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A modified trust-region assisted variable-fidelity optimization framework for computationally expensive problems

ENGINEERING COMPUTATIONS 2022年第7期39卷 2733-2754页

作者： Li, Chengshan Dong, Huachao Changan Univ Sch Construct Machinery Xian Peoples R China Changan Univ Autopilot & Maglev Transportat Technol & Equipmen Xian Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian Peoples R China

Purpose Variable-fidelity optimization (VFO) frameworks generally aim at taking full advantage of high-fidelity (HF) and low-fidelity (LF) models to solve computationally expensive problems. The purpose of this paper is to develop a novel modified trust-region assisted variable-fidelity optimization (MTR-VFO) framework that can improve the optimization efficiency for computationally expensive engineering design problems. Design/methodology/approach Though the LF model is rough and inaccurate, it probably contains the gradient information and trend of the computationally expensive HF model. In the proposed framework, the extreme locations of the LF kriging model are firstly utilized to enhance the HF kriging model, and then a modified trust-region (MTR) method is presented for efficient local search. The proposed MTR-VFO framework is verified through comparison with three typical methods on some benchmark problems, and it is also applied to optimize the configuration of underwater tandem wings. Findings The results indicate that the proposed MTR-VFO framework is more effective than some existing typical methods and it has the potential of solving computationally expensive problems more efficiently. Originality/value The extreme locations of LF models are utilized to improve the accuracy of HF models and a MTR method is first proposed for local search without utilizing HF gradient. Besides, a novel MTR-VFO framework is presented which is verified to be more effective than some existing typical methods and shows great potential of solving computationally expensive problems effectively.

关键词： Variable-fidelity optimization Kriging Trust-region method computationally expensive problems Design optimization

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A twofold infill criterion-driven heterogeneous ensemble surrogate-assisted evolutionary algorithm for computationally expensive problems

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KNOWLEDGE-BASED SYSTEMS 2022年第0期236卷 107747-107747页

作者： Yu, Mingyuan Liang, Jing Wu, Zhou Yang, Zhile Chongqing Univ Sch Automat Chongqing 40000 Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China

Over the past decade, surrogate-assisted evolutionary algorithms (SAEAs) have received considerable attention in solving expensive optimization problems from both academia and industry. In SAEAs, model management plays a key role in the all-encompassing use of surrogates and data. Constructing sophisticated management strategies and high-fidelity surrogates, nevertheless, remains an urgent and foundational task. In this context, we propose a twofold infill criterion-driven heterogeneous ensemble surrogate-assisted neighborhood field optimization algorithm (HESNFO). The proposed algorithm takes into account both the diversity and accuracy of surrogates to speed up the optimization process. A twofold infill criterion is presented to strike a balance between exploration and exploitation on the basis of updating the surrogates online. Meanwhile, to enhance the diversity of surrogates, a heterogeneous ensemble surrogate consisting of multiple radial basis function models with different architectures and different inputs has been built. Finally, our experimental results on several benchmark problems as well as an electromagnetic acoustic transducers optimization instance demonstrate that the proposed algorithm is superior. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Heterogeneous ensemble surrogate Infill criterion computationally expensive problems Radial basis function Surrogate-assisted evolutionary algorithms

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A surrogate-assisted hybrid swarm optimization algorithm for high-dimensional computationally expensive problems

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 72卷

作者： Li, Fan Li, Yingli Cai, Xiwen Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

In this paper, a surrogate-assisted hybrid swarm optimization algorithm is proposed to solve high-dimensional computationally expensive problems. Two swarms are, respectively, used in different optimization states. The first swarm uses the teaching-learning-based optimization in the early stage to enhance the exploration. The second swarm uses the particle swarm optimization in the later stage to accelerate convergence. Two different pre-screening criteria based on the corresponding evolutionary rules are proposed to select promising individuals for exact function evaluations. Several commonly used benchmark functions with their dimensions varying from 30 to 200 and an engineering optimization problem are used to validate the efficiency of the proposed algorithm. In addition, a comprehensive analysis is conducted to demonstrate the effectiveness of each main component of the proposed algorithm. The experimental results demonstrate the superiority of the proposed algorithm over other compared algorithms.

关键词： computationally expensive problems Hybrid swarm Particle swarm optimization Teaching-learning-based optimization Surrogate

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A Surrogate-Assisted Multiswarm Optimization Algorithm for High-Dimensional computationally expensive problems

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第3期51卷 1390-1402页

作者： Li, Fan Cai, Xiwen Gao, Liang Shen, Weiming Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

This article presents a surrogate-assisted multiswarm optimization (SAMSO) algorithm for high-dimensional computationally expensive problems. The proposed algorithm includes two swarms: the first one uses the learner phase of teaching-learning-based optimization (TLBO) to enhance exploration and the second one uses the particle swarm optimization (PSO) for faster convergence. These two swarms can learn from each other. A dynamic swarm size adjustment scheme is proposed to control the evolutionary progress. Two coordinate systems are used to generate promising positions for the PSO in order to further enhance its search efficiency on different function landscapes. Moreover, a novel prescreening criterion is proposed to select promising individuals for exact function evaluations. Several commonly used benchmark functions with their dimensions varying from 30 to 200 are adopted to evaluate the proposed algorithm. The experimental results demonstrate the superiority of the proposed algorithm over three state-of-the-art algorithms.

关键词： Optimization Iron Particle swarm optimization Sociology Statistics Education Search problems computationally expensive problems multiswarm optimization particle swarm optimization (PSO) surrogate model teaching-learning-based optimization (TLBO)

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Multi-output Gaussian process prediction for computationally expensive problems with multiple levels of fidelity

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KNOWLEDGE-BASED SYSTEMS 2021年 227卷 107151-107151页

作者： Lin, Quan Hu, Jiexiang Zhou, Qi Cheng, Yuansheng Hu, Zhen Couckuyt, Ivo Dhaene, Tom Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Naval Architecture & Ocean Engn Wuhan 430074 Peoples R China Univ Michigan Dept Ind & Mfg Syst Dearborn MI 48128 USA Univ Ghent Dept Informat Technol Ghent Belgium

The multi-output Gaussian process (MOGP) modeling approach is a promising way to deal with multiple correlated outputs since it can capture useful information across outputs to provide more accurate predictions than simply modeling these outputs separately. While the existing MOGP models are considered as single-fidelity models and need a large number of high-fidelity samples to construct a relatively accurate surrogate model. The computational cost is often prohibitive, especially for computationally expensive problems. In this paper, a multi-fidelity multi-output Gaussian process (MMGP) model is proposed to deal with multi-fidelity (MF) data with multiple correlated outputs. It aims to approximate multiple correlated high-fidelity (HF) functions enhanced by the low-fidelity (LF) data. The MMGP model can make a trade-off between high prediction accuracy and low computational cost to relieve the computational burden. The performance of the MMGP model is demonstrated with two analytical examples with different data structures and an aerodynamic prediction example for a NACA 0012 airfoil. Another four well-known surrogate modeling methods are also tested to compare with the proposed approach. Results show that the proposed model is capable of dealing with diverse data structures, heterotopic data, or HF samples generated in different regions. The proposed model provides more accurate predictions than the other approaches for most test cases, especially when using the heterotopic training data. In the NACA0012 airfoil case, the root-mean-square error (RMSE) for the drag coefficient can be reduced by 30%-40% using the MMGP model compared with that of the other MF modeling methods. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Surrogate model Multi-fidelity Multi-output Gaussian process computationally expensive problems

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A fast surrogate-assisted particle swarm optimization algorithm for computationally expensive problems

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APPLIED SOFT COMPUTING 2020年 92卷 106303-106303页

作者： Li, Fan Shen, Weiming Cai, Xiwen Gao, Liang Wang, G. Gary Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Simon Fraser Univ Sch Mechatron Syst Engn Burnaby BC Canada

Although many surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve computationally expensive problems, they usually need to consume plenty of expensive evaluations to obtain an acceptable solution. In this paper, we proposed a fast surrogate-assisted particle swarm optimization (FSAPSO) algorithm to solve medium scaled computationally expensive problems through a small number of function evaluations (FEs). Two criteria are applied in tandem to select candidates for exact evaluations. The performance-based criterion is used to exploit the current global best and accelerate the convergence rate, while the uncertainty-based criterion is used to enhance the exploration of the algorithm. The distance-based uncertainty criterion in SAEAs does not consider the fitness landscape of different problems. Therefore, we developed a criterion to estimate uncertainty by considering the distance and fitness value information simultaneously. This criterion can make up for the disadvantage of the conventional distance-based uncertainty criterion by considering the fitness landscape of a problem. In addition, it can be applied in any surrogate-assisted evolutionary algorithm irrespective of the used surrogate model. Twenty-three benchmark functions widely adopted in the literature and a 10-dimension propeller design problem are used to test the proposed approach. Experimental results demonstrate the superiority of the proposed FSAPSO algorithm over seven state-of-the-art algorithms. (C) 2020 Elsevier B.V. All rights reserved.

关键词： computationally expensive problems Particle swarm optimization (PSO) Surrogate model Uncertainty

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Two-layer adaptive surrogate-assisted evolutionary algorithm for high-dimensional computationally expensive problems

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JOURNAL OF GLOBAL OPTIMIZATION 2019年第2期74卷 327-359页

作者： Yang, Zan Qiu, Haobo Gao, Liang Jiang, Chen Zhang, Jinhao Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China

Surrogate-assisted evolutionary algorithms (SAEAs) have recently shown excellent ability in solving computationally expensive optimization problems. However, with the increase of dimensions of research problems, the effectiveness of SAEAs for high-dimensional problems still needs to be improved further. In this paper, a two-layer adaptive surrogate-assisted evolutionary algorithm is proposed, in which three different search strategies are adaptively executed during the iteration according to the feedback information which is proposed to measure the status of the algorithm approaching the optimal value. In the proposed method, the global GP model is used to pre-screen the offspring produced by the DE/current-to-best/1 strategy for fast convergence speed, and the DE/current-to-randbest/1 strategy is proposed to guide the global GP model to locate promising regions when the feedback information reaches a presetting threshold. Moreover, a local search strategy (DE/best/1) is used to guide the local GP model which is built by using individuals closest to the current best individual to intensively exploit the promising regions. Furthermore, a dimension reduction technique is used to construct a reasonably accurate GP model for high-dimensional expensive problems. Empirical studies on benchmark problems with 50 and 100 variables demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems under a limited computational budget.

关键词： Surrogate-assisted evolutionary algorithms computationally expensive problems Differential evolution Dimension reduction technique

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Semi-supervised Learning Assisted Particle Swarm Optimization of computationally expensive problems 18

Semi-supervised Learning Assisted Particle Swarm Optimizatio...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Sun, Chaoli Jin, Yaochu Tan, Ying Taiyuan Univ Sci & Technol Dept Comp Sci & Technol Taiyuan Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China Univ Surrey Dept Comp Sci Guildford Surrey England

ISBN: (纸本)9781450356183

In many real-world optimization problems, it is very time-consuming to evaluate the performance of candidate solutions because the evaluations involve computationally intensive numerical simulations or costly physical experiments. Therefore, standard population based meta-heuristic search algorithms are not best suited for solving such expensive problems because they typically require a large number of performance evaluations. To address this issue, many surrogate-assisted meta-heuristic algorithms have been proposed and shown to be promising in achieving acceptable solutions with a small computation budget. While most research focuses on reducing the required number of expensive fitness evaluations, not much attention has been paid to take advantage of the large amount of unlabelled data, i.e., the solutions that have not been evaluated using the expensive fitness functions, generated during the optimization. This paper aims to make use of semi-supervised learning techniques that are able to enhance the training of surrogate models using the unlabelled data together with the labelled data in a surrogate-assisted particle swarm optimization algorithm. Empirical studies on five 30-dimensional benchmark problems show that the proposed algorithm is able to find high-quality solutions for computationally expensive problems on a limited computational budget.

关键词： Particle swarm optimization semi-supervised learning computationally expensive problems

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A multi-fidelity RBF surrogate-based optimization framework for computationally expensive multi-modal problems with application to capacity planning of manufacturing systems

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2020年第4期62卷 1787-1807页

作者： Yi, Jin Shen, Yichi Shoemaker, Christine A. Natl Univ Singapore Environm Res Inst Singapore Singapore Natl Univ Singapore Inst Operat Res & Analyt Singapore Singapore Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore

This paper presents a multi-fidelity RBF (radial basis function) surrogate-based optimization framework (MRSO) for computationally expensive multi-modal optimization problems when multi-fidelity (high-fidelity (HF) and low-fidelity (LF)) models are available. The HF model is expensive and accurate while the LF model is cheaper to compute but less accurate. To exploit the correlation between the LF and HF models and improve algorithm efficiency, in MRSO, we first apply the DYCORS (dynamic coordinate search algorithm using response surface) algorithm to search on the LF model and then employ a potential area detection procedure to identify the promising points from the LF model. The promising points serve as the initial start points when we further search for the optimal solution based on the HF model. The performance of MRSO is compared with 6 other surrogate-based optimization methods (4 are using a single-fidelity surrogate and the rest 2 are using multi-fidelity surrogates). The comparisons are conducted on a multi-fidelity optimization test suite containing 10 problems with 10 and 30 dimensions. Besides the benchmark functions, we also apply the proposed algorithm to a practical and computationally expensive capacity planning problem in manufacturing systems which involves discrete event simulations. The experimental results demonstrate that MRSO outperforms all the compared methods.

关键词： Surrogate-assisted optimization Multi-fidelity surrogate Radial basis function computationally expensive problems

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A surrogate-assisted a priori multiobjective evolutionary algorithm for constrained multiobjective optimization problems

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JOURNAL OF GLOBAL OPTIMIZATION 2024年第2期90卷 459-485页

作者： Pour, Pouya Aghaei Hakanen, Jussi Miettinen, Kaisa Univ Jyvaskyla Fac Informat Technol POB 35 Agora 40014 Finland

We consider multiobjective optimization problems with at least one computationally expensive constraint function and propose a novel surrogate-assisted evolutionary algorithm that can incorporate preference information given a priori. We employ Kriging models to approximate expensive objective and constraint functions, enabling us to introduce a new selection strategy that emphasizes the generation of feasible solutions throughout the optimization process. In our innovative model management, we perform expensive function evaluations to identify feasible solutions that best reflect the decision maker's preferences provided before the process. To assess the performance of our proposed algorithm, we utilize two distinct parameterless performance indicators and compare them against existing algorithms from the literature using various real-world engineering and benchmark problems. Furthermore, we assemble new algorithms to analyze the effects of the selection strategy and the model management on the performance of the proposed algorithm. The results show that in most cases, our algorithm has a better performance than the assembled algorithms, especially when there is a restricted budget for expensive function evaluations.

关键词： Multiple objectives Model management A priori algorithms Constraint handling Surrogate-assisted optimization Constrained problems computationally expensive problems

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