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A multi-objective bayesian optimization approach based on variable-fidelity multi-output metamodeling

STRUCTURAL AND multiDISCIPLINARY optimization 2023年第5期66卷 100-100页

作者： Lin, Quan Zheng, Anran Hu, Jiexiang Shu, Leshi Zhou, Qi Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Peoples R China China Ship Sci Res Ctr Shanghai 200011 Peoples R China Taihu Lab Deepsea Technol Sci Wuxi 214082 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

practical engineering problems are often involved multiple computationally expensive objectives. A promising strategy to alleviate the computational cost is the variable-fidelity metamodel-based multi-objective Bayesian optimization approach. However, the existing approaches are under the assumption of independent correlations across the multiple objectives by constructing a variable-fidelity metamodel for each objective, which may lose some useful information in a way. To facilitate the usage of the variable-fidelity metamodel-based multi-objective Bayesian optimization approach, a multi-objective Bayesian optimization approach based on variable-fidelity multi-output (VFMO) metamodeling is proposed in this paper. A variable-fidelity multi-output metamodeling approach is developed to model the multiple objectives jointly, which can capture the latent correlations across the multiple objectives and further enhance the optimization. Furthermore, a weighted expected hypervolume improvement acquisition function based on the VFMO metamodeling approach (VFMO-WEHVI) is proposed for multi-objective optimization. The weight coefficients are adaptively determined according to the information from the current metamodel, which allows a better tradeoff between global exploration and local exploitation. Moreover, the probability of feasibility is introduced to deal with multi-objective optimization problems with constraints. The effectiveness of the proposed approach is demonstrated using five analytical benchmark examples and the multi-objective optimization of a metamaterial vibration isolator. Results indicate that the proposed VFMO-WEHVI approach has the best overall performance compared with the state-of-the-art approaches.

关键词： multi-objective optimization Bayesian optimization multi-output metamodeling Variable-fidelity

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Large language model-based evolutionary optimizer: Reasoning with elitism

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NEUROCOMPUTING 2025年 622卷

作者： Brahmachary, Shuvayan Joshi, Subodh M. Panda, Aniruddha Koneripalli, Kaushik Sagotra, Arun Kumar Patel, Harshil Sharma, Ankush Jagtap, Ameya D. Kalyanaraman, Kaushic Shell India Markets Pvt Ltd Computat Sci Grp Chennai India Worcester Polytech Inst Aerosp Engn Dept Worcester MA 01609 USA

Large Language Models (LLMs) have demonstrated remarkable reasoning abilities, prompting interest in their application as black-box optimizers. This paper asserts that LLMs possess the capability for zero- shot optimization across diverse scenarios, including multi-objective and high-dimensional problems. We introduce a novel population-based method for numerical optimization using LLMs called Large Language- Model-Based Evolutionary Optimizer (LEO). Our hypothesis is supported through numerical examples, spanning benchmark and industrial engineering problems such as supersonic nozzle shape optimization, heat transfer, and windfarm layout optimization. We compare our method to several gradient-based and gradient-free optimization approaches. While LLMs yield comparable results to state-of-the-art methods, their imaginative nature and propensity to hallucinate demand careful handling. We provide practical guidelines for obtaining reliable answers from LLMs and discuss method limitations and potential research directions.

关键词： Large language models Evolutionary Optimizers multi-objective optimization Aerodynamic Design

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An optimization-based Monte Carlo method for estimating the two-terminal survival signature of networks with two component classes

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NAVAL RESEARCH LOGISTICS 2025年第2期72卷 169-186页

作者： da Silva, Daniel B. Lopes Sullivan, Kelly M. Clemson Univ Dept Ind Engn Clemson SC USA Univ Arkansas Dept Ind Engn Fayetteville AR 72701 USA

Evaluating two-terminal network reliability is a classical problem with numerous applications. Because this problem is #P$$ \#\mathsf{P} $$-Complete, practical studies involving large systems commonly resort to approximating or estimating system reliability rather than evaluating it exactly. Researchers have characterized signatures, such as the destruction spectrum and survival signature, which summarize the system's structure and give rise to procedures for evaluating or approximating network reliability. These procedures are advantageous if the signature can be computed efficiently;however, computing the signature is challenging for complex systems. With this motivation, we consider the use of Monte Carlo (MC) simulation to estimate the survival signature of a two-terminal network in which there are two classes of i.i.d. components. In this setting, we prove that each MC replication to estimate the signature of a multi-class system entails solving a multi-objective maximum capacity path problem. For the case of two classes of components, we adapt a Dijkstra's-like bi-objective shortest path algorithm from the literature for the purpose of solving the resulting bi-objective maximum capacity path problem. We perform computational experiments to compare our method's efficiency against intuitive benchmark approaches. Our computational results demonstrate that the bi-objective optimization approach consistently outperforms the benchmark approaches, thereby enabling a larger number of MC replications and improved accuracy of the reliability estimation. Furthermore, the efficiency gains versus benchmark approaches appear to become more significant as the network increases in size.

关键词： bi-objective optimization Monte Carlo simulation network reliability survival signature two-terminal reliability

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multi-objective reinforcement learning-based approach for pressurized water reactor optimization

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ANNALS OF NUCLEAR ENERGY 2024年 205卷

作者： Seurin, Paul Shirvan, Koroush MIT 77 Massachusetts Ave Cambridge MA 02139 USA

A novel method, the Pareto Envelope Augmented with Reinforcement Learning (PEARL), has been developed to address the challenges posed by multi-objective problems, particularly in the field of engineering where the evaluation of candidate solutions can be time-consuming. PEARL distinguishes itself from traditional policy-based multi-objective Reinforcement Learning methods by learning a single policy, eliminating the need for multiple neural networks to independently solve simpler sub-problems. Several versions inspired from deep learning and evolutionary techniques have been crafted, catering to both unconstrained and constrained problem domains. Curriculum Learning (CL) is harnessed to effectively manage constraints in these versions. PEARL's performance is first evaluated on classical multi-objective benchmarks. Additionally, it is tested on two practical PWR core Loading Pattern (LP) optimization problems to showcase its real-world applicability. The first problem involves optimizing the Cycle length (L-C) and the rod-integrated peaking factor (F-Delta h) as the primary objectives, while the second problem incorporates the mean average enrichment as an additional objective. Furthermore, PEARL addresses three types of constraints related to boron concentration (C-b), peak pin burnup (Bu-max), and peak pin power (F-q). The results are systematically compared against conventional approaches from stochastic optimization. Notably, PEARL, specifically the PEAL-NdS (crowding) variant, efficiently uncovers a Pareto front without necessitating additional efforts from the algorithm designer, as opposed to a single optimization with scaled objectives. It also outperforms the classical approach across multiple performance metrics, including the Hyper-volume. Future works will encompass a sensitivity analysis of hyper-parameters with statistical analysis to optimize the application of PEARL and extend it to more intricate problems.

关键词： PWR loading pattern optimization Reinforcement learning multi-objective optimization PEARL Curriculum learning

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Application of multi-objective optimization in 3D Image Reconstruction

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TRAITEMENT DU SIGNAL 2024年第3期41卷 1419-1427页

作者： Zhang, Fengli Heze Univ Sch Math & Stat Heze 274015 Peoples R China

3D image reconstruction technology holds significant potential for applications in medical imaging, industrial inspection, and virtual reality, offering more intuitive and precise internal structure visualization. However, due to the complexity of human anatomy and the diversity of medical imaging data, traditional 3D reconstruction methods often struggle to achieve optimal results in terms of reconstruction accuracy, computational efficiency, and structural continuity simultaneously. The application of multi -objective optimization in 3D image reconstruction can comprehensively consider multiple objectives, providing more comprehensive and optimized reconstruction results. However, current research methods still have some deficiencies, primarily neglecting the trade-offs between different objectives and experiencing high computational load and low efficiency when handling complex medical imaging data. This study includes the development of image -target 3D reconstruction algorithms in trajectory space and the establishment and solution of a multiobjective optimization -based 3D image reconstruction model. The research content of this paper aims to improve the quality of reconstruction results and provide more reliable technical support for practical applications, in the hopes of enriching the theoretical foundation of 3D image reconstruction as well as offering new technical approaches for practical applications, having significant theoretical and practical value.

关键词： 3D image reconstruction multi-objective optimization medical imaging computational efficiency structural continuity

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Joint Spectral and Power Efficiency optimization in Uplink Radio Stripes

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第8期72卷 5209-5225页

作者： Conceicao, Filipe Gomes, Marco Silva, Vitor Dinis, Rui Antunes, Carlos Henggeler Univ Coimbra Inst Telecomun IT P-3030290 Coimbra Portugal Univ Coimbra Dept Elect & Comp Engn DEEC P-3030290 Coimbra Portugal Univ Nova Lisboa UNL Inst Telecomun IT P-2829516 Caparica Portugal Univ Nova Lisboa UNL Fac Ciencias & Tecnol FCT P-2829516 Caparica Portugal Univ Coimbra Dept Elect & Comp Engn DEEC P-3030290 Coimbra Portugal Inst Syst Engn & Comp Coimbra INESC P-3030290 Coimbra Portugal

The radio stripe (RS) concept has been emerging as a practical implementation of the cell-free (CF) massive multiple-input-multiple-output (MMIMO) network where all access points (APs) that perform the signal processing required to serve the users' equipment (UEs) share a common stripe. In such a system, an uplink (UL) power optimization must be performed to increase the system's spectral and power efficiencies (SE and PE). In this work, we develop a set of multi-objective UL power optimization models and algorithms based on the differential evolution (DE) meta-heuristic (MH) considering the optimal sequential linear processing (OSLP) and maximum ratio combining (MRC) detection schemes. The bi-objective approaches aim at deriving nondominated solutions in models to: 1) maximize the max-min fairness (MMF) and max-sum rate (MSR) metrics;2) maximize the MMF and minimize transmission power (MTP);3) maximize the MSR and minimize the MTP. A tri-objective approach is also developed to maximize the MMF and MSR and minimize the MTP. Solutions displaying interesting trade-offs are obtained with the proposed models and algorithmic approaches, compared with the case where the UEs employ full power in the UL. Results show that improved SE and PE can be attained with the proposed bi-objective and tri-objective optimization approaches.

关键词： optimization Resource management Measurement Precoding Uplink Computer architecture Programming Radio stripe (RS) uplink power optimization max-min fairness (MMF) max-sum rate (MSR) minimum transmission power (MTP) multi-objective differential evolution (DE)

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Beyond Diagonal RIS for multi-Band multi-Cell MIMO Networks: A practical Frequency-Dependent Model and Performance Analysis

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第1期24卷 749-766页

作者： de Sena, Arthur Sousa Rasti, Mehdi Mahmood, Nurul Huda Latva-aho, Matti Univ Oulu Ctr Wireless Commun Oulu 90570 Finland

This paper delves into the unexplored frequency-dependent characteristics of beyond diagonal reconfigurable intelligent surfaces (BD-RISs). A generalized practical frequency-dependent reflection model is proposed as a fundamental framework for configuring fully-connected and group-connected RISs in a multi-band multi-base station (BS) multiple-input multiple-output (MIMO) network. Leveraging this practical model, multi-objective optimization strategies are formulated to maximize the received power at multiple users connected to different BSs, each operating under a distinct carrier frequency. By relying on matrix theory and exploiting the symmetric structure of the reflection matrices inherent to BD-RISs, relaxed tractable versions of the challenging problems are achieved for scenarios with obstructed and unobstructed direct channel links. The relaxed solutions are then combined with codebook-based approaches to configure the practical capacitance values for the BD-RISs. Simulation results reveal the frequency-dependent behaviors of different RIS architectures and demonstrate the effectiveness of the proposed schemes. Notably, BD-RISs exhibit high reflection performance across the intended frequency range, remarkably outperforming conventional single-connected RISs. Moreover, the proposed optimization approaches prove effective in enabling the targeted operation of BD-RISs across one or more carrier frequencies. The results also shed light on the potential for harmful interference in the absence of synchronization between RISs and adjacent BSs.

关键词： Beyond diagonal reconfigurable intelligent surface (BD-RIS) frequency-dependent RIS multi-band MIMO networks Beyond diagonal reconfigurable intelligent surface (BD-RIS) frequency-dependent RIS multi-band MIMO networks

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On optimistic, pessimistic and mixed fuzzy-programming based approaches to solve multi-objective fully intuitionistic fuzzy linear fractional programming problems

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ANNALS OF OPERATIONS RESEARCH 2025年第2期346卷 1399-1443页

作者： Malik, Manisha Gupta, S. K. Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India

A decision-maker often encounters a state of uncertainty as well as hesitancy due to various unpredictable factors while solving real-world optimization problems. Due to ambiguity in the decision-making process, the parameters and decision variables of an optimization model generally fail to have the exact values. To deal with such realistic uncertain conditions, the concept of intuitionistic fuzzy numbers has been employed widely. In this article, a model of the linear fractional programming problem having multiple objectives with all the parameters and decision variables expressed as intuitionistic fuzzy numbers has been presented. To solve the problem, we first linearize the fractional model using appropriate transformations, and thereafter utilizing the accuracy function and intuitionistic fuzzy programming, the equivalent optimization model is obtained. The conflicting nature of the multiple objectives in the linearized model has been handled using the linear and exponential membership/non-membership functions applying normal, optimistic, pessimistic, and mixed approaches. Moreover, to validate the various equivalent constructions of the original problem, the underlying theorems are also proved at the relevant places. In last, a practical application related to production process in the textile industry has been constructed and solved using the proposed algorithm, and further, a comparison is drawn among various approaches to establish the efficacy of the proposed modelling and developed algorithm.

关键词： multi-objective optimization Intuitionistic fuzzy numbers Membership functions Linear fractional optimization Intuitionistic fuzzy programming Optimistic and pessimistic approaches

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multi-objective Patient Appointment Scheduling Framework (MO-PASS): a data-table input simulation-optimization approach

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2023年第4期99卷 363-383页

作者： Dehghanimohammadabadi, Mohammad Rezaeiahari, Mandana Seif, Javad Northeastern Univ Dept Mech & Ind Engn Boston MA USA Univ Arkansas Med Sci Fay W Boozman Coll Publ Hlth Little Rock AR USA Calif State Polytech Univ Pomona Dept Ind & Mfg Engn Pomona CA USA Northeastern Univ Dept Mech & Ind Engn 360 Huntington Ave Boston MA 02115 USA

Appointment scheduling is one of the critical factors for improving patient satisfaction with healthcare services. A practical and robust appointment scheduling solution allows clinics to efficiently utilize medical devices, equipment, and other resources. This study introduces a multi-objective Patient Appointment Scheduling (MO-PASS) framework to enhance clinic operations and quality of care. The proposed framework integrates three modules: (1) optimization (using MATLAB), (2) Data-Exchange (MS Excel), and (3) Simulation (Simio). To implement MO-PASS, the multi-objective Particle Swarm optimization (MOPSO) algorithm is coded in MATLAB, and a Simio API is developed, which exchanges simulated scenarios with MOPSO via Excel. The efficiency of the proposed framework is evaluated in a breast cancer clinic with multiple physicians and patient types. Two objective functions are defined for evaluating the solutions of the AS problem: (1) minimizing the total service time and (2) maximizing the number of (admitted) patients with zero overtime. Finally, the performance of MO-PASS is tested against three heuristic approaches with respect to objective functions. The computational experiment results show that the proposed MO-PASS outperforms the existing heuristic benchmarks. Also, the framework is accompanied by all the necessary details to make it practical and easy to implement.

关键词： multi-appointment scheduling template scheduling multi-objective scheduling simulation-based optimization simheuristics multi-objective Particle Swarm optimization (MOPSO)

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A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning

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APPLIED ENERGY 2024年第PartA期353卷

作者： Abid, Md. Shadman Apon, Hasan Jamil Hossain, Salman Ahmed, Ashik Ahshan, Razzaqul Lipu, M. S. Hossain Islamic Univ Technol Dept Elect & Elect Engn Gazipur 1704 Bangladesh Sultan Qaboos Univ Coll Engn Dept Elect & Comp Engn Muscat 123 Oman Green Univ Bangladesh Dept Elect & Elect Engn Dhaka 1461 Bangladesh

multi-agent deep reinforcement learning (MADRL) approaches are at the forefront of contemporary research in optimum electric vehicle (EV) charging scheduling challenges. These techniques involve multiple agents that respond to a dynamic simulation environment to strategically integrate EV charging stations (EVCSs) on microgrids by incorporating the constraints posed by stochastic trip durations. In addition, recent research works have demonstrated that planning frameworks based on multi-objective optimization (MOO) techniques are suitable for the efficient functioning of microgrids comprising renewable energy sources (RESs) and battery energy storage systems (BESSs). Even though MADRL techniques have been used to solve the optimum EV charging scheduling challenges and MOO frameworks have been developed to determine the optimal RES-BESS allocation, the potential of merging MADRL and MOO is yet to be explored. Therefore, this research provides an opportunity to determine the effectiveness of combined MOO-MADRL dynamics and their computational efficacy. In this context, this work presents a novel multi-objective Artificial Vultures optimization Algorithm based on multi-agent Deep Deterministic Policy Gradient (MOAVOA-MADDPG) planning framework for allocating RESs, BESSs, and EVCSs on microgrids. The objective function is formulated to optimize the network power losses, total installation and operational costs, greenhouse gas emissions, and system voltage stability. Moreover, the proposed framework incorporates the sporadic nature of RES systems and intends to improve the state of charge (SOC) of the EVs present in the network. The presented approach is validated using practical weather data and EV commuting behavior on the modified IEEE 33 bus network, two practical distribution feeders in Bangladesh, and the Turkish 141 bus network. According to the findings, the MOAVOA-MADDPG framework effectively accommodated the financial, technical, and environmental consideration

关键词： Reinforcement learning Microgrid Deep learning optimization Electric vehicle

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