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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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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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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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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 1-21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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Accelerating multi-objective optimization of Composite Structures Using multi-Fidelity Surrogate Models and Curriculum Learning

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MATERIALS 2025年第7期18卷 1469-1469页

作者： Miller, Bartosz Ziemianski, Leonard Rzeszow Univ Technol Fac Civil & Environm Engn & Architecture Al Powstancow Warszawy 12 PL-35959 Rzeszow Poland

The optimization of multilayer composite structures requires balancing mechanical performance, economic efficiency, and computational feasibility. This study introduces an innovative approach that integrates Curriculum Learning (CL) with a multi-fidelity surrogate model to enhance computational efficiency in engineering design. A multi-fidelity strategy is introduced to generate training data efficiently, leveraging a high-fidelity finite element model for accurate simulations and a low-fidelity model to provide a larger dataset at reduced computational cost. Unlike conventional surrogate modeling approaches, the proposed method applies CL to iteratively refine the surrogate model, enabling step-by-step learning of complex structural patterns and improving prediction accuracy. Genetic algorithms (GAs) are then applied to optimize structural parameters while minimizing computational expense. The integration of CL and multi-fidelity modeling allows for a reduction in computational burden while preserving accuracy, demonstrating practical applicability in real-world structural design problems. The effectiveness of this methodology is validated by evaluating Pareto front quality using selected performance indicators. Results demonstrate that the proposed approach reduces optimization burden while achieving accurate predictions, highlighting the benefits of integrating surrogate modeling, multi-fidelity analysis, CL, and GAs for efficient composite structure optimization. This work contributes to the advancement of optimization methodologies by providing a scalable framework applicable to complex engineering problems requiring high computational efficiency.

关键词： multi-objective optimization composite multi-fidelity models surrogate models deep neural networks genetic algorithms curriculum learning

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Handling objective preference and variable uncertainty in evolutionary multi-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Yadav, Deepanshu Ramu, Palaniappan Deb, Kalyanmoy Indian Inst Technol Madras Dept Engn Design Chennai 600036 India Michigan State Univ Dept Elect & Comp Engn E Lansing MI USA

Evolutionary algorithms (EAs) are widely employed in multi-objective optimization (MOO) to find a well- distributed set of near-Pareto solutions. Among various types of practicalities that demand standard evolutionary multi-objective optimization (EMO) algorithms to be modified suitably, we propose here a framework for handling two important ones: (i) decision-making to choose one or more preferred Pareto regions, rather than finding the entire Pareto set, and (i) uncertainty in variables and parameters of the problem which is inevitable in any practical problem. While the first practicality will allow a focused set of preferred solutions to be found, the second practicality will enable finding robust yet high-performing non-dominated solutions. We propose and analyze four different approaches for finding preferred and robust solutions for handling both practicalities simultaneously. Our results on a number of two to 10-objective tests and engineering problems indicate the superiority of one specific approach. Fora comprehensive evaluation of new EMO algorithms for finding a preferred and robust solution set, we also propose anew performance metric by identifying and utilizing a number of desired properties of such trade-off solutions. The study is comprehensive and should encourage researchers to develop more competitive EMO algorithms for finding preferred and robust Pareto solutions.

关键词： Evolutionary algorithms Pareto front Robust front multi-criteria decision-making Preference-based EMO

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EMO-TS: An Enhanced multi-objective optimization Algorithm for Energy-Efficient Task Scheduling in Cloud Data Centers

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IEEE ACCESS 2025年 13卷 8187-8200页

作者： Nambi, S. Thanapal, P. Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore 632014 India

The rapid expansion of cloud data centers, driven by the increasing demand for diverse user services, has escalated energy consumption and greenhouse gas emissions, posed severe environmental risks, and increased operational costs. Addressing these challenges requires innovative solutions for optimizing resource allocation without compromising service quality. This paper presents the Enhanced multi-objective optimization Algorithm for Task Scheduling (EMO-TS). This novel approach integrates Deep Reinforcement Learning (DRL) and Enhanced Electric Fish optimization (EEFO) to create an adaptive, dynamic, and energy-efficient scheduling framework. The primary objective of EMO-TS is to significantly reduce the energy consumption of cloud data centers while maintaining high levels of resource utilization, time efficiency, and service quality. Through the hybrid methodology of DRL and EEFO, EMO-TS dynamically adjusts task scheduling based on real-time workloads and operational conditions, effectively minimizing power consumption without sacrificing system performance. Additionally, EMO-TS introduces improvements in makespan and task execution, ensuring timely completion and optimal resource use. A comprehensive set of experiments and simulations demonstrates the practical implications of EMO-TS's results. EMO-TS outperforms traditional scheduling approaches, reducing energy consumption by up to 25% and decreasing makespan by 15%. These results underscore the algorithm's potential to address cloud service providers' economic and environmental concerns, offering a practical solution for green cloud computing initiatives. Furthermore, the integration of renewable energy sources within the EMO-TS framework shows potential for further reducing the carbon footprint of cloud operations, aligning with global sustainability goals.

关键词： Cloud computing Data centers Processor scheduling Resource management Dynamic scheduling Energy consumption Heuristic algorithms Energy efficiency Real-time systems Scalability Cloud data centers deep reinforcement learning electric fish optimization energy efficiency makespan task scheduling

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