Article
Automation & Control Systems
Genghui Li, Zhenkun Wang, Maoguo Gong
Summary: Researchers propose a new algorithm, called SAMFEO, which combines surrogate-assisted and model-free evolutionary optimization to tackle complex and high-dimensional problems. Experimental results demonstrate that SAMFEO outperforms several state-of-the-art methods on complex benchmark problems and a real-world problem.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Information Systems
Yuxing Wang, Tiantian Zhang, Yongzhe Chang, Xueqian Wang, Bin Liang, Bo Yuan
Summary: This paper proposes a generic module called Surrogate-assisted Controller (SC) that can be applied on existing hybrid learning frameworks to alleviate the computational burden of expensive fitness evaluation. The functionality and effectiveness of SC are highlighted through empirical studies.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Yuanchao Liu, Jianchang Liu, Shubin Tan
Summary: This paper proposes a surrogate-assisted evolutionary algorithm based on decision space partition (DSP-SAEA) for dealing with expensive optimization. The algorithm introduces a two-stage search strategy that integrates global search and local search. In the global search stage, the decision space is partitioned into regions based on clustered evaluated points, where a surrogate model is constructed for each region. The algorithm simultaneously searches these regions with the help of the built surrogate models, resulting in obtaining promising points distributed in different regions. In the local search stage, a model adaptive selection strategy and a trust region local search are integrated. Experimental results demonstrate that DSP-SAEA performs competitively compared with state-of-the-art algorithms on benchmark problems and parameter estimation for frequency-modulated sound waves problem.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Yufeng Zhou, Xuexian Li, Shunling Ruan
Summary: Model management is crucial for surrogate-assisted evolutionary algorithms to tackle computationally expensive optimization problems. The proposed algorithm in this paper, based on radial space division, outperforms commonly used surrogate-assisted evolutionary algorithms in benchmark and automobile structure design problems.
APPLIED SOFT COMPUTING
(2021)
Review
Computer Science, Artificial Intelligence
Chunlin He, Yong Zhang, Dunwei Gong, Xinfang Ji
Summary: This paper provides a systematic overview of surrogate-assisted evolutionary algorithms (SAEAs), including the necessity of studying SAEAs, commonly used surrogate models, classification and discussion of existing SAEAs, review of their applications in various fields, and suggestions for future research directions.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Qiuzhen Lin, Xunfeng Wu, Lijia Ma, Jianqiang Li, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes an ensemble surrogate-based framework for solving computationally expensive multiobjective optimization problems (EMOPs). The framework trains a global surrogate model and multiple surrogate submodels to enhance prediction accuracy and reliability. Experimental results demonstrate the advantages of this approach in solving EMOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Yong Pang, Xiaonan Lai, Yitang Wang, Xiwang He, Shuai Zhang, Xueguan Song
Summary: In this work, a new algorithm based on the Kriging surrogate model and a novel constraint handling method is proposed for expensive multiobjective optimization problems. The algorithm achieves superior results in highly heterogeneous optimization problems and bi-objective constrained scenarios, by reducing time complexity and increasing accuracy. The effectiveness of the proposed method is verified through benchmark comparison problems and practical applications.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Qian Wang, Neal N. Xiong, Song Jiang, Lu Chen
Summary: A surrogate-assisted evolutionary algorithm is proposed in this paper for solving expensive constrained multi-objective discrete optimization problems. By embedding random forest models and an improved stochastic ranking strategy, the algorithm makes significant progress in optimization efficiency and candidate solution quality.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yi Zhao, Jianchao Zeng, Ying Tan
Summary: The proposed method combines reference vector guided evolutionary algorithm and radial basis function networks to optimize individuals and introduces an infill strategy, showing competitive performance in solving computationally expensive many-objective optimization problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Yuan Yuan, Wolfgang Banzhaf
Summary: We propose a new surrogate-assisted evolutionary algorithm for expensive multiobjective optimization. The algorithm uses two classification-based surrogate models, addresses dominance prediction problem using deep learning techniques, and integrates the surrogate models with multiobjective evolutionary optimization using a two-stage preselection strategy. Experimental results show the superiority of the proposed algorithm compared with several representative surrogate-assisted algorithms.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Dong Han, Wenli Du, Xinjie Wang, Wei Du
Summary: The study introduces a surrogate-assisted decomposition-based evolutionary algorithm that considers the balance between exploration and exploitation by incorporating distribution information of weight vectors and population, as well as develops a replacement strategy to limit model training time. Empirical results demonstrate competitive performance of the algorithm, with superior outcomes when applied to real-world optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Xiaodong Ren, Daofu Guo, Zhigang Ren, Yongsheng Liang, An Chen
Summary: Surrogate-assisted evolutionary algorithms, particularly hierarchical ones, have been effective in solving computationally expensive optimization problems by reducing real fitness evaluations. This study introduces a new hierarchical SAEA that uses random projection technique to train local surrogate models, significantly improving their accuracy and showing clear advantages over state-of-the-art SAEAs in experiments on benchmark functions of 100 and 200 dimensions.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Information Systems
Wenhao Du, Zhigang Ren, Jihong Wang, An Chen
Summary: This study proposes a surrogate-assisted multimodal evolutionary algorithm with knowledge transfer to address the problem of expensive multimodal optimization. By using global surrogate-assisted sampling and joint surrogate-assisted local search, this method can efficiently explore and exploit modalities, leading to competitive performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Artificial Intelligence
Kuihua Huang, Huixiang Zhen, Wenyin Gong, Rui Wang, Weiwei Bian
Summary: To solve high-dimensional expensive optimization problems, a surrogate-assisted evolutionary algorithm called ESPSO is proposed. ESPSO utilizes evolutionary sampling-assisted strategies to improve population initialization, approximate the objective function landscape with a local radial basis function model, and accelerate the search process with surrogate-assisted local search and surrogate-assisted trust region search. Experimental comparisons with five state-of-the-art surrogate-assisted evolutionary algorithms demonstrate that ESPSO outperforms the others in terms of search efficiency.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Qiqi Liu, Yaochu Jin, Martin Heiderich, Tobias Rodemann
Summary: A new surrogate-assisted evolutionary algorithm is proposed in this study to handle expensive irregular multi-objective optimization problems. The algorithm balances convergence and diversity by adapting reference vectors and implementing a surrogate management strategy, effectively taking irregularity of the Pareto front into account.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hao Hao, Jinyuan Zhang, Xiaofen Lu, Aimin Zhou
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2020)