Article
Mathematics
Shande Li, Jian Wen, Jun Wang, Weiqi Liu, Shuai Yuan
Summary: This paper proposes a high-precision surrogate modeling method based on the parallel multipoint expected improvement point infill criteria for solving large-scale complex simulation problems. The method combines global search ability with local search ability, improving the overall accuracy of the fitting function.
Article
Computer Science, Interdisciplinary Applications
Zhendong Guo, Qineng Wang, Liming Song, Jun Li
Summary: The study introduces a new infill criterion called Filter-GEI for addressing sample assignment issue in multi-fidelity optimization. By considering correlations between HF and LF models and adding an adaptive filter function on top of the GEI acquisition function, Filter-GEI efficiently allocates HF and LF samples to achieve a good balance between local and global search, with further improvement in efficiency through infilling multiple HF and LF samples in each iteration along with parallel computing. Tests on mathematical toy problems and an engineering problem demonstrate the effectiveness of the proposed algorithm.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Engineering, Civil
Guangyong Sun, Linsong Li, Jianguang Fang, Qing Li
Summary: In this study, a novel approach for developing multiobjective LCB criteria based on LCB improvement matrix is proposed, introducing different improvement functions to reduce computational cost and improve efficiency. The testing results demonstrate faster convergence and lower computational cost, showing potential for effective engineering design in complex situations.
THIN-WALLED STRUCTURES
(2021)
Article
Computer Science, Interdisciplinary Applications
Leshi Shu, Ping Jiang, Yan Wang
Summary: This work proposes a multi-fidelity Bayesian optimization approach that utilizes hierarchical Kriging to reduce optimization costs, quantifies the impact of high and low-fidelity samples based on expected further improvement, and introduces a novel acquisition function to determine the location and fidelity level of the next sample simultaneously. The proposed approach is compared with state-of-the-art methods for multi-fidelity global optimization and shows that it can achieve global optimal solutions with reduced computational costs.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Automation & Control Systems
Jiao Liu, Yong Wang, Guangyong Sun, Tong Pang
Summary: To solve expensive optimization problems (EOPs) more efficiently, a new infill criterion called evolutionary EI (EEI) is proposed, which incorporates the population distribution into expected improvement (EI) and focuses on promising regions. By using EEI as the infill criterion, a new algorithm called EEI-BO is designed for Bayesian optimization. An extended version called EEI-BO+ is also introduced to handle multitask EOPs. Experimental results demonstrate that EEI-BO and EEI-BO+ can obtain high-quality solutions with limited function evaluations, and perform well on solving lightweight and crashworthiness design problems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Dawei Zhan, Yun Meng, Huanlai Xing
Summary: This study proposes a novel fast multipoint expected improvement (EI) criterion that is easier to implement and compute than the classical multipoint EI criterion. The proposed criterion uses only univariate normal cumulative distributions, resulting in significantly reduced computational time compared to the classical approach. Additionally, cooperative coevolutionary algorithms (CCEAs) are introduced to solve the inner optimization problem of the proposed criterion, leading to improved performance compared to standard evolutionary algorithms.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Mechanical
Randall J. Kania, Shapour Azarm
Summary: Engineering design optimization problems often involve two competing objectives and uncertainty. This article proposes an approach using a Bayesian framework to solve multi-objective optimization problems under interval uncertainty. The method iteratively relaxes solutions to converge to a set of non-dominated, robust optimal solutions and uses a variation of the bi-objective expected improvement criterion to encourage variety and density of solutions. Several examples are tested and compared, showing that the proposed method performs well at finding robustly optimized feasible solutions with limited function evaluations.
JOURNAL OF MECHANICAL DESIGN
(2023)
Article
Green & Sustainable Science & Technology
Xiaodong Song, Mingyang Li, Zhitao Li, Fang Liu
Summary: This paper introduces a Kriging-based global optimization method using a multi-point infill sampling criterion to improve public traffic performance. Testing on typical functions and the 445 bus line in Beijing demonstrates that this method outperforms traditional methods in efficiency and effectiveness.
Article
Agronomy
Yaohui Li, Junjun Shi, Hui Cen, Jingfang Shen, Yanpu Chao
Summary: The proposed method improves the GEI criterion into dual objectives and utilizes multi-objective PSO method to optimize these objectives, producing the Pareto frontier for updating the Kriging model. Test results show that this method outperforms other classical optimization methods in terms of convergence and accuracy.
AGRICULTURAL WATER MANAGEMENT
(2021)
Article
Mathematics
Junjun Shi, Jingfang Shen, Yaohui Li
Summary: The high-precision Kriging modeling method based on hybrid sampling criteria (HKM-HS) successfully finds new valuable sampling points by optimizing infilling sampling strategies, enhancing the modeling accuracy and stability effectively.
Article
Engineering, Mechanical
Anh Tran, Michael Eldred, Scott McCann, Yan Wang
Summary: The study introduces a novel multi-objective optimization framework, which combines three different Gaussian processes to solve multi-objective optimization problems, and employs a multi-objective augmented Tchebycheff function to convert multi-objective to single-objective at each iteration.
JOURNAL OF MECHANICAL DESIGN
(2022)
Article
Engineering, Multidisciplinary
Zecong Liu, Hanyan Huang, Xiaoyu Xu, Mei Xiong, Qizhe Li
Summary: The efficient global optimization (EGO) algorithm is a Bayesian optimization algorithm that uses Kriging interpolation model and expectation improvement (EI) criteria as surrogate model and acquisition function. The revised efficient global optimization (REGO) algorithm is proposed to overcome the local optima problem by introducing a balance factor in the revised expectation improvement (REI) criteria. The Latin hypercube based indicator is introduced to balance exploration and computational cost.
ENGINEERING OPTIMIZATION
(2023)
Article
Computer Science, Artificial Intelligence
Mei Han, Linhan Ouyang
Summary: This study proposes a novel framework for Bayesian optimization of multiple stochastic responses, which improves the performance of multi-objective stochastic simulation optimization by constructing stochastic Kriging metamodels and integrating acquisition functions.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Haizhou Yang, Seong Hyeong Hong, Yi Wang
Summary: This paper presents a novel computation-aware multi-fidelity surrogate-based optimization methodology and a new sequential and adaptive sampling strategy based on expected improvement reduction. It improves the exploration and convergence rate of the optimization process under a fixed computational budget.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Engineering, Multidisciplinary
Zhaoyi Xu, Yanjie Guo, Joseph H. Saleh
Summary: This article proposes a hybrid Bayesian BFGS algorithm (HB2O) to address the efficiency problem, and develops an adaptive expected improvement (AEI) acquisition function to realize a self-adaptive sampling strategy. The computational experiments demonstrate that the HB2O can efficiently converge on functions' optima with limited simulation samples and outperform other optimizers for various test functions.
ENGINEERING OPTIMIZATION
(2021)
Article
Management
Hamed Jalali, Raisa Carmen, Inneke Van Nieuwenhuyse, Robert Boute
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2019)
Article
Economics
Wen Shi, Kaijun Leng, Inneke Van Nieuwenhuyse, Yucui Liu, Xiaohong Chen
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2020)
Article
Management
Sebastian Rojas Gonzalez, Hamed Jalali, Inneke Van Nieuwenhuyse
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2020)
Article
Nursing
Chih-Hsuan Huang, Hsin-Hung Wu, Yii-Ching Lee, Inneke Van Nieuwenhuyse, Meng-Chen Lin, Cheng-Feng Wu
JOURNAL OF PEDIATRIC NURSING-NURSING CARE OF CHILDREN & FAMILIES
(2020)
Article
Computer Science, Interdisciplinary Applications
Nasrulloh Loka, Ivo Couckuyt, Federico Garbuglia, Domenico Spina, Inneke Van Nieuwenhuyse, Tom Dhaene
Summary: Multi-objective optimization of complex engineering systems is a challenging problem. Bayesian optimization is a popular technique to tackle this problem. We develop an approach that can handle a mix of expensive and cheap objective functions, offering lower complexity and superior performance in cases where the cheap objective function is difficult to approximate.
ENGINEERING WITH COMPUTERS
(2023)
Article
Engineering, Industrial
Hamed Jalali, Maud Van den Broeke, Inneke Van Nieuwenhuyse
Summary: The interaction between product platform and product portfolio decisions is crucial for a company's competitive advantage, but not well understood. Operational parameters and marketing parameters jointly impact the optimal product portfolio and platform design, and marketing parameters do not always affect the optimal product development strategy.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2022)
Article
Computer Science, Artificial Intelligence
Alejandro Morales-Hernandez, Inneke Van Nieuwenhuyse, Sebastian Rojas Gonzalez
Summary: Hyperparameter optimization (HPO) is a necessary step to ensure the best performance of machine learning algorithms. This article provides a systematic survey of the literature on multi-objective HPO algorithms published from 2014 to 2020, categorizing them into metaheuristic-based algorithms, metamodel-based algorithms, and hybrid approaches. It also discusses quality metrics and future research directions in multi-objective HPO.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Retraction
Computer Science, Information Systems
Kaijun Leng, Linbo Jin, Wen Shi, Inneke Van Nieuwenhuyse
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Alejandro Morales-Hernandez, Inneke Van Nieuwenhuyse, Gonzalo Napoles
Summary: The performance of Machine Learning algorithms is influenced by the choice of hyperparameters. However, finding the optimal hyperparameters is challenging due to the expensive training and evaluation process. This paper proposes a method that combines Tree-structured Parzen Estimators (TPE) sampling strategy with Gaussian Process Regression (GPR) to optimize hyperparameters with uncertainty, leading to improved results compared to existing methods.
OPTIMIZATION AND LEARNING, OLA 2022
(2022)
Article
Business
Peng Xia, Zhixue Liu, Weijiao Wang, Wen Shi, Inneke Van Nieuwenhuyse
Summary: This study examines the factors driving vehicle recalls in the Chinese automobile industry and finds that firm innovation and negative electronic word-of-mouth play a significant role. The ownership structure of the firms also has an impact on the recall volume.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2022)
Proceedings Paper
Computer Science, Theory & Methods
Sebastian Rojas-Gonzalez, Juergen Branke, Inneke Van Nieuwehuyse
2019 WINTER SIMULATION CONFERENCE (WSC)
(2019)
Article
Computer Science, Interdisciplinary Applications
Rafael Praxedes, Teobaldo Bulhoes, Anand Subramanian, Eduardo Uchoa
Summary: The Vehicle Routing Problem with Simultaneous Pickup and Delivery is a classical optimization problem that aims to determine the least-cost routes while meeting pickup and delivery demands and vehicle capacity constraints. In this study, a unified algorithm is proposed to solve multiple variants of the problem, and extensive computational experiments are conducted to evaluate the algorithm's performance.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ragheb Rahmaniani, Teodor Gabriel Crainic, Michel Gendreau, Walter Rei
Summary: Benders decomposition (BD) is a popular solution algorithm for stochastic integer programs. However, existing parallelization methods often suffer from inefficiencies. This paper proposes an asynchronous parallel BD method and demonstrates its effectiveness through numerical studies and performance enhancement strategies.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Giulia Caselli, Maxence Delorme, Manuel Iori, Carlo Alberto Magni
Summary: This study addresses a real-world scheduling problem and proposes four exact methods to solve it. The methods are evaluated through computational experiments on different types of instances and show competitive advantages on specific subsets. The study also demonstrates the generalizability of the algorithms to related scheduling problems with contiguity constraints.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaowen Yao, Chao Tang, Hao Zhang, Songhuan Wu, Lijun Wei, Qiang Liu
Summary: This paper examines the problem of two-dimensional irregular multiple-size bin packing and proposes a solution that utilizes an iteratively doubling binary search algorithm to find the optimal bin combination, and further optimizes the result through an overlap minimization approach.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Decheng Wang, Ruiyou Zhang, Bin Qiu, Wenpeng Chen, Xiaolan Xie
Summary: Consideration of driver-related constraints, such as mandatory work break, in vehicle scheduling and routing is crucial for safety driving and protecting the interests of drivers. This paper addresses the drop-and-pull container drayage problem with flexible assignment of work break, proposing a mixed-integer programming model and an algorithm for solving realistic-sized instances. Experimental results show the effectiveness of the proposed algorithm in handling vehicle scheduling and routing with work break assignment.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
William N. Caballero, Jose Manuel Camacho, Tahir Ekin, Roi Naveiro
Summary: This research provides a novel probabilistic perspective on the manipulation of hidden Markov model inferences through corrupted data, highlighting the weaknesses of such models under adversarial activity and emphasizing the need for robustification techniques to ensure their security.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Davood Zaman Farsa, Shahryar Rahnamayan, Azam Asilian Bidgoli, H. R. Tizhoosh
Summary: This paper proposes a multi-objective evolutionary framework for compressing feature vectors using deep autoencoders. The framework achieves high classification accuracy and efficient image representation through a bi-level optimization scheme. Experimental results demonstrate the effectiveness and efficiency of the proposed framework in image processing tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Matthew E. Scherer, Raymond R. Hill, Brian J. Lunday, Bruce A. Cox, Edward D. White
Summary: This paper discusses instance generation methods for the multidemand multidimensional knapsack problem and introduces a primal problem instance generator (PPIG) to address feasibility issues in current instance generation methods.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Yin Yuan, Shukai Li, Lixing Yang, Ziyou Gao
Summary: This paper investigates the design of real-time train regulation strategies for urban rail networks to reduce train deviations and passenger waiting times. A mixed-integer nonlinear programming (MINLP) model is used and an efficient iterative optimization (IO) approach is proposed to address the complexity. The generalized Benders decomposition (GBD) technique is also incorporated. Numerical experiments show the effectiveness and computational efficiency of the proposed method.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xinghai Guo, Netirith Narthsirinth, Weidan Zhang, Yuzhen Hu
Summary: This study proposes a bi-level scheduling method that utilizes unmanned surface vehicles for container transportation. By formulating mission decision and path control models, efficient container transshipment and path planning are achieved. Experimental results demonstrate the effectiveness of the proposed approach in guiding unmanned surface vehicles to complete container transshipment tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Review
Computer Science, Interdisciplinary Applications
Jose-Fernando Camacho-Vallejo, Carlos Corpus, Juan G. Villegas
Summary: This study aims to review the published papers on implementing metaheuristics for solving bilevel problems and performs a bibliometric analysis to track the evolution of this topic. The study provides a detailed description of the components of the proposed metaheuristics and analyzes the common combinations of these components. Additionally, the study provides a detailed classification of how crucial bilevel aspects of the problem are handled in the metaheuristics, along with a discussion of interesting findings.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xudong Diao, Meng Qiu, Gangyan Xu
Summary: In this study, an optimization model for the design of an electric vehicle-based express service network is proposed, considering limited recharging resources and power management. The proposed method is validated through computational experiments on realistic instances.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ramon Piedra-de-la-Cuadra, Francisco A. Ortega
Summary: This study proposes a procedure to select candidate sites optimally for ensuring energy autonomy and reinforced service coverage for electric vehicles, while considering demand and budget restrictions.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Danny Blom, Christopher Hojny, Bart Smeulders
Summary: This paper focuses on a robust variant of the kidney exchange program problem with recourse, and proposes a cutting plane method for solving the attacker-defender subproblem. The results show a significant improvement in running time compared to the state-of-the-art, and the method can solve previously unsolved instances. Additionally, a new practical policy for recourse is proposed and its tractability for small to mid-size kidney exchange programs is demonstrated.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Anqi Li, Congying Han, Tiande Guo, Bonan Li
Summary: This study proposes a general framework for designing linear programming instances based on the preset optimal solution, and validates the effectiveness of the framework through experiments.
COMPUTERS & OPERATIONS RESEARCH
(2024)
