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practical approaches to group-level multi-objective Bayesian optimization in interaction technique design

Collective Intelligence 2024年第1期3卷

作者： Yi-Chi Liao George B Mo John J Dudley Chun-Lien Cheng Liwei Chan Per Ola Kristensson Antti Oulasvirta Department of Information and Communications Engineering Aalto University Aalto Finland Department of Engineering University of Cambridge Cambridge UK Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Designing interaction techniques for end-users often involves exploring vast design spaces while balancing many objectives. Bayesian optimization offers a principled human-in-the-loop method for selecting designs for evaluation to efficiently explore such design spaces. To date, the application of Bayesian optimization in a human-in-the-loop setting has largely been restricted to optimization, or customization, of interaction techniques for individual user needs. In practice, interaction techniques are typically designed for a target population or group of users, with the goal is to produce a design that works well for most users. To accommodate this common use case in interaction technique design, we introduce two practical approaches that facilitate multi-objective Bayesian optimization at the group level. Specifically, our approaches streamline the process of (1) deriving designs suitable for a group of users from data collected in individual user evaluations; and (2) deriving an initialization from group data to improve the efficiency of design optimization for new users. We demonstrate the advantages of these practical approaches in two multi-phase user studies involving the design of non-trivial interaction techniques.

关键词： Human-in-the-loop optimization interaction technique interface design optimization design optimization Bayesian optimization pointing haptics input touch

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BIM-based multi-objective optimization of clash resolution: A NSGA-II approach

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JOURNAL OF BUILDING ENGINEERING 2024年 89卷

作者： Liu, Xinnan Zhao, Junxiang Yu, Yi Ji, Yingbo North China Univ Technol Sch Civil Engn Beijing 100144 Peoples R China China Commun Informat Technol Grp Co Ltd Beijing 100088 Peoples R China

With the wide application of building information modelling (BIM) in the Architectural, Engineering, and Construction (AEC) industry, holistic approaches based on the network theory to improve clash detection and resolution processes have received increasing attention. The previous studies using holistic approaches in the clash resolution mainly focus on single objective optimization. However, in engineering practice of BIM-based clash resolution, finding optimal solutions for a single-objective that meet the project's requirements is challenging. Moreover, optimization objectives need to be appropriately balanced because these objectives tend to conflict with each other, which leads to a typical multi-objective optimization problem. In this paper, by taking minimum number and minimum moving distance of components to be changed as optimization objectives, a second-generation non-dominated sorting genetic algorithm (NSGAII) approach to solve the multi-objective optimization problem is proposed based on component spatial relation network. Clash solutions are automatically generated under the spatial movement constraints and system priority constraints for clash components. The proposed method is validated by clash resolution of an underground parking lot in a residential project, and the results show that clash solutions generated by the proposed method is practical and acceptable. The proposed method is promising in facilitating the practical application of BIM-based automatic clash resolution.

关键词： BIM Component spatial relation network Clash resolution multi-objective optimization NSGA-II

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From Single-objective to Bi-objective Maximum Satisfiability Solving

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JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH 2024年 80卷 1223-1269页

作者： Jabs, Christoph Berg, Jeremias Niskanen, Andreas Jarvisalo, Matti Univ Helsinki Dept Comp Sci Helsinki Finland

The declarative approach is key to efficiently finding optimal solutions to various types of NP-hard real-world combinatorial optimization problems. Most work on practical declarative solvers-ranging from classical integer programming to finite-domain constraint optimization and maximum satisfiability (MaxSAT)-has focused on optimization under a single objective;fewer advances have been made towards efficient declarative techniques for multi-objective optimization problems. Motivated by significant recent advances in practical solvers for MaxSAT, in this work we develop BIOPTSAT, an exact declarative approach for finding Pareto-optimal solutions to bi-objective optimization problems, with propositional logic as the underlying constraint language. BIOPTSAT can be viewed as an instantiation of the lexicographic method. The approach makes use of a single Boolean satisfiability solver that is incrementally employed throughout the entire search procedure, allowing for finding a single Pareto-optimal solution, finding one representative solution for each non-dominated point, and enumerating all Pareto-optimal solutions. We detail several algorithmic instantiations of BIOPTSAT, each building on recent algorithms proposed for single-objective MaxSAT. We empirically evaluate the instantiations compared to recently-proposed alternative approaches to multi-objective MaxSAT solving on several real-world domains from the literature, showing the practical benefits of our approach.

关键词： Combinatorial optimization

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Adaptive surrogate assisted multi-objective optimization approach for highly nonlinear and complex engineering design problems

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APPLIED SOFT COMPUTING 2024年 150卷

作者： Younis, Adel Dong, Zuomin Australian Univ Sch Engn Mech Engn Dept POB 1411 Kuwait 13015 Kuwait Univ Victoria Dept Mech Engn Victoria BC V8P 5C2 Canada

Despite enormous advances in computer power, computationally costly models impede the use of traditional optimization approaches that must be invoked repeatedly during the optimization process in practical engi-neering applications. Surrogate models have been found to be a promising endeavor in multi-objective optimi-zation problems involving expensive analysis and simulation processes such as multi-physics modeling and simulation, finite element analysis (FEA), and computational fluid dynamics (CFD. Developing an optimization algorithm that can easily identify the Pareto frontier of highly nonlinear multi-objective optimization problems with less computation cost is the aim of this work. In this paper, an Adaptive multi-objective optimization approach based Surrogate models (AMOS) is developed to reduce computation cost of fitness evaluations and discover the Pareto optima for multi-objective optimization problems with comparable high accuracy. AMOS explores the design space by sampling using LHD to identify promising regions. Then, AMOS exploits the identified promising region by adaptively constructing the most suitable surrogate model, which could be response surface, radial basis, or Kriging surrogates, in the feasible design space based on root mean square error values (RMSE). AMOS stops iterating when a termination criterion is met, and a Pareto frontier is identified based on developed guidance and fitness functions. AMOS has successfully identified the pareto frontier of practical engineering optimization problems with expensive black box functions and significantly reduced the computation cost. The novel method was put to the test utilizing real-world challenges and engineering design examples such vehicle magnetorheological design and wind turbine airfoil geometry.

关键词： multi-objective optimization Surrogate models Pareto frontier Intensive computation Black box Kriging Radial basis function Quadratic response function

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Cardinality-Constrained multi-objective optimization: Novel Optimality Conditions and Algorithms

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JOURNAL OF optimization THEORY AND APPLICATIONS 2024年第1期201卷 323-351页

作者： Lapucci, Matteo Mansueto, Pierluigi Univ Florence via Santa Marta 3 I-50139 Florence Italy

In this paper, we consider multi-objective optimization problems with a sparsity constraint on the vector of variables. For this class of problems, inspired by the homonymous necessary optimality condition for sparse single-objective optimization, we define the concept of L-stationarity and we analyze its relationships with other existing conditions and Pareto optimality concepts. We then propose two novel algorithmic approaches: the first one is an iterative hard thresholding method aiming to find a single L-stationary solution, while the second one is a two-stage algorithm designed to construct an approximation of the whole Pareto front. Both methods are characterized by theoretical properties of convergence to points satisfying necessary conditions for Pareto optimality. Moreover, we report numerical results establishing the practical effectiveness of the proposed methodologies.

关键词： multi-objective optimization Cardinality constraints Optimality conditions Numerical methods Pareto front

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multi-objective optimization and uncertainty quantification for inductors based on neural network

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2024年第4期43卷 890-903页

作者： Kong, Xiaohan Yin, Shuli Gong, Yunyi Igarashi, Hajime Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan

PurposeThe prolonged training time of the neural network (NN) has sparked considerable debate regarding their application in the field of optimization. The purpose of this paper is to explore the beneficial assistance of NN-based alternative models in inductance design, with a particular focus on multi-objective optimization and uncertainty analysis ***/methodology/approachUnder Gaussian-distributed manufacturing errors, this study predicts error intervals for Pareto points and select robust solutions with minimal error margins. Furthermore, this study establishes correlations between manufacturing errors and inductance value discrepancies, offering a practical means of determining permissible manufacturing errors tailored to varying accuracy *** NN-assisted methods are demonstrated to offer a substantial time advantage in multi-objective optimization compared to conventional approaches, particularly in scenarios where the trained NN is repeatedly used. Also, NN models allow for extensive data-driven uncertainty quantification, which is challenging for traditional ***/valueThree objectives including saturation current are considered in the multi-optimization, and the time advantages of the NN are thoroughly discussed by comparing scenarios involving single optimization, multiple optimizations, bi-objective optimization and tri-objective optimization. This study proposes direct error interval prediction on the Pareto front, using extensive data to predict the response of the Pareto front to random errors following a Gaussian distribution. This approach circumvents the compromises inherent in constrained robust optimization for inductance design and allows for a direct assessment of robustness that can be applied to account for manufacturing errors with complex distributions.

关键词： multi-objective optimization Surrogate optimization Uncertainties in electromagnetics

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A user-guided innovization-based evolutionary algorithm framework for practical multi-objective optimization problems

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ENGINEERING optimization 2023年第12期55卷 2084-2096页

作者： Ghosh, Abhiroop Deb, Kalyanmoy Goodman, Erik Averill, Ronald Michigan State Univ E Lansing MI 48824 USA

The knowledge of experienced users in solving real-world optimization problems can be formulated as inter-variable relationships to guide an optimization algorithm towards good solutions faster. Alternatively, such interactions can be learned algorithmically during the optimization by analysing good solutions-a process called innovization. Any common pattern extracted from good solutions can be used as a repair operator to modify candidate solutions. The key aspect is to strike a balance between the relevance of the pattern and the extent of its use in the repair operator. This article proposes a multi-objective evolutionary algorithm framework that combines problem-specific knowledge and online innovization approaches to solve two real-world large-scale multi-objective problems: a 879- and 1479-variable truss design and a 544-variable solid fuel rocket design. Four repair operators suitable for uncovering monotonic relations involving multiple decision variables are proposed. Performance variations resulting from different combinations of initial user knowledge and repair operators are also presented.

关键词： multi-objective optimization 'innovization' knowledge-based optimization

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Knowledge hierarchy-based dynamic multi-objective optimization method for AUV path planning in cooperative search missions

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OCEAN ENGINEERING 2024年第Part3期312卷

作者： Wang, Yinhuan Liu, Kaizhou Geng, Lingbo Zhang, Shaoze Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

This study focuses on path planning for Autonomous Underwater Vehicles (AUVs) in underwater cooperative search missions. The complexities of the ocean environment, the uncertainty of target movements, and limited communication capabilities render underwater cooperative search missions dynamic multi-objective optimization challenges. Studies focusing on communication-constrained search strategies still lack reliable metrics for assessing underwater communication quality and do not consider communication dead zones. Addressing these deficiencies, this paper introduces a novel communication optimization strategy utilizing packet error rate as a metric for assessing communication efficacy, complemented by a potential field-based method for navigating out of communication dead zones. To tackle the inherently dynamic nature of cooperative search missions for mobile underwater targets, we propose a dynamic multi-objective optimization algorithm that employs a knowledge hierarchy strategy. This method enhances the NSGA-II algorithm's efficiency by extracting effective gene segments from historical Pareto solution set and generating a new initial population through recombination, hierarchy, and prediction. Distinct from other advanced dynamic multi-objective optimization approaches that are limited to theoretical problems, our approach is directly applicable to practical scenarios. The effectiveness and practicality of the proposed method are validated through a series of simulation experiments considering the impact of underwater acoustic communication. These results demonstrate that this research is not only innovative in theory but also holds significant engineering value and practical prospects in real-world applications.

关键词： Autonomous underwater vehicle (AUV) Cooperative search Dynamic multi-objective optimization NSGA-II Restricted communication Underwater acoustic channel model

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Leveraging trust for joint multi-objective and multi-fidelity optimization

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第1期5卷 015056页

作者： Irshad, Faran Karsch, Stefan Doepp, Andreas Ludwig Maximilian Univ Munich Fac Phys Coulombwall 1 D-85748 Garching Germany

In the pursuit of efficient optimization of expensive-to-evaluate systems, this paper investigates a novel approach to Bayesian multi-objective and multi-fidelity (MOMF) optimization. Traditional optimization methods, while effective, often encounter prohibitively high costs in multi-dimensional optimizations of one or more objectives. multi-fidelity approaches offer potential remedies by utilizing multiple, less costly information sources, such as low-resolution approximations in numerical simulations. However, integrating these two strategies presents a significant challenge. We propose the innovative use of a trust metric to facilitate the joint optimization of multiple objectives and data sources. Our methodology introduces a modified multi-objective (MO) optimization policy incorporating the trust gain per evaluation cost as one of the objectives of a Pareto optimization problem. This modification enables simultaneous MOMF optimization, which proves effective in establishing the Pareto set and front at a fraction of the cost. Two specific methods of MOMF optimization are presented and compared: a holistic approach selecting both the input parameters and the fidelity parameter jointly, and a sequential approach for benchmarking. Through benchmarks on synthetic test functions, our novel approach is shown to yield significant cost reductions-up to an order of magnitude compared to pure MO optimization. Furthermore, we find that joint optimization of the trust and objective domains outperforms sequentially addressing them. We validate our findings with the specific use case of optimizing particle-in-cell simulations of laser-plasma acceleration, highlighting the practical potential of our method in the Pareto optimization of highly expensive black-box functions. Implementation of the methods in existing Bayesian optimization frameworks is straightforward, with immediate extensions e.g. to batch optimization possible. Given their ability to handle various continuous

关键词： Bayesian optimization multi-objective optimization computational physics

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Nature-Inspired approaches for Optimizing Food Drying Processes: A Critical Review

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FOOD ENGINEERING REVIEWS 2025年第2期17卷 270-290页

作者： Ashtiani, Seyed-Hassan Miraei Martynenko, Alex Dalhousie Univ Fac Agr Dept Engn Halifax NS B2N 5E3 Canada

Food drying is a critical process in food preservation, directly impacting the quality, energy consumption, and environmental sustainability of the final product. Traditional optimization techniques, while useful, often fall short in addressing the complex, nonlinear, and multi-objective nature of food drying. Nature-inspired algorithms, which mimic biological, chemical, and physical systems, have emerged as powerful tools for optimizing these processes, demonstrating superior performance in handling multiple parameters and conflicting objectives. This review critically examines the application of various nature-inspired optimization approaches in food drying, including artificial neural networks, genetic algorithms, particle swarm optimization, and non-dominated sorting genetic algorithm II. The review highlights the theoretical underpinnings of these algorithms, their specific applications in food drying, and the advantages and limitations of each method. Recent case studies are also discussed to illustrate the practical implementation of these techniques in improving product quality, energy efficiency, and environmental impact in food processing plants. The findings highlight the potential of integrating these advanced optimization algorithms into computer-integrated manufacturing systems, driving the food drying industry toward more sustainable and cost-effective practices. Additionally, the scalability of these methods for industrial applications is critically evaluated, identifying practical barriers and suggesting pathways for future research. This comprehensive review aims to serve as a valuable resource for researchers and practitioners interested in the latest developments in food drying optimization, providing insights that are both broad and deep, suitable for a multidisciplinary audience.

关键词： multi-objective optimization Metaheuristic Optimal operating conditions Energy Quality attributes Industrial barriers

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