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An Evolutional Topology optimization Method Based on Kernel Level Set Function

IEEE ACCESS 2025年 13卷 47181-47200页

作者： Sato, Takahiro Watanabe, Kota Igarashi, Hajime Muroran Inst Technol Grad Sch Engn Muroran Hokkaido 0508585 Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 0600814 Japan

Topology optimizations involving evolutionary algorithms are promising approaches to solve practical engineering design problems, since their use of derivation-free algorithms makes them applicable to any design problem. In some evolutional topology optimization methods, structures are determined through the use of spatially smooth parametric functions. This article, first, presents a new parametric level set function using kernel functions as a general formulation of spatially smooth function-based methods. It is called the kernel level set function, which is used to determine the distributions of materials in the design region. By using the evolutionary algorithm to modify the profile of the kernel level set function, topology optimizations can be realized for various design problems, and the shape expression ability of the kernel level set function can be customized by changing the kernel functions. Second, a multi-objective formulation considering an objective and structural complexity is introduced, and an optimization algorithm specialized for solving it is proposed. The combination of the kernel level set function and the present optimization algorithm makes it possible to solve various topology optimization problems, and Pareto fronts with respect to target performance and structural complexity can be obtained. The present method is applied to three topology optimization problems. The numerical results are used to examine the characteristics of the present method for various kernel functions. Furthermore, the present method is applied to an optimization problem of an interior permanent magnet motor for electric vehicles. It is shown that the present method can solve practical design problems that have various objectives and constraint conditions, thanks to the use of a derivation-free evolutionary algorithm.

关键词： Level set Kernel Topology Evolutionary computation Linear programming Complexity theory Electric machines Finite element analysis Shape Reviews CMA-ES electric machine evolutionary algorithms topology optimization

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A data driven sequential learning framework to accelerate and optimize multi-objective manufacturing decisions

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JOURNAL OF INTELLIGENT MANUFACTURING 2024年第8期35卷 4087-4112页

作者： Khosravi, Hamed Olajire, Taofeeq Raihan, Ahmed Shoyeb Ahmed, Imtiaz West Virginia Univ Dept Ind & Management Syst Engn Morgantown WV 26505 USA

Manufacturing advanced materials and products with a specific property or combination of properties is often warranted. To achieve that it is crucial to find out the optimum recipe or processing conditions that can generate the ideal combination of these properties. Most of the time, a sufficient number of experiments are needed to generate a Pareto front. However, manufacturing experiments are usually costly and even conducting a single experiment can be a time-consuming process. So, it's critical to determine the optimal location for data collection to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This paper presents a novel data-driven Bayesian optimization framework that utilizes sequential learning to efficiently optimize complex systems with multiple conflicting objectives. Additionally, this paper proposes a novel metric for evaluating multi-objective data-driven optimization approaches. This metric considers both the quality of the Pareto front and the amount of data used to generate it. The proposed framework is particularly beneficial in practical applications where acquiring data can be expensive and resource intensive. To demonstrate the effectiveness of the proposed algorithm and metric, the algorithm is evaluated on a manufacturing dataset. The results indicate that the proposed algorithm can achieve the actual Pareto front while processing significantly less data. Our data-driven framework can facilitate more efficient manufacturing choices, which not only minimizes resource usage but also promotes reduced energy consumption and thereby aids in pollution prevention.

关键词： multi-objective bayesian optimization Data-driven decisions Gaussian process Sequential learning Smart manufacturing

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Heterogenous architected materials: enhancing mechanical performance through multi-objective optimization

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ENGINEERING WITH COMPUTERS 2025年第2期41卷 1241-1259页

作者： Arefin, Amit Khatri, Nava Habib, A. K. M. Ahasun Lu, Qiugang Idesman, Alexander Egan, Paul F. Texas Tech Univ Dept Mech Engn Lubbock TX 79409 USA Texas Tech Univ Dept Elect & Comp Engn Lubbock TX 79409 USA Southwestern Oklahoma State Univ Dept Chem & Phys Weatherford OK 73096 USA Texas Tech Univ Dept Chem Engn Lubbock TX 79409 USA

Computational approaches are a growing necessity for designing complex architected structures, particularly for multifunctional systems with numerous trade-offs. Architected lattice structures formed from repeating unit cells often face multiple loading scenarios in practical applications which make it difficult to determine the best configuration, especially considering emergent behaviors when combining different unit cells. Here, a non-dominated sorting genetic algorithm (NSGA II) is adopted for dual-objective optimization of architected materials for elastic and shear moduli with heterogeneous unit cell placement. Design inputs consist of two different cubic unit cell topologies, each with a fixed unit cell length of 3.6 mm and beam diameter of 0.8 mm. Designs during optimization were evaluated with finite element analysis and search results demonstrated a non-dominated front for elastic and shear modulus trade-offs with a diversity of designs. Computationally optimized heterogenous design solutions increased the elastic modulus limit by 11% and shear modulus limit by 39% when compared to the homogenous and randomized heterogenous solutions. optimization findings were empirically validated by fabricating 3D printed lattices with mechanical testing of compression and shear moduli. Overall, results provided new strategies for configuring lattice designs that demonstrated advantageous multifunctional performance by optimized heterogenous unit cell placement.

关键词： multi-objective optimization 3D printing Architected materials Design Mechanics Lattices

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Gradient-Informed Pareto-Based multi-objective Binary Topology optimization

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IEEE Access 2025年 13卷 86576-86587页

作者： Lucchini, Francesco Torchio, Riccardo Alotto, Piergiorgio Università degli Studi di Padova Department of Industrial Engineering Padua35131 Italy Università degli Studi di Padova Department of Information Engineering Padua35131 Italy

multi-objective topology optimization (TO) problems frequently arise in practical engineering applications, necessitating the identification of Pareto-optimal solutions. This article introduces a gradient informed Pareto-based multi-objective TO algorithm with binary decision variables, called GPBTO, tailored to constrained bi-objective optimization problems (CBOPs), a common scenario in engineering design. By leveraging a binary decision space and incorporating a linearization step for objectives and constraints, the method enables the use of efficient integer linear programming (ILP) techniques for evolving the decision vector. Unlike traditional weighted sum (WS) approaches, which are widely used in TO despite their known limitations, GPBTO provides an alternative that integrates gradient-based formulations while facilitating the identification of Pareto-optimal solutions. While WS remains a dominant method in TO, GPBTO represents a promising alternative for cases where Pareto-based solutions are desirable. To the best of the authors’ knowledge, this is the first attempt to integrate Pareto-based TO with binary variables and gradient information, highlighting its potential for further exploration and application. © 2013 IEEE.

关键词： Integer programming

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Metaheuristic multi-objective optimization with artificial neural networks surrogate modeling for optimal energy-economic performance for CSP technology

ENERGY AND AI

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ENERGY AND AI 2025年 20卷

作者： Allouhi, A. Amine, M. Benzakour Aoul, K. A. Tabet USMBA Ecole Super Technol Fes Route ImouzzerBP 2427 Fes Morocco Univ Chouaib Doukkali Fac Sci El Jadida El Jadida 24000 Morocco United Arab Emirates Univ Coll Engn Architectural Engn Dept POB 15551 Al Ain U Arab Emirates

Among CSP technologies, the linear Fresnel reflector (LFR) can provide reliable carbon-neutral electricity for large-scale applications. In this study, the performance of a large solar LFR power plant under varying climatic conditions and the dependency of the performance on major plant design specifications, such as solar multiple and full-load thermal storage hours, were examined. Next, artificial neural network (ANN) surrogate models were introduced to predict the annual capacity factor of 100 MWe power plants operating with LFR technology. Single-hidden-layer ANN models with different numbers of neurons in the hidden layer were used and the Levenberg-Marquardt training algorithm was adopted. To overcome overfitting, validation and Bayesian Regularization approaches were compared. As training and testing data, 36 geographical sites with various combinations of design parameters were used. Through multi-objective optimization techniques, including the multi- objective Particle-Swarm Optimizer and multi-objective Grey Wolf Optimizer coupled with ANN surrogate modeling, this study navigates the trade-offs to identify Pareto-optimal solutions for large-scale LFR-based CSP integration based on the energy and cost criteria. The study also identified Site 4 (S4) as a promising candidate for optimal balance between the capacity factor (51.05%) and specific cost (5246.71$/kW), showcasing the practical implications of the research for sustainable and efficient CSP plant implementation.

关键词： Artificial neural network Capacity factor Linear fresnel reflector multi-objective optimization Training algorithm Metaheuristics

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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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multi-Project Staff Scheduling optimization Considering Employee Welfare in Construction Projects

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BUILDINGS 2025年第10期15卷 1706-1706页

作者： Peng, Junlong Zhou, Fei Changsha Univ Sci & Technol Sch Transportat Changsha 410114 Peoples R China

multi-project staff scheduling optimization is a critical challenge in engineering management. However, traditional methods often overlook employee welfare, resulting in scheduling solutions that lack a people-centered approach. This study develops a multi-objective model that incorporates employee welfare to balance project performance and staff well-being. A mixed-integer programming model is proposed, integrating task allocation, resource scheduling, income growth, and workload balance as core optimization objectives. An improved Spider Wasp optimization Algorithm (SWOIM) is employed to solve this model. The experimental results demonstrate that SWOIM outperforms conventional optimization algorithms in convergence speed, stability, and solution quality. It enhances staff scheduling efficiency while ensuring fair compensation and balanced workload distribution. A case study further validates the practical applicability of the proposed approach, showing that the optimized schedule not only improves task allocation but also enhances employee satisfaction and overall organizational performance. This study presents a people-centered scheduling framework aligned with sustainable workforce management principles and offers practical insights for improving multi-project coordination.

关键词： employee benefits multi-project staff scheduling mixed-integer programming models improved spider wasp optimization algorithm (SWOIM) sustainable construction management

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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 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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