optimization (RBDO) model that comprehensively considers mass, mechanical performance, and carbon emissions. First, a mechanical performance model and a carbon emission model for the cradle-type double rotary table have been established. Considering the assembly relationships within the internal structure of the rotary table, the sensitivity indicators for each component's structural parameters are obtained through sensitivity analysis, and these indicators are used to guide the optimization process. During the optimization process, an adaptive Kriging surrogate model is employed to reconstruct the functional relationship between the structural parameters of the rotary table and its performance. Given the high dimensionality and wide range of the design variables, a multi-stage optimization method has been proposed to overcome the impact of training data density on the fitting accuracy of the surrogate model and improve the convergence effect of the optimization. The optimization results indicate that the multi-stage optimization method can achieve better outcomes in a shorter time compared to traditional optimization approaches. Moreover, compared to deterministic design optimization, RBDO significantly enhances the reliability of the system, providing a new solution for the optimized design of the double rotary table and other complex mechanical structures.

关键词： Cradle-type double rotary table low-carbon design multi-objective optimization reliability assessment rigidity

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Link Topology and multi-objective Mission Flow optimization for Remote Sensing Satellites With Inter-Layer Links and Satellite-Ground Links

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第10期73卷 15621-15635页

作者： Zhong, Xiaoqing Guo, Ningxuan Li, Anshou Gong, Yupeng Wang, Yuqi Wang, Ningyuan Liu, Liang Peng Cheng Lab Dept Strateg & Adv Interdisciplinary Res Shenzhen 518055 Guangdong Peoples R China China Acad Space Technol Inst Telecommun & Nav Satellites Beijing 100094 Peoples R China

In the future Space Information Network (SIN), remote sensing satellites (RSSs) can access communication constellations (CCs) and ground stations (GSs) through inter-layer links (ILLs) and satellite-ground links (SGLs) to realize the timely transmission of large amounts of observation data. In this paper, we study the link topology of both ILLs and SGLs with different time slot durations from the perspective of each RSS based on the time-expanded graph. We propose a multi-objective mission flow optimization model to jointly achieve transmission benefits maximization, end-of-period energy maximization, and transmission wait time minimization. This model considers missions' importance differences under limited storage, energy, and link bandwidth resources. To reduce the solving complexity and solution time, we propose a Phased multi-objective (PMO) algorithm to solve this problem. We simulate the proposed methods in different scale SIN with practical parameters. The results show that RSS's entire orbital period can be covered by ILLs and SGLs, except for the ILLs' handover time. When the ILLs' data transmission rate is much less than the RSSs' data acquisition rate, important mission data can be transmitted in priority. When the transmission rate approaches the data acquisition rate, all observation data can be transmitted in near real-time. The proposed PMO algorithm can obtain the optimal solution in 19.1 seconds for a constellation with 500 satellites, which is much less than the 3.8-minute solution time used by other existing methods.

关键词： Satellites Topology Space communications Information systems optimization Planning Network topology Inter-layer and satellite-ground links multi-objective mission flow optimization remote sensing satellites space information network time-expanded graph

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Enhancing the predictive performance of ensemble models through novel multi-objective strategies: evidence from credit risk and business model innovation survey data

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ANNALS OF OPERATIONS RESEARCH 2023年第2期325卷 1029-1047页

作者： Jha, Paritosh Cucculelli, Marco Univ Bergamo Dept Econ & Management Bergamo Italy Marche Polytech Univ Dept Econ & Social Sci Ancona Italy

This paper proposes novel multi-objective optimization strategies to develop a weighted ensemble model. The comparison of the performance of the proposed strategies against simulated data suggests that the multi-objective strategy based on joint entropy is superior to other proposed strategies. For the application, generalization, and practical implications of the proposed approaches, we implemented the model on two real datasets related to the prediction of credit risk default and the adoption of the innovative business model by firms. The scope of this paper can be extended in ordering the solutions of the proposed multi-objective strategies and can be generalized for other similar predictive tasks.

关键词： multi-objective optimization Ensemble model Prediction Business model innovation Credit risk

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Automatic clustering and feature selection using multi-objective crow search algorithm

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APPLIED SOFT COMPUTING 2023年 142卷

作者： Ranjan, Rajesh Chhabra, Jitender Kumar Natl Inst Technol Comp Engn Dept Kurukshetra 136119 India

Today's real-world data is frequently significant in size, with many redundant, missing, and noise -based features and data instances must be addressed before applying various data-mining-based algorithms for further knowledge discovery. Excessive dimensionality may be mitigated by carefully excluding unnecessary characteristics and selecting a reasonable subset of features. When presented as an optimization issue, choosing the best clusters using the most suitable subset of attributes is a challenge that may be handled using practical meta-heuristic approaches. Besides this, the automatic finding of the appropriate cluster number is another challenging task for the real-world dataset in the unsupervised machine-learning study. The present work proposes a multi-objective crow search algorithm for clustering and feature selection (MO-CSACFS) by modifying the crow search algorithm and introducing a levy flight-based two-point cross-over mechanism for a better exploration phase of the crow and further making it suitable for multi-objective optimization problems. MO-CSACFS addresses both issues using the three objective functions to find appropriate cluster numbers and features. MO-CSACFS is implemented over several real-life and synthetic datasets with varying instances, features, and cluster numbers to assess the algorithm's performance;apart from that, the present work is also applied over several gene-expression datasets. MO-CSACFS is compared with two similar recently proposed multi-objective optimization processes used over an automatic, unsupervised machine learning task. The results show that the MO-CSACFS has produced a compact and robust cluster comparable to other similar works from the literature. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： multi -objective optimization Data clustering Feature selection Crow search algorithm

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Integrated self-consistent macro-micro traffic flow modeling and calibration framework based on trajectory data

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 158卷

作者： Wang, Zelin Liu, Zhiyuan Cheng, Qixiu Gu, Ziyuan Southeast Univ Jiangsu Prov Collaborat Innovat Ctr Modern Urban T Sch Transportat Jiangsu Key Lab Urban ITS Nanjing 210096 Peoples R China Univ Bristol Univ Bristol Business Sch Bristol BS8 1PY England

Calibrating microscopic car-following (CF) models is crucial in traffic flow theory as it allows for accurate reproduction and investigation of traffic behavior and phenomena. Typically, the cali-bration procedure is a complicated, non-convex optimization issue. When the traffic state is in equilibrium, the macroscopic flow model can be derived analytically from the corresponding CF model. In contrast to the microscopic CF model, calibrated based on trajectory data, the macroscopic representation of the fundamental diagram (FD) primarily adopts loop detector data for calibration. The different calibration approaches at the macro-and microscopic levels may lead to misaligned parameters with identical practical meanings in both macro-and micro-traffic models. This inconsistency arises from the difference between the parameter calibration processes used in macro-and microscopic traffic flow models. Hence, this study proposes an integrated multiresolution traffic flow modeling framework using the same trajectory data for parameter calibration based on the self-consistency concept. This framework incorporates multiple objective functions in the macro-and micro-dimensions. To expeditiously execute the proposed framework, an improved metaheuristic multi-objective optimization algorithm is presented that employs multiple enhancement strategies. Additionally, a deep learning technique based on attention mechanisms was used to extract stationary-state traffic data for the macroscopic calibration process, instead of directly using the entire aggregated data. We conducted experiments using real-world and synthetic trajectory data to validate our self-consistent calibration framework.

关键词： Self -consistency multi -resolution modeling Car -following Fundamental diagram multi -objective optimization Deep learning

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A differentiable structural analysis framework for high-performance design optimization

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STRUCTURES 2025年 78卷

作者： Lee, Keith J. Huang, Yijiang Mueller, Caitlin T. MIT Cambridge MA 02139 USA Swiss Fed Inst Technol Zurich Switzerland

Fast, gradient-based structural optimization has long been limited to a highly restricted subset of problems- namely, density-based compliance minimization-for which gradients can be analytically derived. For other objective functions, constraints, and design parameterizations, computing gradients has remained inaccessible, requiring the use of derivative-free algorithms that scale poorly with problem size. This has restricted the applicability of optimization to abstract and academic problems, and has limited the adoption of these potentially impactful methods in practice. In this paper, we bridge the gap between computational efficiency and the freedom of problem formulation through a differentiable analysis framework designed for general structural optimization. We achieve this by leveraging Automatic Differentiation (AD) to manage the complex computational graph of structural analysis programs, and implementing specific derivation rules for performance critical functions along this graph. This paper provides a complete overview of gradient computation for arbitrary structural design objectives, identifies the barriers to their practical use, and derives key intermediate derivative operations that resolve these bottlenecks. Our framework is then tested against a series of structural design problems of increasing complexity: two highly constrained minimum volume problems, a multi-stage shape and section design problem, and an embodied carbon minimization problem. We benchmark our framework against other common optimization approaches, and show that our method outperforms others in terms of speed, stability, and solution quality.

关键词： Automatic differentiation Structural analysis Structural optimization Computational design

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A Symmetric and Comparative Study of Decision Making in Intuitionistic multi-objective optimization Environment: Past, Present and Future

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2025年 1-39页

作者： Pinki, Rakesh Kumar, Rakesh Viriyasitavat, Wattana Sapsomboon, Assadaporn Dhiman, Gaurav Alshahrani, Reem Solaiman, Suhare Choudhary, Rashmi Dey, Protyay Sivaranjani, R. Lovely Profess Univ Dept Math Phagwara 144411 Punjab India Chulalongkorn Univ Fac Commerce & Accountancy Dept Stat Business Informat Technol Div Bangkok Thailand Chitkara Univ Ctr Res Impact & Outcome Rajpura Punjab India Chandigarh Univ Dept Comp Sci & Engn Univ Ctr Res & Dev Gharuan Mohali India Yuan Ze Univ Dept Comp Sci & Engn Tao Yuan Taiwan Chandigarh Grp Coll Jhanjeri Chandigarh Engn Coll Dept Comp Sci & Engn Mohali Punjab India NIMS Univ NIMS Sch Comp Sci & Artificial Intelligence Jaipur Rajasthan India Raghu Engn Coll Dept Comp Sci & Engn Visakhapatnam Andhra Pradesh India

In this article, we look at how intuitionistic fuzzy programming (IFP) for MOO works in several real-life situations. Problems in the real world frequently have non-linear properties, in contrast to the majority of MOO research, which has traditionally relied on linear assignment functions in an intuitionistic setting. To tackle this, our research takes into account non-linear functions such as hyperbolic, parabolic, exponential, and s-curved functions. These functions handle the constraints caused by convexity and concavity in certain areas of the domain, as well as the impact of the functions' slopes. We then investigate 25 potential hybrid scenarios involving various membership and non-membership functions in IFP methods. Evaluating how these hybrid scenarios affect IFP's ability to handle the complexity of MOO is our main goal. By evaluating how various scenarios perform, we attempt to determine the best setups and comprehend their advantages and disadvantages. The results of our quantitative evaluations and practical implementations shed light on multi-objective optimization in real-world settings, which is useful for practitioners and decision makers. To further illustrate the real-world consequences of different IFP approaches, we offer an engaging case study in the agricultural sector. This study not only consolidates current knowledge but also provides practical assistance for achieving optimal results in diverse situations, enhancing our grasp of optimization strategies based on IFP.

关键词： Membership functions

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A Distributed multi-objective Detection Method for multi-Sensor Systems With Unknown Local SNR

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 649-663页

作者： Gao, Chang Zhang, Qingfu Varshney, Pramod K. Lin, Xi Liu, Hongwei Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong 999077 Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen 518057 Peoples R China Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

Distributed detection, which fuses the preprocessed observations of the same area from local sensors, can generally improve target detection performance. For scenarios in practical applications where sensors cannot obtain the target signal-to-noise ratio (SNR) parameters in advance, non-coherent integration is mostly used for distributed detection. However, this detector is equivalent to the optimal detector only under the condition that the target SNRs of all the sensors are exactly the same. This condition is quite stringent for the observation of non-cooperative targets. This paper first compares the performance of traditional optimal detectors, the non-coherent integration (NCI) detector, and the single-sensor detector from a unified perspective based on the concept of Pareto optimality. Then, from the perspective of multi-objective optimization, the fusion rule and corresponding parameter learning method are designed. Theoretical analysis shows that the proposed non-identical SNR detection fusion rule possesses weak Pareto optimality. Simulation experiments demonstrate that the proposed method effectively achieves a trade-off between the optimal detection performance across sensors with multiple SNRs. Compared to the optimal detector in the presence of mismatch between the assumed and actual SNR of the target, the proposed method can achieve a significant improvement in detection performance. Additionally, the proposed method outperforms the NCI detector in scenarios where the SNR distributions of target observations across different sensors exhibit greater diversity.

关键词： Detectors Sensors Signal to noise ratio Radar Sensor fusion Radar cross-sections optimization Sensor phenomena and characterization Target tracking Radar signal processing Distributed detection optimal fusion rule multi-objective optimization Pareto optimality

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Conditional probability based multi-objective cooperative task assignment for heterogeneous UAVs

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartC期123卷

作者： Gao, Xiaohua Wang, Lei Yu, Xinyong Su, Xichao Ding, Yu Lu, Chen Peng, Haijun Wang, Xinwei Dalian Univ Technol Sch Math Sci Dalian 116024 Liaoning Peoples R China Naval Aeronaut & Astronaut Univ Dept Airborne Vehicle Engn Yantai 264001 Peoples R China Sci & Technol Reliabil & Environm Engn Lab Beijing 100191 Peoples R China Beihang Univ Inst Reliabil Engn Beijing 100191 Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Optimizat & CAE Software Dalian 116024 Liaoning Peoples R China

In actual air combat, there is an inevitable risk that an unmanned aerial vehicle (UAV) will be destroyed. However, this risk is rarely considered in the mission planning phase. In this paper, we focus on cooperative mission assignment for heterogeneous UAVs. We develop a multi-objective optimization model to find a balance between mission gains and UAV losses. The objective function is expressed using conditional probability theory by introducing the probabilities of mission success and UAV loss. Munitions loading capacity, time constraints, and priority constraints are modeled as constraints. To solve this combinatorial problem, an improved multi -objective genetic algorithm, which incorporates a natural chromosome encoding format and specially designed genetic operators, is developed. An efficient unlocking method is constructed to address the unavoidable dead-lock phenomenon meanwhile maintaining the population randomness. Numerical simulations for different problem sizes and ammunition stocks are performed, and the proposed algorithm is compared with the multi -objective Particle Swarm optimization and the multi-objective Grey Wolf optimization, respectively, using different unlocking approaches. The simulation and comparison results demonstrate the practical value and effectiveness of the developed model and the proposed algorithm.

关键词： Unmanned aerial vehicle Cooperative task assignment multi-objective optimization Genetic algorithm

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